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AI Agents in Line at HR

AI Agents in Line at HR

AI Agents in Line at HR may only be a satirical cartoon for a very short time. Sorry, Farside, but your AI bits may not be able to keep up with AI. July, 2034 — A new software unicorn has just emerged inbehind a bar in a pub in East London. Unicorn, by the way, descibes a startup company valued at over $1 billion, not necessarily with a billion dollar concept. Back to East London behind the soggy bar. Hey, its our fantasy. Besides if Amazon can start in a garage, isn’t anything possible? The CEO logs in as usual and gathers daily updates from the team. The Chief Technology Officer is suggesting a new feature to deploy. The Chief Product Officer wants to redesign the CRM (or whatever CRM has evolved to) integration. The Chief Revenue Officer is showing off the new pipeline, forecast by Accountant in a Box. The Chief Customer Officer is discussing the latest customer levitation tools and product feedback. The Chief Information Security Officer has found a new privacy conflict, which they are addressing with a newly-revised infrastructure set-up. And the Head of HR is fretting about the latest round of IT candidates. This sounds like every software business you’ve ever heard of. But the difference is that the CEO’s teammates are entirely AI, not human: The CTO is Lovable. The CPO is Cogna. The CCO is Gradient Labs. The CRO is 11x. The CISO is Zylon. Back to 2024: The Rise of AI Agents In 2024, the hottest topic in software is AI agents, or Agentic AI. Founders are rapidly standing up agentic applications that can solve specific needs in functions like sales and customer services — without a human required. Software buyers, seeing real opportunities to quickly improve their P&L, are swiftly building or purchasing these agentic products. Investors have poured hundreds of millions of dollars into startups in this space in recent months. Even Salesforce wasn’t launched with a silver AI spoon in its mouth. Salesforce began investing in artificial intelligence (AI) in 2014, when the company started acquiring machine learning startups and announced its Customer Success Platform. In 2016, Salesforce launched Einstein, its AI platform that supports several of its cloud services. Einstein is built into Salesforce products and includes features like natural language processing, machine learning, and predictive analytics. It helps organizations automate processes, make decisions based on insights, and improve the customer experience. YouTube How To Increase Revenue Using AI for CRM: Salesforce … Feb 12, 2024 — What is Salesforce Einstein? Salesforce Einstein is the first trusted artifici… TechForce Services How does Salesforce Use AI for Business Growth? Jan 31, 2024 — Powered by technologies like Machine Learning, Natural Language Processing, im… saasguru · LinkedIn · 7mo History of Salesforce AI From Predictive to Generative – LinkedIn Published Nov 27, 2023. In 2014, Salesforce, under the visionary leadership of… Twistellar AI in Salesforce: History, Present State and Prospects Organizations generate tons of data on marketing and sales, and surely your sales managers… Wikipedia Salesforce – Wikipedia In October 2014, Salesforce announced the development of its Customer Success Platform. Less than ten years ago, folks. Salesforce’s large database of data has helped the company address AI challenges quickly and with quality. The company’s data cloud offering provides AI with the right information at the right time, which can reduce friction and improve the customer experience.  Salesforce’s AI-powered solutions include: To catalyze this evolution, Salesforce strategically acquired RelateIQ in 2014. This move injected machine learning into the Salesforce ecosystem, capturing workplace communications data and providing valuable insights. Europe is home to many of these exciting companies. For example, H, a French AI agent startup, raised a $220 million seed round in May. Beyond RPA: The New Wave of AI Agents AI agents represent a significant step-change from Robotic Process Automation (RPA) bots, which, as explored last year, have several limitations due to their deterministic nature. Next-generation AI agents are non-deterministic, meaning that instead of stopping at a “dead end,” they can learn from mistakes and adjust their series of tasks. Not entirely unlock the mouse running the same maze over and over for the cheese. Eventually Mr. Squeakers learns which paths are dead ends and avoids them by making better choices at intersections. In AI Agents this makes them suited to complex and unstructured tasks and means they can transform the journey from intent to implementation in software development. They can deliver “pure work,” rather than acting only as a helpful co-pilot. The rise of AI agents is not only an opportunity to expand automation beyond what is possible with RPA but also to broadly redefine how knowledge work is performed. And by who. And even how is it defined. Given the right guardrails, next-generation AI agents have the potential to effectively and safely replace knowledge workers in many business scenarios. AI Agents in Action These agents are about to revolutionize the world of work as we know it and are already getting started. For example, Klarna recently revealed that its AI agent system handled two-thirds of customer chats in its first month in operation. While HR may not be swamped with AI CVs yet, it is certainly fathomable. One would suppose those candidates would have to be reviewed and interviewed by IT, not just HR. Here’s another deep thought. The internet of things (IoT) first appeared in a speech by Peter T. Lewis in September 1985. The Internet of Things (IoT) is a network of physical devices that can collect and transmit data over the internet using sensors, software, and other technologies. IoT devices can communicate with each other and with the cloud, and can even perform data analysis and be controlled remotely. The IoT concept was smart homes, health care environments, office spaces, and transportation. Only recently have we begun to think of the IoT as including the actual computers, or AI, in addition to sensored devices. It isn’t exactly a chicken and the egg question, but more of a

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Salesforce AI Journey

Salesforce AI Journey

“We’re on an Incredible Journey”: Why Salesforce’s AI Push is Just Beginning This article originally appeared in TechRadar by Mike Moore. Salesforce AI Journey. When it comes to leveraging AI to enhance global workforces, some companies are leading the charge, particularly when their technology is at the forefront. Salesforce, known for its robust AI tools, is one such company pushing the boundaries. At its recent World Tour London event, Salesforce emphasized its commitment to AI, showcasing how its Einstein tools are already benefiting customers worldwide. TechRadar Pro spoke with Paul O’Sullivan, SVP, Solution Engineering & UKI, CTO, Salesforce, about the company’s vision for enhancing efficiency and productivity across all markets, particularly in the UK. A Wave of Innovation “We’re on an incredible journey,” O’Sullivan stated, referencing Salesforce’s $4 billion investment in the UK and Ireland in 2023. “We’re well-positioned in the UK to maximize AI’s potential and help our customers achieve true value.” This ambition is epitomized by Salesforce’s new AI center in London. The 40,000 square foot facility is set to be a hub for AI collaboration and development, addressing the growing demand for AI technology. O’Sullivan hinted that this is just the beginning. “We’re an innovation-led company—always looking ahead,” he said, highlighting the UK’s history of driving innovation as a positive indicator for AI’s future in the capital. Growing Demand and Education As demand for AI tools and services increases among businesses of all sizes, O’Sullivan acknowledged the rapid pace of change in the AI landscape. “It starts with education—at all levels,” he noted, recognizing the varying degrees of AI knowledge among business leaders. O’Sullivan compared the current AI momentum to past technological revolutions like cloud computing, websites, and ecommerce. Companies had to adapt quickly to avoid falling behind, and he noted that the window for catching up with AI might be even smaller. He predicted a “steady wave of innovation” in AI before it becomes ubiquitous in the business world, with various models and platforms vying for dominance. “It feels like everyone is in a race for AI,” he added, “and there’s a collective agreement that AI will enhance productivity and efficiency, benefiting both the bottom and top lines of big enterprises.” Human Jobs and AI Looking forward, O’Sullivan dismissed concerns that AI would replace human jobs. He suggested that AI would instead create new opportunities for human workers. “I think human nature is inherently curious,” he said. “We will continue to explore new ways of doing things and offer different levels of connection and service.” Drawing parallels to the industrial revolution, O’Sullivan pointed out that machines didn’t eliminate jobs; they increased productivity and efficiency. He believes AI will have a similar impact. “We’re going to see a new level of productivity and efficiency with AI, just as we did with the industrial revolution,” he concluded. Salesforce’s AI journey is only just beginning, promising exciting advancements and opportunities for businesses and workers alike. 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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DHS AI Hires

DHS AI Hires

DHS Makes First AI Corps Hires to Enhance Federal AI Capabilities The Department of Homeland Security (DHS) has announced its first 10 hires for the newly formed AI Corps, as detailed in a release exclusively shared with Axios. Why It Matters U.S. officials emphasize the need for more expertise to determine the best ways to safely leverage artificial intelligence tools within the federal government. The AI Corps Initiative The new 50-person AI Corps, modeled after the U.S. Digital Service, will explore AI applications across DHS’s portfolio, including: Key Quote “The interest in it has been phenomenal,” Homeland Security Secretary Alejandro Mayorkas told Axios. “We need that expertise to really fuel our interest in leading the federal government in the safe and responsible deployment of AI to advance our mission.” New AI Corps Members According to a press release, the following individuals are joining DHS’s AI Corps: Between the Lines Competition for these roles has been intense. Mayorkas previously mentioned in April that over 3,000 applications were received for the 50 positions. The selected members come from diverse backgrounds, including current government employees, Big Tech, startups, and the research community. Hiring Practices and Future Moves New flexible hiring practices for AI-related jobs have enabled DHS to compete with private sector roles and fast-track AI Corps hires. “Things move more rapidly, and so we’re moving more rapidly, as well, to meet the moment,” Mayorkas added. 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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Salesforce Envision

Salesforce Envision

Salesforce: Enabling Customer Experiences Through Integrated Solutions Salesforce is a cloud-based CRM platform offering software, services, and applications designed to create valuable customer experiences. It integrates various organizational departments such as marketing, sales, and services, providing a unified view of the customer. Salesforce Envision is a digital transformation engagement. This insight outlines a typical customer’s journey through a Salesforce Envision Advisory engagement with the Tectonic team. We will highlight three key areas that contribute to a powerful and successful Envision engagement: Outcome-Driven Focus The cornerstone of any Salesforce Envision Advisory engagement is an upfront and honest conversation with the customer about their future business aspirations. Establishing desired outcomes and understanding how customers should perceive the business are critical. This focus is about envisioning future possibilities rather than dwelling on current systems. Collaboration with the Customer Alignment between the customer and the consulting team is essential. Success hinges on ensuring all organizational groups are aligned, as individual great ideas often fail without collective buy-in. Effective collaboration can prevent projects from delivering minimal benefits and adding redundant systems. Planning for Success Understanding customer goals and desired outcomes allows building on existing efforts within business and IT departments. This understanding helps create a roadmap for business transformation, enabling the customer to serve their customers better, achieve efficiencies, and scale for future growth. Every stakeholder must participate in creating this roadmap, aligning with organizational leadership. This roadmap is not merely a technology rollout plan but a capabilities roadmap to meet business outcomes. Business processes and existing IT systems must be analyzed and re-architected for successful execution. What is Salesforce Envision? Salesforce Envision is a design-led engagement enabling organizations to undergo digital transformation to unlock customer insights, build actionable roadmaps, and develop successful solutions. The principles guiding Salesforce Envision include: Envision engagements are typically co-led by a Salesforce Senior Business Architect and a Senior Salesforce Technical Architect to provide guidance and ensure customer alignment. A seasoned Salesforce Solutions Architect leads the Envision discovery process to ensure no stone is left unturned. Typical Phases of an Envision Engagement: The future-state capability matrix, developed from these strategies, identifies IT systems to be decommissioned, contributing to cost and maintenance benefits and supporting a solid business case. This elimination of technical debt can fully offset the long-term costs with the investment in your future success. Final Executive Brief The Envision engagement concludes with an executive brief for business and IT stakeholders, covering: By following these steps, organizations can effectively leverage Salesforce Envision to achieve their digital transformation goals. 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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Gemma 2 Available

Gemma 2 is now available to researchers and developers

News from Google – Gemma 2 is now available! Introducing Gemma 2: Advanced AI for Everyone Expanding Access to AI AI has the potential to solve some of humanity’s most pressing issues, but this can only happen if everyone has the tools to build with it. Earlier this year, Google introduced Gemma, a family of lightweight, state-of-the-art open models based on the same research and technology used to create the Gemini models. They’ve since expanded the Gemma family with CodeGemma, RecurrentGemma, and PaliGemma, each offering unique capabilities for various AI tasks. These models are easily accessible through integrations with partners like Hugging Face, NVIDIA, and Ollama. Launching Gemma 2 Google is now officially releasing Gemma 2 to researchers and developers worldwide. Available in both 9 billion (9B) and 27 billion (27B) parameter sizes, Gemma 2 offers higher performance and greater efficiency than its predecessor, along with significant safety enhancements. The 27B model provides competitive alternatives to models more than twice its size, achieving performance levels that were only possible with proprietary models as recently as last December. This performance is now achievable on a single NVIDIA H100 Tensor Core GPU or TPU host, significantly reducing deployment costs. Setting a New Standard for Efficiency and Performance Gemma 2 is built on a redesigned architecture, engineered for exceptional performance and inference efficiency. Here’s what sets it apart: Designed for Developers and Researchers Gemma 2 is not only more powerful but also easier to integrate into your workflows: Supporting Responsible AI Development Google is committed to providing resources for responsible AI development, including their Responsible Generative AI Toolkit. The recently open-sourced LLM Comparator helps with in-depth evaluation of language models. You can now use its companion Python library to run comparative evaluations and visualize the results. Additionally, we are working on open-sourcing our text watermarking technology, SynthID, for Gemma models. When training Gemma 2, Google followed rigorous safety processes, filtering pre-training data and performing extensive testing and evaluation to identify and mitigate potential biases and risks. They publish their results on public benchmarks related to safety and representational harms. Projects Built with Gemma The first Gemma launch led to over 10 million downloads and numerous inspiring projects. For instance, Navarasa used Gemma to create a model rooted in India’s linguistic diversity. Looking Ahead Gemma 2 will enable even more ambitious projects, unlocking new levels of performance and potential in AI creations. We will continue to explore new architectures and develop specialized Gemma variants for a broader range of AI tasks and challenges, including an upcoming 2.6B parameter model designed to bridge the gap between lightweight accessibility and powerful performance. Getting Started Gemma 2 is now available in Google AI Studio, allowing you to test its full performance capabilities at 27B without hardware requirements. You can also download Gemma 2’s model weights from Kaggle and Hugging Face Models, with Vertex AI Model Garden coming soon. To support research and development, Gemma 2 is acessable free of charge through Kaggle or a free tier for Colab notebooks. First-time Google Cloud customers may be eligible for $300 in credits. Academic researchers can apply for the Gemma 2 Academic Research Program to receive Google Cloud credits to accelerate their research with Gemma 2. Applications are open now through August 9. 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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Generative AI Regulations

AI Manipulation

The Future of AI: Convenience and Risk Our lives are on the brink of being transformed by conversational AI agents designed to anticipate our needs, offer tailored information, and perform useful tasks on our behalf. These agents will rely on extensive personal data, including our interests, hobbies, backgrounds, aspirations, personality traits, and political views, all aimed at making our lives more convenient. What then will be the source of AI Manipulation? Advanced AI Agents: The Next Generation These AI agents are becoming increasingly sophisticated. OpenAI recently released GPT-4o, a next-generation chatbot capable of reading human emotions. It does this not only by analyzing the sentiment in written text but also by assessing voice inflections (if spoken to through a mic) and facial cues (if interacting via video). This rapid development signifies the future of computing. Google, for instance, announced Project Astra, an advanced seeing and talking responsive agent designed to interact conversationally while understanding its surroundings. This allows it to provide real-time interactive guidance and assistance. OpenAI’s Sam Altman has predicted that assistive agents will be the killer app for AI. He envisions a future where everyone has a personalized agent acting as a super-competent colleague, knowing everything about their life to take useful actions on their behalf. The Potential Risks-AI Manipulation While this sounds promising, significant risks accompany these advancements. As I wrote in VentureBeat last year, AI agents pose a risk to human agency through targeted manipulation. This risk is particularly acute as these agents become embedded in our mobile devices, which are gateways to our digital lives. These devices provide AI agents with a continuous flow of our personal information, enabling them to learn intimate details about us while filtering the content we receive. Such systems could become powerful tools for interactive manipulation. AI agents equipped with cameras and microphones will react to our environments without explicit prompts, potentially triggering targeted influences based on our activities and situations. Public Perception and Adoption Despite the creepy level of tracking and intervention, I predict that people will embrace this technology. These agents will be designed to make our lives easier, providing reminders, tutoring, and even social coaching. The competition among tech companies will drive rapid adoption, with individuals feeling disadvantaged if they do not use these features. By 2030, these technologies will likely be ubiquitous. The AI Manipulation Problem In my new book, “Our Next Reality,” I discuss how AI agents can empower us with mental superpowers while also serving as tools for persuasion. AI agents, designed for profit, will influence our thoughts and behaviors. They will be more effective than traditional content because they can engage us interactively, using sophisticated techniques based on extensive personal data. These agents will read our emotions with unparalleled precision, adapting their influence tactics in real-time. Without regulation, they could document our reactions to tailor their approaches, making them highly effective at persuasion. The agents’ appearances could also be optimized to maximize their impact on us personally. Feedback Control and the Need for Regulation The technical danger of AI agents lies in their feedback control capabilities. Given an “influence objective,” these agents can continuously adapt their strategies to maximize their impact on us. This ability is similar to heat-seeking missiles adjusting their path in real-time to hit a target. To mitigate this risk, regulators must impose strict limits on interactive conversational advertising, which is the gateway to more dangerous uses of these technologies. If unchecked, this could lead to an arms race among tech companies to develop the most effective conversational ads, ultimately driving misinformation and propaganda. The Urgent Need for Regulatory Action The time for policymakers to act is now. While traditional AI risks like generating misinformation at scale are significant, targeted interactive manipulation poses a far greater threat. Recent announcements from OpenAI and Google highlight the urgency for regulation. An outright ban or stringent limitations on interactive conversational advertising is a crucial first step. Without such measures, we risk allowing AI agents to become powerful tools of manipulation. Conclusion The future of AI holds both promise and peril. As conversational AI agents become integral to our daily lives, we must balance their benefits with the potential for abuse. Regulatory action is essential to ensure these technologies enhance our lives without compromising our autonomy. Louis Rosenberg, PhD, is an American technologist specializing in AI and XR. His new book, “Our Next Reality,” explores the impact of AI on society and is published by Hachette. He earned his PhD from Stanford, was a professor at California Polytechnic, and is currently CEO of Unanimous AI. This piece originally appeared in VentureBeat on 5/17/24. 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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Salesforce Carbon Removal Technology

Salesforce Carbon Removal Technology

Salesforce Bets Big on Carbon Removal Technology Salesforce has revealed three major climate efforts, including a $25 million investment to advance carbon removal, to hasten the transition to a fair and sustainable energy system, including Salesforce has revealed three major climate efforts, including Salesforce carbon removal technology. Salesforce, the cloud software giant based in San Francisco, has committed $25 million to carbon dioxide removal technology through Frontier. This funding aims to support carbon removal startups, demonstrating demand for their products and accelerating industry growth. This initial investment is part of a larger pledge Salesforce made in 2022 to purchase $100 million worth of carbon dioxide removal credits by 2030. “This industry needs to scale. It needs investment early on so that today’s technologies can become commercially available to all buyers by 2030 and beyond,” says Jamila Yamani, Salesforce’s lead for carbon removal. The Need for Carbon Removal While many climate strategies focus on reducing emissions, carbon removal seeks to pull carbon dioxide from the atmosphere through various methods. This emerging technology has garnered both interest and criticism due to its current high costs and developmental stage. For instance, direct air capture—a form of carbon removal—costs about $600 to $1,000 per ton of CO2 removed, according to a 2023 estimate by a Boston Consulting Group executive. To be widely adopted, costs need to fall below $200 per ton. Frontier’s Role Frontier, a subsidiary of fintech company Stripe, aims to accelerate carbon removal technology development by creating a market for it. “Carbon removal has no intrinsic value, so there isn’t a natural customer like with energy,” explains Nan Ransohoff, head of climate at Stripe and Frontier. This lack of natural customers means fewer startups in the space, and those that exist offer high rates due to the lack of scale. Frontier’s funds become revenue for startups, not equity investments. To date, Frontier has over $1 billion committed, with around $230 million in contracted offtake agreements with specific companies. Expanding the Market “The idea with Frontier was to send a loud demand signal to entrepreneurs, investors, and researchers that there is a market for carbon removal technologies, especially those in early stages,” says Ransohoff. “This is now over a billion dollars of revenue for carbon removal companies.” Salesforce joins other major companies in Frontier, including Google parent company Alphabet, Meta, Stripe, Shopify, and McKinsey Sustainability. Frontier’s portfolio includes technologies like Lithos, which uses basalt to capture CO2, and Heirloom, which employs limestone as a carbon sink. The Advantage of Frontier Though companies like Salesforce could establish their own offtake agreements, Ransohoff notes that pooling resources through Frontier sends a “louder signal” to the market and allows for better vetting of startups. “We have six PhDs full-time on staff dedicated to finding and vetting the best companies,” she says. Salesforce’s Comprehensive Climate Strategy Carbon removal is just one part of Salesforce’s broader climate change mitigation strategy. Yamani highlights that Salesforce also focuses on grid decarbonization, nature restoration, sustainable aviation, decentralized renewable energy, and more. The company aims to cut its absolute emissions by 50% by 2030 and reach “near zero by 2040.” “It’s an all-of-the-above approach where we’re leveraging all tools to build a portfolio that can help decarbonize the planet. It’s not a one-size-fits-all solution,” says Yamani. At the core, lies Salesforce Net Zero Cloud. In addition to the carbon removal commitment, Salesforce recently signed a 15-year virtual power purchase agreement with Qualitas Energy in Italy to expand its solar energy portfolio. “Corporate procurements can catalyze new renewable energy markets, providing clean electricity worldwide,” Yamani adds. With this move, Salesforce is demonstrating its commitment to achieving 100% renewable energy dependency and is making its first step into virtual power purchase agreements (VPPAs) in Europe. 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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Lakeflow for Data Engineering

Lakeflow for Data Engineering

Databricks unveiled Databricks LakeFlow last week, a new tool designed to unify all aspects of data engineering, from data ingestion and transformation to orchestration. What is Databricks LakeFlow? According to Databricks, LakeFlow simplifies the creation and operation of production-grade data pipelines, making it easier for data teams to handle complex data engineering tasks. This solution aims to meet the growing demands for reliable data and AI by providing an efficient and streamlined approach. The Current State of Data Engineering Data engineering is crucial for democratizing data and AI within businesses, yet it remains a challenging field. Data teams must often deal with: How LakeFlow Addresses These Challenges LakeFlow offers a unified experience for all aspects of data engineering, simplifying the entire process: Key Features of LakeFlow LakeFlow comprises three main components: LakeFlow Connect, LakeFlow Pipelines, and LakeFlow Jobs. Availability LakeFlow is entering preview soon, starting with LakeFlow Connect. Customers can register to join the waitlist today. 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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AI Impact on Workforce

AI Impact on Workforce

About a month ago, Jon Stewart did a segment on AI causing people to lose their jobs. He spoke against it. Well, his words were against it, but deep down, he’s for it—and so are you, whether you realize it or not. AI Impact on Workforce is real, but is it good or bad? The fact that Jon Stewart can go on TV to discuss cutting-edge technology like large language models in AI is because previous technology displaced jobs. Lots of jobs. What probably felt like most jobs. Remember, for most of human history, 80–90% of people were farmers. The few who weren’t had professions like blacksmithing, tailoring, or other essential trades. They didn’t have TV personalities, TV executives, or even TVs. Had you been born hundreds of years ago, chances are you would have been a farmer, too. You might have died from an infection. But as scientific and technological progress reduced the need for farmers, it also gave us doctors and scientists who discovered, manufactured, and distributed cures for diseases like the plague. Innovation begets innovation. Generative AI is just the current state of the art, leading the next cycle of change. The Core Issue This doesn’t mean everything will go smoothly. While many tech CEOs tout the positive impacts of AI, these benefits will take time. Consider the automobile: Carl Benz patented the motorized vehicle in 1886. Fifteen years later, there were only 8,000 cars in the US. By 1910, there were 500,000 cars. That’s 25 years, and even then, only about 0.5% of people in the US had a car. The first stop sign wasn’t used until 1915, giving society time to establish formal regulations and norms as the technology spread. Lessons from History Social media, however, saw negligible usage until 2008, when Facebook began to grow rapidly. In just four years, users soared from a few million to a billion. Social media has been linked to cyberbullying, self-esteem issues, depression, and misinformation. The risks became apparent only after widespread adoption, unlike with cars, where risks were identified early and mitigated with regulations like stop signs and driver’s licenses. Nuclear weapons, developed in 1945, also illustrate this point. Initially, only a few countries possessed them, understanding the catastrophic risks and exercising restraint. However, if a terrorist cell obtained such weapons, the consequences could be dire. Similarly, if AI tools are misused, the outcomes could be harmful. Just this morning a news channel was covering an AI bot that was doing robo-calling. Can you imagine the increase in telemarketing calls that could create? How about this being an election cycle year? AI and Its Rapid Adoption AI isn’t a nuclear weapon, but it is a powerful tool that can do harm. Unlike past technologies that took years or decades to adopt, AI adoption is happening much faster. We lack comprehensive safety warnings for AI because we don’t fully understand it yet. If in 1900, 50% of Americans had suddenly gained access to cars without regulations, the result would have been chaos. Similarly, rapid AI adoption without understanding its risks can lead to unintended consequences. The adoption rate, impact radius (the scope of influence), and learning curve (how quickly we understand its effects) are crucial. If the adoption rate surpasses our ability to understand and manage its impact, we face excessive risk. Proceeding with Caution Innovation should not be stifled, but it must be approached with caution. Consider historical examples like x-rays, which were once used in shoe stores without understanding their harmful effects, or the industrial revolution, which caused significant environmental degradation. Early regulation could have mitigated many negative impacts. AI is transformative, but until we fully understand its risks, we must proceed cautiously. The potential for harm isn’t a reason to avoid it altogether. Like cars, which we accept despite their risks because we understand and manage them, we need to learn about AI’s risks. However, we don’t need to rush into widespread adoption without safeguards. It’s easier to loosen restrictions later than to impose them after damage has been done. Let’s innovate, but with foresight. Regulation doesn’t kill innovation; it can inspire it. We should learn from the past and ensure AI development is responsible and measured. We study history to avoid repeating mistakes—let’s apply that wisdom to AI. Content updated July 2024. 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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Agentic AI is Here

Agentic AI is Here

Embracing the Era of Agentic AI: Redefining Autonomous Systems A new paradigm in artificial intelligence, known as “Agentic Artificial Intelligence,” is poised to revolutionize the capabilities of the known autonomous universe. This cutting-edge technology represents a significant leap forward in AI-driven decision-making and action, promising transformative impacts across various industries including healthcare, manufacturing, IT, finance, marketing, and HR. Agents are the way to go! There is no two ways about this. Looking into the progression of the Large Language Model based applications since last year, its not hard to see that the Agentic Process (agents as reusable, specific and dedicated single unit of work) — would be the way to build Gen AI applications. What is Agentic AI? Agentic Artificial Intelligence marks a departure from traditional AI models that primarily focus on passive observation and analysis. Unlike its predecessors, which often require human intervention to execute tasks, Agentic AI systems possess the autonomy to initiate actions independently based on their assessments. This allows them to navigate much more complex environments and undertake tasks with a level of initiative and adaptability previously unseen. At least outside of sci-fy movies. Real-World Applications of Agentic Artificial Intelligence Healthcare In healthcare, Agentic AI systems are transforming patient care. These systems autonomously monitor vital signs, administer medication, and assist in surgical procedures with unparalleled precision. By augmenting healthcare professionals’ capabilities, these AI-driven agents enhance patient outcomes and streamline care processes. Augmenting is the key word, here. Manufacturing and Logistics In manufacturing and logistics, Agentic AI optimizes operations and boosts efficiency. Intelligent agents handle predictive maintenance of machinery, autonomous inventory management, and robotic assembly. Leveraging advanced algorithms and sensor technologies, these systems anticipate issues, coordinate complex workflows, and adapt to real-time production demands, driving a shift towards fully autonomous production environments. Customer Service Within enterprises, AI agents are revolutionizing business operations across various departments. In customer service, AI-powered chatbots with Agentic Artificial Intelligence capabilities engage with customers in natural language, providing personalized assistance and resolving queries efficiently. This enhances customer satisfaction and allows human agents to focus on more complex tasks. Marketing and Sales Agentic Artificial Intelligence empowers marketing and sales teams to analyze vast datasets, identify trends, and personalize campaigns with unprecedented precision. By understanding customer behavior and preferences at a granular level, AI agents optimize advertising strategies, maximize conversion rates, and drive revenue growth. Finance and Accounting In finance and accounting, Agentic AI streamlines processes like invoice processing, fraud detection, and risk management. These AI-driven agents analyze financial data in real time, flag anomalies, and provide insights that enable faster, more informed decision-making, thereby improving operational efficiency. Ethical Considerations of Agentic Artificial Intelligence The rise of Agentic AI also brings significant ethical and societal challenges. Concerns about data privacy, algorithmic bias, and job displacement necessitate robust regulation and ethical frameworks to ensure responsible and equitable deployment of AI technologies. Navigating the Future with Agentic AI The advent of Agentic AI ushers in a new era of autonomy and innovation in artificial intelligence. As these intelligent agents permeate various facets of our lives and enterprises, they present both challenges and opportunities. To navigate this new world, we must approach it with foresight, responsibility, and a commitment to harnessing technology for the betterment of humanity. 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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SFR-Embedding v2 from Salesforce

SFR-Embedding v2 from Salesforce

The release of Salesforce Embedding Model version 2 (SFR-embedding-v2) marks a notable milestone in the field of Natural Language Processing (NLP), underscoring Salesforce’s commitment to advancing AI technologies. SFR-Embedding v2 from Salesforce. Key Highlights of the SFR-embedding-v2 Model Release: Achievement on MTEB Benchmark: SFR-embedding-v2 has achieved a top-1 position on the HuggingFace MTEB benchmark, surpassing a performance score of 70+. This accomplishment reflects its advanced capabilities and the rigorous development undertaken by Salesforce’s research team. Enhanced Multitasking Capabilities: The model introduces a new multi-stage training recipe aimed at enhancing multitasking abilities. This innovative approach enables simultaneous performance across multiple tasks, significantly improving versatility and efficiency. Advancements in Classification and Clustering: Significant strides have been made in classification and clustering tasks, enhancing the model’s ability to understand and categorize data accurately. These improvements make SFR-embedding-v2 highly effective across diverse applications, from data sorting to pattern identification. Strong Retrieval Performance: Beyond classification and clustering, the model excels in retrieval tasks, efficiently locating and retrieving relevant information from extensive datasets. This capability is crucial for AI applications requiring rapid access to data insights. Technical Specifications: SFR-embedding-v2 boasts a substantial size with 7.11 billion parameters and utilizes the BF16 tensor type. These technical specifications contribute to its robust performance and capacity to handle complex tasks, showcasing Salesforce’s innovative AI model architecture. Community and Collaboration: Developed collaboratively by a dedicated team of Salesforce researchers including Rui Meng, Ye Liu, Tong Niu, Shafiq Rayhan Joty, Caiming Xiong, Yingbo Zhou, and Semih Yavuz, the model integrates diverse expertise and innovative approaches, pivotal to its success. Future Directions: Salesforce continues to explore new avenues and enhancements for the model. Future updates aim to push the boundaries of AI capabilities, addressing current limitations and expanding its utility across various sectors. Practical Applications: The versatility of SFR-embedding-v2 extends to text generation, feature extraction, and natural language understanding, making it invaluable across industries such as healthcare and finance where accurate and efficient data processing is critical. In summary, the release of Salesforce Embedding Model version 2 represents a significant advancement in AI technology. Its top performance on benchmarks, enhanced multitasking capabilities, and improvements in critical tasks like classification and clustering underscore its potential to revolutionize AI applications. Supported by robust technical specifications and ongoing research efforts, SFR-embedding-v2 is poised to lead the AI community forward with its innovative capabilities. 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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Metrics That Matter in Field Service

Metrics That Matter in Field Service

To gauge success in field service management, it’s crucial to measure key metrics, ranging from tactical aspects to customer satisfaction. Establishing Key Performance Indicators (KPIs) for field service can significantly contribute to the efficiency and revenue growth of your business. This guide outlines straightforward fixes and capabilities to achieve positive results in field service and attain faster time-to-value. If your company has recently invested in AI-powered field service management, you’re already taking steps to future-proof your business. The next essential move is to integrate your field service management solution with your Customer Relationship Management (CRM) platform. This connection provides a comprehensive customer view, highlighting operational efficiency and areas for improvement. By connecting these systems, you gain insights into crucial field service metrics, spanning from tactical elements like first-time fix rate and time to site to the paramount field service KPI: customer satisfaction. If improvements are not evident in these areas, consider implementing simple fixes and capabilities to yield positive outcomes for both customers and operational costs. Customer Satisfaction: Employee and Contractor Turnover: Overall Costs: Sales Leads: If your field service operation shows room for improvement in these core metrics, consider taking a guided course on field service basics to maximize the potential of your field service management solution. Transform your business by incorporating AI-powered field service management, setting up your teams for success and achieving increased revenue, productivity, and cost savings. 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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Time to Reset AI Expectations

Time to Reset AI Expectations

AI is often portrayed as either the ultimate solution to all our problems or a looming threat that must be handled with extreme caution. These are the two polar extremes of a debate that surrounds any transformative technology, and the reality likely lies somewhere in the middle. Time to Reset AI Expectations. At the recent 2024 MIT Sloan CIO Symposium, AI was the central theme, with numerous keynotes and panels devoted to the topic. The event also featured informal roundtable discussions that touched on legal risks in AI deployment, AI as a driver for productivity, and the evolving role of humans in AI-augmented workplaces. Time to Reset AI Expectations A standout moment was the closing keynote, “What Works and Doesn’t Work with AI,” delivered by MIT professor emeritus Rodney Brooks. Brooks, who directed the MIT AI Lab from 1997 to 2003 and was the founding director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) until 2007, offered insights to distinguish between the hype and reality of AI. A seasoned robotics entrepreneur, Brooks founded several companies, including iRobot, Rethink Robotics, and Robust.AI. In his keynote, Brooks introduced his “Three Laws of Artificial Intelligence,” which serve to ground our understanding of AI: Brooks reminded the audience that AI has been a formal academic discipline since the 1950s when its pioneers believed that nearly every aspect of human intelligence could, in principle, be encoded as software and executed by increasingly powerful computers. Decades of Efforts In the 1980s, leading AI researchers were confident that within a generation, AI systems capable of human-like cognitive abilities could be developed. They secured government funding to pursue this vision. However, these projects underestimated the complexities of replicating human intelligence, particularly cognitive functions like language, thinking, and reasoning, in software. After years of unmet expectations, these ambitious projects were largely abandoned, leading to the so-called AI winter—a period of reduced interest and funding in AI. AI experienced a resurgence in the 1990s with a shift towards a statistical approach that analyzed patterns in vast amounts of data using sophisticated algorithms and high-performance supercomputers. This data-driven approach yielded results that approximated intelligence and scaled far better than the earlier programming-based models. Over the next few decades, AI achieved significant milestones, including Deep Blue’s 1997 victory over chess grandmaster Garry Kasparov, Watson’s 2011 win in the Jeopardy! Challenge, and AlphaGo’s 2016 triumph over Lee Sedol, one of the world’s top Go players. AI also made strides in autonomous vehicles, as evidenced by the successful completion of the 2007 DARPA Grand Challenge and the 2012 DARPA Robotics Challenge for disaster response robots. Is It Different Now? Following these achievements, AI seemed poised to “change everything,” according to Brooks. But is it really? Since 2017, Brooks has published an annual Predictions Scorecard, comparing predictions for future milestones in robotics, AI, machine learning, self-driving cars, and human space travel. “I made my predictions because, then as now, I saw an immense amount of hype surrounding these topics,” Brooks said. He observed that the media and public were making premature conclusions about the impact of AI on jobs, road safety, space exploration, and more. “My predictions, complete with timelines, were meant to temper expectations and inject some reality into what I saw as irrational exuberance.” So why have so many AI predictions missed the mark? Brooks, who has a penchant for lists, attributes this to what he calls the Seven Deadly Sins of Predicting the Future of AI. In a 2017 essay, he described these “sins”: The takeaway? While AI has made remarkable progress, there’s still a long journey ahead. It’s Time to Reset AI Expectations. 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 Feeding Post-Pandemic AI-Powered Digital Transformation

Salesforce Feeding Post-Pandemic AI-Powered Digital Transformation

The Digital Transformation Imperative: Salesforce’s AI Solutions The COVID-19 pandemic didn’t just accelerate digital transformation; it cemented it as an existential imperative for businesses across all industries. The sudden shift to remote work, digital customer engagement, and e-commerce highlighted the stark contrast between organizations that had prioritized digitization and those that hadn’t. In the post-pandemic era, digital agility has become synonymous with resilience and competitiveness. Salesforce Feeding Post-Pandemic AI-Powered Digital Transformation with unparalled innovation. However, the path to digital transformation remains challenging for many companies. Legacy systems, data silos, and manual processes continue to hinder adaptation and innovation at the pace demanded by today’s market and consumer. This has led to a certain weariness and skepticism around transformation initiatives, often perceived as an ever-receding target. Salesforce’s AI-Powered Integration Solutions Salesforce’s AI-powered integration solutions aim to revitalize the digital transformation journey. With tools like Einstein for Flow, Intelligent Document Processing (IDP), and Einstein for MuleSoft, Salesforce is embedding AI across its automation and integration portfolio to address some of the most difficult challenges in digitization. Anypoint Partner Manager: Harnessing AI for B2B Integration Salesforce’s latest MuleSoft offering, Anypoint Partner Manager, exemplifies this AI-centric approach. The cloud-native B2B integration solution leverages IDP to streamline partner onboarding and manage API and EDI-based transactions, addressing a key pain point for companies in complex supply chain ecosystems. “EDI has historically been that code-driven solution. You must really know the EDI spec,” noted Andrew Comstock, VP of Product Management at Salesforce. “Partner Manager actually brings the partner definition into a form, and you can just define that, save it, and you’re off and done. We can deploy all the applications that you need for you.” By using AI to extract and structure data from unstructured documents like invoices and purchase orders, Anypoint Partner Manager democratizes B2B integration, making it accessible to businesses beyond the traditional technology sector. The solution is now generally available. MuleSoft Accelerator for Salesforce Order Management: Bridging B2B and B2C Salesforce also introduced the MuleSoft Accelerator for Salesforce Order Management. This tool provides pre-built APIs, connectors, and templates to unify B2B and B2C orders from a centralized hub. By connecting Salesforce OMS with ERP systems in real-time, the accelerator enables end-to-end visibility across channels, a critical capability in today’s omnichannel environment. “For many companies, [order management] is super critical and vital,” emphasized Comstock. “The more that they can standardize and centralize that, the better visibility, controls, and governance they have.” The MuleSoft Accelerator for Salesforce OMS is now generally available. The AI Imperative in Digital Transformation Salesforce’s AI-powered integration solutions come at a time when businesses are grappling with the realities of the post-pandemic digital imperative. Automating complex B2B processes, unifying data flows across ecosystems, and extracting insights from unstructured data is no longer a luxury but a necessity for survival in the digital economy. Salesforce Feeding Post-Pandemic AI-Powered Digital Transformation “A lot of our successes are happening at companies that are not traditional technology companies. Using solutions like MuleSoft and Salesforce allows them to build those technologies better,” noted Comstock. In this context, AI is emerging as a key enabler of digital transformation at scale. By abstracting complexity and automating manual tasks, AI-powered integration tools like those from Salesforce are helping businesses overcome the hurdles that have long stymied digitization efforts. For companies still wrestling with the challenges of digital transformation, Salesforce’s AI-powered integration portfolio offers a glimmer of hope. By harnessing the power of large language models and other AI technologies to streamline integration and automation, Salesforce is providing a new path forward for organizations looking to thrive in the post-pandemic digital landscape. Salesforce Feeding Post-Pandemic AI-Powered Digital Transformation with Einstein, Mulesoft, Flow, and more. 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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