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Salesforce Enhances Nonprofit Cloud with Generative AI

Salesforce Enhances Nonprofit Cloud with Generative AI

Salesforce Enhances Nonprofit Cloud with Generative AI Capabilities On August 6, 2024, Salesforce Inc. announced that its Nonprofit Cloud platform is the latest in its suite to receive a boost from generative artificial intelligence (AI). This marks a significant milestone as it is the first integration of the Einstein 1 Platform within Salesforce’s Industry Cloud portfolio. The update introduces AI-powered tools designed to help nonprofits enhance operational efficiency, tailor donor engagement, and discover new funding opportunities. Notable features include AI-generated personalized gift proposals and concise summaries of program successes, grant details, donor histories, and more. This move signals Salesforce’s broader strategy to embed AI solutions across its industry-specific offerings, potentially transforming how various sectors leverage AI tools. The new features, such as AI-powered fundraising tools and program summaries, aim to help nonprofits navigate an increasingly challenging landscape by improving efficiency, personalizing donor engagement, and boosting fundraising efforts. Additionally, Salesforce launched Data Cloud for Nonprofits, a new product designed to unify and harmonize data, providing a comprehensive view of donors, volunteers, and program participants. Salesforce’s Nonprofit Cloud, introduced last year, is a specialized version of its renowned customer relationship management (CRM) platform. It offers all the essential CRM features alongside tools tailored for nonprofit organizations. These include donor management capabilities, fundraising tools, and tracking systems for program participation and outcomes. AI-Driven Outreach and Summaries The latest update integrates Salesforce’s Einstein 1 generative AI platform into Nonprofit Cloud, offering AI-powered tools to enhance operational efficiency. For instance, the new fundraising gift proposals feature uses generative AI to create personalized proposals based on an organization’s data, such as previous donor interactions and supported causes. This automation aims to reduce the time needed for nonprofits to solicit financial support. Salesforce Enhances Nonprofit Cloud with Generative AI Aligning With Four Pillars This development aligns with Salesforce’s “four-pillar” approach to enterprise AI success: By integrating the Einstein 1 Platform into Nonprofit Cloud, Salesforce is showcasing its broader AI strategy across its portfolio. Similar integrations could soon follow for other Industry Cloud offerings, potentially accelerating AI adoption in various sectors. Salesforce also introduced the Einstein Summaries feature, which is expected to add significant value by helping nonprofits better understand program success, enhance donor engagement, and simplify the grant review process through AI-generated summaries. Data Cloud for Nonprofits The new Data Cloud for Nonprofits merges structured and unstructured data from various sources into a unified model. This integration enables nonprofits to create comprehensive views of donors, volunteers, and program participants, ultimately helping them assess program performance and fundraising effectiveness. Salesforce unveiled three key innovations for Nonprofit Cloud, each addressing specific challenges in the nonprofit sector: Data Cloud for Nonprofits is available immediately, while the AI-powered fundraising gift proposals and summaries will be generally available this fall. Salesforce also introduced Nonprofit Cloud Einstein 1 Edition, which bundles Nonprofit Cloud, Data Cloud, Einstein, Experience Cloud, and Slack. Nonprofits Embracing AI According to Salesforce, nonprofits are eager for these AI capabilities. The sixth edition of its annual Nonprofit Trends Report highlights the growing need for diversified fundraising strategies and cost reduction. Lori Freeman, Global GM for Salesforce for Nonprofits, emphasized the transformative potential of these innovations: “We’re at a watershed moment for nonprofits. AI is not just another tech trend; it’s a game-changer that could help organizations overcome critical challenges like increased demand for services, rising costs, and donor attrition. By embedding AI directly into Nonprofit Cloud, we’re enabling organizations to streamline their workflows, gain deeper insights, and ultimately increase their impact – all while maintaining the highest standards of data security and trust.” Salesforce’s new Data Cloud for Nonprofits is available now, with AI-powered fundraising gift proposals and summaries features rolling out this fall. Early adopters have already reported positive experiences with the new capabilities. Julie Fleshman, CEO of the Pancreatic Cancer Action Network, praised Nonprofit Cloud for streamlining clinical trial finder and physician database initiatives, stating that Salesforce is helping advance their mission by connecting patients with specialized healthcare providers and relevant clinical trials. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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SearchGPT and Knowledge Cutoff

SearchGPT and Knowledge Cutoff

Tackling the Knowledge Cutoff Challenge in Generative AI In the realm of generative AI, a significant hurdle has been the issue of knowledge cutoff—where a large language model (LLM) only has information up until a specific date. This was an early concern with OpenAI’s ChatGPT. For example, the GPT-4o model that currently powers ChatGPT has a knowledge cutoff in October 2023. The older GPT-4 model, on the other hand, had a cutoff in September 2021. Traditional search engines like Google, however, don’t face this limitation. Google continuously crawls the internet to keep its index up to date with the latest information. To address the knowledge cutoff issue in LLMs, multiple vendors, including OpenAI, are exploring search capabilities powered by generative AI (GenAI). Introducing SearchGPT: OpenAI’s GenAI Search Engine SearchGPT is OpenAI’s GenAI search engine, first announced on July 26, 2024. It aims to combine the strengths of a traditional search engine with the capabilities of GPT LLMs, eliminating the knowledge cutoff by drawing real-time data from the web. SearchGPT is currently a prototype, available to a limited group of test users, including individuals and publishers. OpenAI has invited publishers to ensure their content is accurately represented in search results. The service is positioned as a temporary offering to test and evaluate its performance. Once this evaluation phase is complete, OpenAI plans to integrate SearchGPT’s functionality directly into the ChatGPT interface. As of August 2024, OpenAI has not announced when SearchGPT will be generally available or integrated into the main ChatGPT experience. Key Features of SearchGPT SearchGPT offers several features designed to enhance the capabilities of ChatGPT: OpenAI’s Challenge to Google Search Google has long dominated the search engine landscape, a position that OpenAI aims to challenge with SearchGPT. Answers, Not Links Traditional search engines like Google act primarily as indexes, pointing users to other sources of information rather than directly providing answers. Google has introduced AI Overviews (formerly Search Generative Experience or SGE) to offer AI-generated summaries, but it still relies heavily on linking to third-party websites. SearchGPT aims to change this by providing direct answers to user queries, summarizing the source material instead of merely pointing to it. Contextual Continuity In contrast to Google’s point-in-time search queries, where each query is independent, SearchGPT strives to maintain context across multiple queries, offering a more seamless and coherent search experience. Search Accuracy Google Search often depends on keyword matching, which can require users to sift through several pages to find relevant information. SearchGPT aims to combine real-time data with an LLM to deliver more contextually accurate and relevant information. Ad-Free Experience SearchGPT offers an ad-free interface, providing a cleaner and more user-friendly experience compared to Google, which includes ads in its search results. AI-Powered Search Engine Comparison Here’s a comparison of the AI-powered search engines available today: Search Engine Platform Integration Publisher Collaboration Ads Cost SearchGPT (OpenAI) Standalone prototype Strong emphasis Ad-free Free (prototype stage) Google SGE Built on Google’s infrastructure SEO practices, content partnerships Includes ads Free Microsoft Bing AI/Copilot Built on Microsoft’s infrastructure SEO practices, content partnerships Includes ads Free Perplexity AI Standalone Basic source attribution Ad-free Free; $20/month for premium You.com AI assistant with various modes Basic source attribution Ad-free Free; premium tiers available Brave Search Independent search index Basic source attribution Ad-free Free Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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Data Cloud and Genpact

Data Cloud and Genpact

Genpact (NYSE: G), a global professional services and solutions firm delivering outcomes that shape the future, announced today its integration with Salesforce Data Cloud to offer AI-driven industry-specific cloud solutions to transform operations and drive competitive advantages for enterprises. Genpact’s integration with Data Cloud will solve issues associated with disconnected and unstructured data, including poor quality, accessibility, and scalability. By combining Genpact’s deep industry knowledge in consumer goods, life sciences, manufacturing, banking, and insurance, with Data Cloud, businesses can improve decision-making, optimize operations, and drive growth. “To navigate an increasingly complex business environment, business leaders must unlock the full potential of their data assets – but they can only do so if they have a more holistic view,” said Riju Vashisht, Chief Growth Officer, Genpact. “Our partnership with Salesforce combines our data, technology, and AI expertise and a global talent pool with the Salesforce Data Cloud, helping businesses break down data silos, gain real-time insights, and deliver personalized experiences at scale.” Data Cloud offers a 360-degree view of the customer every team can act on by seamlessly connecting, unifying, and activating data that lives in silos across an organization. This allows Genpact to better enable automation, analytics, and personalized engagement. Genpact has also launched a comprehensive training program for its employees on Salesforce’s Einstein AI and Data Cloud platforms to enhance skills and boost innovation to stay ahead in the AI landscape.  “Data Cloud is the platform powering organizations’ data and AI driven engagement. Data Cloud seamlessly harmonizes and unifies structured and unstructured data to deliver integrated experiences,” said Rahul Auradkar, EVP and GM, Unified Data Services and Einstein, Salesforce. “The AI revolution is about data, Data Cloud in concert with Salesforce Einstein 1 platform drives predictive and generative AI, automation, and analytics for customer engagement.” Genpact’s collaboration with Salesforce underscores the company’s commitment to delivering high-quality solutions and achieving client satisfaction within the Salesforce ecosystem. Visit here for more information about Genpact and Salesforce. About Genpact Genpact (NYSE: G) is a global professional services and solutions firm delivering outcomes that shape the future. Our 125,000+ people across 30+ countries are driven by our innate curiosity, entrepreneurial agility, and desire to create lasting value for clients. Powered by our purpose – the relentless pursuit of a world that works better for people – we serve and transform leading enterprises, including the Fortune Global 500, with our deep business and industry knowledge, digital operations services, and expertise in data, technology, and AI. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Impact of EHR Adoption

Connected Care Technology

How Connected Care Technology Can Transform the Provider Experience Northwell Health is leveraging advanced connected care technologies, including AI, to alleviate administrative burdens and foster meaningful interactions between providers and patients. While healthcare technology has revolutionized traditional care delivery models, it has also inadvertently created barriers, increasing the administrative workload and distancing providers from their patients. Dr. Michael Oppenheim, Senior Vice President of Clinical Digital Solutions at Northwell Health, highlighted this challenge during the Connected Health 2024 virtual summit, using a poignant illustration published a decade ago in the Journal of the American Medical Association. The image portrays a physician focused on a computer with their back to a patient and family, emphasizing how technology can inadvertently shift attention away from patient care. Reimagining Technology to Enhance Provider-Patient Connections To prevent technology from undermining the patient-provider relationship, healthcare organizations must reduce the administrative burden and enhance connectivity between patients and care teams. Northwell Health exemplifies this approach by implementing innovative solutions aimed at improving access, efficiency, and communication. 1. Expanding Access Without Overloading Providers Connected healthcare technologies can dramatically improve patient access but may strain clinicians managing large patient panels. Dr. Oppenheim illustrated how physicians often need to review extensive patient histories for every interaction, consuming valuable time. Northwell Health addresses this challenge by employing mapping tools, propensity analyses, and matching algorithms to align patients with the most appropriate providers. By connecting patients to specialists who best meet their needs, providers can maximize their time and expertise while ensuring better patient outcomes. 2. Leveraging Generative AI for Chart Summarization Generative AI is proving transformative in managing the immense data volumes clinicians face. AI-driven tools help summarize patient records, extracting clinically relevant details tailored to the provider’s specialty. For instance, in a pilot at Northwell Health, AI successfully summarized complex hospitalizations, capturing the critical elements of care transitions. This “just right” approach ensures providers receive actionable insights without unnecessary data overload. Additionally, ambient listening tools are being used to document clinical consultations seamlessly. By automatically summarizing interactions into structured notes, physicians can focus entirely on their patients during visits, improving care quality while reducing after-hours charting. 3. Streamlining Team-Based Care Effective care delivery often involves a multidisciplinary team, including primary physicians, specialists, nurses, and social workers. Coordinating communication across these groups has historically been challenging. Northwell Health is addressing this issue by adopting EMR systems with integrated team chat functionalities, enabling real-time collaboration among care teams. These tools facilitate better care planning and communication, ensuring patients receive coordinated and consistent treatment. Dr. Oppenheim emphasized the importance of not only uniting clinicians in decision-making but also involving patients in discussions. By presenting clear, viable options, providers can enhance patient engagement and shared decision-making. The Path Forward: Balancing Technology with Provider Needs As healthcare continues its digital transformation, connected care technologies must prioritize clinician satisfaction alongside patient outcomes. Tools that simplify workflows, enhance communication, and reduce administrative burdens are crucial for fostering provider buy-in and ensuring the success of health IT initiatives. Northwell Health’s efforts demonstrate how thoughtfully implemented technologies can empower clinicians, strengthen patient relationships, and create a truly connected healthcare experience. Tectonic is here to help your facility plan. Content updated November 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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Box Acquires Alphamoon

Box Acquires Alphamoon

Box Inc. has acquired Alphamoon to enhance its intelligent document processing (IDP) capabilities and its enterprise knowledge management AI platform. Now that Box acquires Alphamoon, it will imr improves IDP. Box Acquires Alphamoon IDP goes beyond traditional optical character recognition (OCR) by applying AI to scanned paper documents and unstructured PDFs. While AI technologies like natural language processing (NLP), workflow automation, and document structure recognition have been around for some time, Alphamoon introduces generative AI (GenAI) into the mix, providing advanced capabilities. According to Rand Wacker, Vice President of AI Product Strategy at Box, the integration of GenAI helps not only with summarizing and extracting content from documents but also with recognizing document structures and categorizing them. GenAI works alongside existing OCR and NLP tools, making the digital conversion of paper documents more accurate. Box Acquires Alphamoon – Not LLM Although Box hasn’t acquired a large language model (LLM) outright, it has gained a toolkit that will enhance its Box AI platform. Box AI already uses retrieval-augmented generation to combine a user’s content with external LLMs, ensuring data security while training Box AI to better recognize and categorize documents. Alphamoon’s technology will further refine this process, enabling administrators to create tools more efficiently within the Box ecosystem. “For example, if Alphamoon’s OCR misreads or misextracts something, the system can adjust that specific part and feed it back into the LLM,” Wacker explained. “This approach is powered by an LLM, but it’s specifically trained to understand the documents it encounters, rather than relying on generic content from the internet.” Previewing an upcoming report from Deep Analysis, founder Alan Pelz-Sharpe shared that a survey of 500 enterprises across various industries, including financial services, manufacturing, healthcare, and government, revealed that 53% of enterprise documents still exist on paper. This highlights the need for Box users to have more precise tools to digitize contracts, letters, invoices, faxes, and other paper-based documents. Alphamoon’s generative AI-driven IDP solution allows for human oversight to ensure that attributes are correctly imported from the original documents. Pelz-Sharpe noted that IDP is challenging, but AI has made significant advancements, especially in handling imperfections like crumpled paper, coffee stains, and handwriting. He added that this acquisition addresses a critical gap for Box, which previously relied on partners for these capabilities. Box Buys Alphamoon – Integration Box plans to integrate Alphamoon’s tools into its platform later this year, with deeper integrations expected next year. These will include no-code app-building capabilities related to another acquisition, Crooze, as well as Box Relay’s forms and document generation tools. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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AI in scams

AIs Role in Scams

How Generative AI is Supporting the Creation of Lures & Scams A Guide for Value Added Resellers Copyright © 2024 Gen Digital Inc. All rights reserved. Avast is part of Gen™. A long, long time ago, I worked for an antivirus company who has since been acquired by Avast.  Knowing many of the people involved in this area of artificial intelligence, I pay attention when they publish a white paper. AI in scams is something we all should be concerned about. I am excited to share it in our Tectonic Insights. Executive Summary The capabilities and global usage of both large language models (LLMs) and generative AI are rapidly increasing. While these tools offer significant benefits to the general public and businesses, they also pose potential risks for misuse by malicious actors, including the misuse of tools like OpenAI’s ChatGPT and other GPTs. This document explores how the ChatGPT brand is exploited for lures, scams, and other social engineering threats. Generative AI is expected to play a crucial role in the cyber threat world challenges, particularly in creating highly believable, multilingual texts for phishing and scams. These advancements provide more opportunities for sophisticated social engineering by even less sophisticated scammers than ever before. Conversely, we believe generative AI will not drastically change the landscape of malware generation in the near term. Despite numerous proofs of concept, the complexity of generative AI methods still makes traditional, simpler methods more practical for malware creation. In short, the good may not outweigh the bad – just yet. Recognizing the value of generative AI for legitimate purposes is important. AI-based security and assistant tools with various levels of maturity and specialization are already emerging in the market. As these tools evolve and become more widely available, substantial improvements in their capabilities are anticipated. AI-Generated Lures and Scams AI-generated lures and scams are increasingly prevalent. Cybercriminals use AI to create lures and conduct phishing attempts and scams through various texts—emails, social media content, e-shop reviews, SMS scams, and more. AI improves the credibility of social scams by producing trustworthy, authentic texts, eliminating traditional phishing red flags like broken language and awkward addressing. These advanced threats have exploited societal issues and initiatives, including cryptocurrencies, Covid-19, and the war in Ukraine. The popularity of ChatGPT among hackers stems more from its widespread recognition than its AI capabilities, making it a prime target for investigation by attackers. How is Generative AI Supporting the Creation of Lures and Scams? Generative AI, particularly ChatGPT, enhances the language used in scams, enabling cybercriminals to create more advanced texts than they could otherwise. AI can correct grammatical errors, provide multilingual content, and generate multiple text variations to improve believability. For sophisticated phishing attacks, attackers must integrate the AI-generated text into credible templates. They can purchase functional, well-designed phishing kits or use web archiving tools to replicate legitimate websites, altering URLs to phish victims. Currently, attackers need to manually build some aspects of their attempts. ChatGPT is not yet an “out-of-the-box” solution for advanced malware creation. However, the emergence of multi-type models, combining outputs like images, audio, and video, will enhance the capabilities of generative AI for creating believable phishing and scam campaigns. Malvertising Malvertising, or “malicious advertising,” involves disseminating malware through online ads. Cybercriminals exploit the widespread reach and interactive nature of digital ads to distribute harmful content. Instances have been observed where ChatGPT’s name is used in malicious vectors on platforms like Facebook, leading users to fraudulent investment portals. Users who provide personal information become vulnerable to identity theft, financial fraud, account takeovers, and further scams. The collected data is often sold on the dark web, contributing to the broader cybercrime ecosystem. Recognizing and mitigating these deceptive tactics is crucial. YouTube Scams YouTube, one of the world’s most popular platforms, is not immune to cybercrime. Fake videos featuring prominent figures are used to trick users into harmful actions. This strategy, known as the “Appeal to Authority,” exploits trust and credibility to phish personal details or coerce victims into sending money. For example, videos featuring Elon Musk discussing OpenAI have been modified to scam victims. A QR code displayed in the video redirects users to a scam page, often a cryptocurrency scam or phishing attempt. As AI models like Midjourney and DALL-E mature, the use of fake images, videos, and audio is expected to increase, enhancing the credibility of these scams. Typosquatting Typosquatting involves minor changes in URLs to redirect users to different websites, potentially leading to phishing attacks or the installation of malicious applications. An example is an Android app named “Open Chat GBT: AI Chat Bot,” where a subtle URL alteration can deceive users into downloading harmful software. Browser Extensions The popularity of ChatGPT has led to the emergence of numerous browser extensions. While many are legitimate, others are malicious, designed to lure victims. Attackers create extensions with names resembling ChatGPT to deceive users into downloading harmful software, such as adware or spyware. These extensions can also subscribe users to services that periodically charge fees, known as fleeceware. For instance, a malicious extension mimicking “ChatGPT for Google” was reported by Guardio. This extension stole Facebook sessions and cookies but was removed from the Chrome Web Store after being reported. Installers and Cracks Malicious installers often mimic legitimate tools, tricking users into installing malware. These installers promise to install ChatGPT but instead deploy malware like NodeStealer, which steals passwords and browser cookies. Cracked or unofficial software versions pose similar risks, hiding malware that can steal personal information or take control of computers. This particular method of installing malware has been around for decades. However the usage of ChatGPT and other free to download tools has given it a resurrection. Fake Updates Fake updates are a common tactic where users are prompted to update their browser to access content. Campaigns like SocGholish use ChatGPT-related articles to lure users into downloading remote access trojans (RATs), giving attackers control over infected devices. These pages are often hosted on vulnerable WordPress sites or sites with

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State of AI

State of AI

With the Dreamforce conference just a few weeks away, AI is set to be a central theme once again. This week, Salesforce offered a preview of what to expect in September with the release of its “Trends in AI for CRM” report. This report consolidates findings from several Salesforce research studies conducted from February last year to April this year. The report’s executive summary highlights four key insights: The Fear of Missing Out (FOMO) An intriguing statistic from Salesforce’s “State of Data and Analytics” report reveals that 77% of business leaders feel a fear of missing out on generative AI. This concern is particularly pronounced among marketers (88%), followed by sales executives (78%) and customer service professionals (73%). Given the continued hype around generative AI, these numbers are likely still relevant or even higher as of July 2024. As Salesforce AI CEO Clara Shih puts it: “The majority of business executives fear they’re missing out on AI’s benefits, and it’s a well-founded concern. Today’s technology world is reminiscent of 1998 for the Internet—full of opportunities but also hype.” Shih adds: “How do we separate the signal from the noise and identify high-impact enterprise use cases?” The Quest for ROI and Value The surge of hype around generative AI over the past 18 months has led to high expectations. While Salesforce has been more responsible in managing user expectations, many executives view generative AI as a cure-all. However, this perspective can be problematic, as “silver bullets” often miss their mark. Recent tech sector developments reflect a shift toward a longer-term view of AI’s impact. Meta’s share price fell when Mark Zuckerberg emphasized AI as a multi-year project, and Alphabet’s Sundar Pichai faced tough questions from Wall Street about the need for continued investment. State of AI Shih notes a growing impatience with the time required to realize AI’s value: “It’s been over 18 months since ChatGPT sparked excitement about AI in business. Many companies are still grappling with building or buying solutions that are not overly siloed and can be customized. The challenge is finding a balance between quick implementation and configurability.” She adds: “The initial belief was that companies could just integrate ChatGPT and see instant transformation. However, there are security risks and practical challenges. For LLMs to be effective, they need contextual data about users and customers.” Conclusion: A Return to the Future Shih likens the current AI landscape to the late 90s Internet boom, noting: “It’s similar to the late 90s when people questioned if the Internet was overhyped. While some investments will not pan out, the transformative potential of successful use cases is enormous. Just as with the Internet, discovering the truly valuable applications of AI may require experimentation and time. We are very much in the 1998 moment for AI now.” Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI for Consumers and Retailers

AI for Consumers and Retailers

Before generative AI became mainstream, tech-savvy retailers had long been leveraging transformative technologies to automate tasks and understand consumer behavior. Insights from consumer and future trends, along with predictive analytics, have long guided retailers in improving customer experiences and enhancing operational efficiency. AI for Consumers and Retailers improved customer experiences. While AI is currently used for personalized recommendations and online customer support, many consumers still harbor distrust towards AI. Salesforce is addressing this concern by promoting trustworthy AI with human oversight and implementing powerful controls that focus on mitigating high-risk AI outcomes. This approach is crucial as many knowledge workers fear losing control over AI. Although people trust AI to handle significant portions of their work, they believe that increased human oversight would bolster their confidence in AI. Building this trust is a challenge retailers must overcome to fully harness AI’s potential as a reliable assistant. So, where does the retail industry stand with AI, and how can retailers build consumer trust while developing AI responsibly? AI for Consumers and Retailers Recent research from Salesforce and the Retail AI Council highlights how AI is reshaping consumer behavior and retailer interactions. AI is now integral to providing personalized deals, suggesting tailored products, and enhancing customer service through chatbots. Retailers are increasingly embedding generative AI into their business operations. A significant majority (93%) of retailers report using generative AI for personalization, enabling customers to find products and make purchases faster through natural language interactions on digital storefronts and messaging apps. For instance, a customer might tell a retailer’s AI assistant about their camping needs, and based on location, preferences, and past purchases, the AI can recommend a suitable tent and provide a direct link for checkout and store collection. As of early 2024, 92% of retailers’ investments were directed towards AI technology. While AI is not new to retail, with 59% of merchants already using it for product recommendations and 55% utilizing digital assistants for online purchases, its applications continue to expand. From demand forecasting to customer sentiment analysis, AI enhances consumer experiences by predicting preferences and optimizing inventory levels, thereby reducing markdowns and improving efficiency. Barriers and Ethical Considerations Despite its promise, integrating generative AI in retail faces significant challenges, particularly regarding bias in AI outputs. The need for clear ethical guidelines in AI use within retail is pressing, underscoring the gap between adoption rates and ethical stewardship. Strategies that emphasize transparency and accountability are vital for fostering responsible AI innovation. Half of the surveyed retailers indicated they could fully comply with stringent data security standards and privacy regulations, demonstrating the industry’s commitment to protecting consumer data amidst evolving regulatory landscapes. Retailers are increasingly aware of the risks associated with AI integration. Concerns about bias top the list, with half of the respondents worried about prejudiced AI outcomes. Additionally, issues like hallucinations (38%) and toxicity (35%) linked to generative AI implementation highlight the need for robust mitigation strategies. A majority (62%) of retailers have established guidelines to address transparency, data security, and privacy concerns related to the ethical deployment of generative AI. These guidelines ensure responsible AI use, emphasizing trustworthy and unbiased outputs that adhere to ethical standards in the retail sector. These insights reveal a dual imperative for retailers: leveraging AI technologies to enhance operational efficiency and customer experiences while maintaining stringent ethical standards and mitigating risks. Consumer Perceptions and the Future of AI in Retail As AI continues to redefine retail, balancing ethical considerations with technological advancements is essential. To combat consumer skepticism, companies should focus on transparent communication about AI usage and emphasize that humans, not technology, are ultimately in control. Whether aiming for top-line growth or bottom-line efficiency, AI is a crucial addition to a retailer’s technology stack. However, to fully embrace AI, retailers must take consumers on the journey and earn their trust. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Need AI Training

Need AI Training

Workers in office jobs are missing out on essential AI training, crucial for adapting to the evolving labor market, according to a report from software giant Salesforce. Released Tuesday, the report is based on a series of anonymous surveys and reveals that approximately 70% of desk workers have not received training in generative AI. Furthermore, only 21% of respondents said their companies have clearly defined policies regarding approved AI tools and their usage. Additionally, 62% believe they lack the necessary skills to use the technology effectively. Despite this lack of formal training and clear policies, many workers are taking the initiative to use AI tools independently. The report highlights that “workers aren’t waiting for permission to use AI,” with 55% of survey respondents using unapproved tools and 40% using AI tools explicitly banned by their employers. The report emphasizes the need for clear protocols and approved tools to address data security and ethical concerns. Clara Shih, CEO of Salesforce AI, stresses the importance of continuous learning in response to the rapid changes in AI technology, stating, “The unprecedented pace of change in AI requires companies to upskill their entire workforce. This is not a ‘one-and-done’ exercise, but rather a continuous cycle of learning as AI evolves.” However, some companies are stepping up to meet this challenge. While less than half of U.S. companies had initiated AI training for their workers by April, according to a LinkedIn study, businesses like JPMorgan Chase, Amazon, PricewaterhouseCoopers, AT&T, Verizon, Moderna, and General Motors are launching AI literacy initiatives for their employees and the broader workforce. For instance, Amazon aims to train 2 million people globally in generative artificial intelligence by 2025. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI Data Cloud and Integration

AI Data Cloud and Integration

The enterprise has transitioned from merely speculating about artificial intelligence to actively implementing it. In doing so, companies must determine the optimal combination of ancillary technologies that, when strategically paired with AI, can drive relevant use cases and business outcomes. With AI Data Cloud and Integration, your data-driven decisions happen in real-time. Salesforce Inc. is leveraging a powerful trio — its Data Cloud, automation, and AI — to deliver what it considers transformative outcomes for organizations. “AI has such wonderful capability today from predictive to generative, [but] it’s not new to Salesforce,” said Param Kahlon, executive vice president and general manager at Salesforce. “Salesforce has been doing predictive AI for almost 10 years now. But what is great is that generative AI now gives the ability to process these large language models on large amounts of unstructured, semi-structured content to generate great content that can be used by salespeople to send relevant emails and marketing people to create personalized landing pages.” Kahlon spoke with theCUBE Research Senior Analyst George Gilbert during a recent “The Road to Intelligent Data Apps” podcast series. They discussed how Salesforce is revolutionizing business operations in the digital age by harnessing AI-driven insights, contextualizing data with the company’s Data Cloud, and enabling real-time actions. Gen AI and Data Cloud for Contextualization In today’s business environment, intelligence is the cornerstone of success. Salesforce’s AI platform empowers companies with predictive and generative AI capabilities, enabling them to make insightful decisions and craft personalized experiences for their customers. Businesses can now process vast amounts of unstructured data and generate compelling content. “For this AI to be meaningful and for companies to harness the full value of AI, you want to make sure that you’re grounding the data that’s being used to generate those predictions with some things that are relevant to the current business process, to the current transaction, to the current context of interaction you’re happening with the customer,” Kahlon said. Salesforce’s Data Cloud acts as the AI foundation, enriching existing data models with relevant contextual data tailored to the specific needs of each business and their interactions with customers. “When we talk to our large Salesforce customers, they all tell us that AI is really important for them,” Kahlon said. “That is something that they want to drive, but they’re also saying that the data for them is spread out across the enterprise. Some of them tell us that they have more than 900 different business systems in which data is stored, and they want the ability to bring that data together in a seamless way so it can be processed by AI through Data Cloud.” Automation and Integration for Real-Time Action The combination of AI and Data Cloud generates actionable insights, but these insights alone aren’t enough. Businesses need to act swiftly on these predictions, driving real-time actions to capitalize on opportunities. This is where integration and automation come into play, according to Kahlon. “[Customers are] essentially telling us that data is spread across the enterprise and they want the data in real time to be available to customers,” he said. “With MuleSoft and Salesforce integration capabilities, we’ve focused on the real-time nature of making sure that you can take real-time business transactions in the context of the process that is happening, and that’s what’s differentiated in our approach to making sure that we can collect the data in real time and make actions happen in real time.” Integration is the glue that brings together data from various sources, allowing AI to derive meaningful insights. Salesforce’s integration capabilities, powered by MuleSoft, focus on real-time data processing, ensuring that businesses can act on insights as they occur. This low-latency approach enables not only Salesforce applications but also other third-party applications to contribute to the data ecosystem, Kahlon explained. “We’ve got a very large North American airline that has built their entire customer experience, from booking an airline ticket to checking into your flight and ordering special meals for your flight, all of that on an API-based platform — and we’re able to process that scale of transactions,” he said. “As you get into AI, all of that becomes extremely relevant to drive that real-time throughput, and that’s where our customers are finding value in our technology.” When the customer experience is the driver, the experience is always stellar. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Box and Slack AI Integration

Box and Slack AI Integration

Box and Slack Enhance Collaboration with New AI Integration Box, Inc. (NYSE: BOX), the leader in Intelligent Content Cloud solutions, and Slack, a Salesforce company (NYSE: CRM), have announced an expanded partnership that integrates Box AI into Slack, aiming to transform how organizations collaborate. Starting today, customers can leverage unlimited Box AI queries directly within Slack, enabling them to extract critical insights and streamline workflows seamlessly within their existing work environment. Key Features of the Enhanced Integration: Pricing and Availability Unlimited end-user queries in the Box AI for Slack integration are available now for all Slack customers with Box Enterprise Plus plans. The additional integration features are also available immediately for all Box and Slack customers. Customers can add the Box for Slack integration from the Slack App Directory. Quotes from Leadership: Aaron Levie, Co-Founder and CEO of Box, stated, “Enterprises recognize AI’s potential to unlock valuable insights from their content. With thousands of customers already using Box and Slack together, this expanded partnership brings a new level of AI-driven efficiency. Whether working on presentations, contracts, or spreadsheets, you can now leverage Box AI to gain insights directly within Slack.” Denise Dresser, CEO of Slack, added, “Slack’s integration with Box allows companies to intelligently surface insights from critical business content right where their work happens. This partnership exemplifies how Slack can serve as an AI-powered work operating system for the future of work.” Real-World Applications of Box AI in Slack: About Box Box (NYSE: BOX) is the Intelligent Content Cloud, providing a single platform that fuels collaboration, manages the entire content lifecycle, secures critical content, and transforms business workflows with enterprise AI. Founded in 2005, Box simplifies work for leading global organizations, including AstraZeneca, JLL, Morgan Stanley, and Nationwide. Headquartered in Redwood City, CA, Box has offices across the United States, Europe, and Asia. Visit box.com to learn more. For information on how Box supports nonprofits, visit box.org. About Slack Slack is where work happens for millions every day, helping organizations in all industries move faster and achieve their missions. As an AI-powered work operating system, Slack centralizes conversations and collaboration, automates business processes, and delivers trusted generative AI that enhances productivity and drives real outcomes. As a Salesforce company, Slack integrates deeply with Salesforce solutions, bringing rich data and insights directly into the workflow, boosting sales, service, and marketing productivity. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Generative AI Replaces Legacy Systems

Securing AI for Efficiency and Building Customer Trust

As businesses increasingly adopt AI to enhance automation, decision-making, customer support, and growth, they face crucial security and privacy considerations. The Salesforce Platform, with its integrated Einstein Trust Layer, enables organizations to leverage AI securely by ensuring robust data protection, privacy compliance, transparent AI functionality, strict access controls, and detailed audit trails. Why Secure AI Workflows Matter AI technology empowers systems to mimic human-like behaviors, such as learning and problem-solving, through advanced algorithms and large datasets that leverage machine learning. As the volume of data grows, securing sensitive information used in AI systems becomes more challenging. A recent Salesforce study found that 68% of Analytics and IT teams expect data volumes to increase over the next 12 months, underscoring the need for secure AI implementations. AI for Business: Predictive and Generative Models In business, AI depends on trusted data to provide actionable recommendations. Two primary types of AI models support various business functions: Addressing Key LLM Risks Salesforce’s Einstein Trust Layer addresses common risks associated with large language models (LLMs) and offers guidance for secure Generative AI deployment. This includes ensuring data security, managing access, and maintaining transparency and accountability in AI-driven decisions. Leveraging AI to Boost Efficiency Businesses gain a competitive edge with AI by improving efficiency and customer experience through: Four Strategies for Secure AI Implementation To ensure data protection in AI workflows, businesses should consider: The Einstein Trust Layer: Protecting AI-Driven Data The Einstein Trust Layer in Salesforce safeguards generative AI data by providing: Salesforce’s Einstein Trust Layer addresses the security and privacy challenges of adopting AI in business, offering reliable data security, privacy protection, transparent AI operations, and robust access controls. Through this secure approach, businesses can maximize AI benefits while safeguarding customer trust and meeting compliance requirements. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Managing Data Quality in an AI World

Managing Data Quality in an AI World

Each year, Monte Carlo surveys real data professionals about the state of their data quality. This year, we turned our gaze to the shadow of AI—and the message was clear. Managing Data Quality in an AI World is getting harder. Data quality risks are evolving — and data quality management isn’t. Among the 200 data professionals polled about the state of enterprise AI, a staggering 91% said they were actively building AI applications, but two out of three admitted to not completely trusting the data these applications are built on. And “not completely” leaves a lot of room for error in the world of AI. Far from pushing the industry toward better habits and more trustworthy outputs, the introduction of GenAI seems to have exacerbated the scope and severity of data quality problems. The Core Issue Why is this happening, and what can we do about it? 2024 State of Reliable AI Survey The Wakefield Research survey, commissioned by Monte Carlo in April 2024, polled 200 data leaders and professionals. It comes as data teams grapple with the adoption of generative AI. The findings highlight several key statistics that indicate the current state of the AI race and professional sentiment about the technology: While AI is widely expected to be among the most transformative technological advancements of the last decade, these findings suggest a troubling disconnect between data teams and business stakeholders. More importantly, they suggest a risk of downward pressure toward AI initiatives without a clear understanding of the data and infrastructure that power them. Managing Data Quality in an AI World. The State of AI Infrastructure—and the Risks It’s Hiding Even before the advent of GenAI, organizations were dealing with exponentially greater volumes of data than in decades past. Since adopting GenAI programs, 91% of data leaders report that both applications and the number of critical data sources have increased even further, deepening the complexity and scale of their data estates in the process. There’s no clear solution for a successful enterprise AI architecture. Survey results reveal how data teams are approaching AI: As the complexity of AI’s architecture and the data that powers it continues to expand, one perennial problem is expanding with it: data quality issues. The Modern Data Quality Problem While data quality has always been a challenge for data teams, this year’s survey results suggest the introduction of GenAI has exacerbated both the scope and severity of the problem. More than half of respondents reported experiencing a data incident that cost their organization more than $100K. And we didn’t even ask how many they experienced. Previous surveys suggest an average of 67 data incidents per month of varying severity. This is a shocking figure when you consider that 70% of data leaders surveyed also reported that it takes longer than four hours to find a data incident—and at least another four hours to resolve it. Managing Data Quality in an AI World But the real deal breaker is this: even with 91% of teams reporting that their critical data sources are expanding, an alarming 54% of teams surveyed still rely on manual testing or have no initiative in place at all to address data quality in their AI. This anemic approach to data quality will have a demonstrable impact on enterprise AI applications and data products in the coming months—allowing more data incidents to slip through the cracks, multiplying hallucinations, diminishing the safety of outputs, and eroding confidence in both the AI and the companies that build them. Is Your Data AI-Ready? While a lot has certainly changed over the last 12 months, one thing remains absolutely clear: if AI is going to succeed, data quality needs to be front and center. “Data is the lifeblood of all AI — without secure, compliant, and reliable data, enterprise AI initiatives will fail before they get off the ground. The most advanced AI projects will prioritize data reliability at each stage of the model development life cycle, from ingestion in the database to fine-tuning or RAG.” Lior Solomon, VP of Data at Drata, The success of AI depends on the data—and the success of the data depends on your team’s ability to efficiently detect and resolve the data quality issues that impact it. By curating and pairing your own first-party context data with modern data quality management solutions like data observability, your team can mitigate the risks of building fast and deliver reliable business value for your stakeholders at every stage of your AI adventure. What can you do to improve data quality management in your organization? Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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Salesforce Model Tester

Salesforce Model Tester

Salesforce is taking steps to ensure its AI models perform accurately, even with unexpected data. The company recently filed a patent for an “automated testing pipeline for neural network models.” This technology helps developers predict whether their AI models will maintain accuracy when dealing with “unseen queries,” using customer service bots as a primary example. Salesforce Model Tester Typically, developers test their AI models using a subset of the original training data. However, Salesforce notes that this approach may not be ideal for smaller datasets or when real-time data differs significantly from the training set. To address this, Salesforce’s system creates both easy and hard evaluation datasets from real-time customer data. The “hard” datasets contain queries significantly different from the training data, while the “easy” datasets are more similar. The system begins by passing customer data through a “dependency parser,” which filters out specific actions or verbs representing meaningful commands. Then, a pre-trained language model ranks the queries based on their similarity to the training data. A “bag of words” classifier removes queries that are too similar, ensuring the testing data is diverse. These curated datasets are used to evaluate the model’s performance. The pipeline also includes a “human-in-the-loop” feedback mechanism to notify developers when a model isn’t performing well, allowing for adjustments. Salesforce’s primary AI product, Einstein, enables customers to create generative AI experiences using their data. Unlike some companies that focus on building massive AI models, Salesforce aims to empower enterprise clients to develop their own models, according to Bob Rogers, Ph.D., co-founder of BeeKeeperAI and CEO of Oii.ai. This patent could enhance Salesforce’s offerings by ensuring the AI models built under its platform function as intended. “I think Salesforce wants Einstein to generate more leads and faster. And if that’s not happening, it could be a miss for Salesforce,” Rogers said. The patent’s emphasis on improving customer service chatbots suggests Salesforce is focusing on AI-driven customer interactions. This is in line with the company’s recent unveiling of its fully-autonomous Einstein Service Agent, highlighting where Salesforce believes the most traction for Einstein might be. Rogers noted that while creating tools for customers to build their own AI models is challenging, Salesforce’s approach stands out in a market dominated by companies like Google, Microsoft, and OpenAI, which offer ready-to-use AI services. “At the end of the day, most AI utilization is still people saying, ‘solve my problem for me,’” Rogers said. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Impact of Generative AI on Workforce

Impact of Generative AI on Workforce

The Impact of Generative AI on the Future of Work Automation has long been a source of concern and hope for the future of work. Now, generative AI is the latest technology fueling both fear and optimism. AI’s Role in Job Augmentation and Replacement While AI is expected to enhance many jobs, there’s a growing argument that job augmentation for some might lead to job replacement for others. For instance, if AI makes a worker’s tasks ten times easier, the roles created to support that job could become redundant. A June 2023 McKinsey report highlighted that generative AI (GenAI) could automate 60% to 70% of employee workloads. In fact, AI has already begun replacing jobs, contributing to nearly 4,000 job cuts in May 2023 alone, according to Challenger, Gray & Christmas Inc. OpenAI, the creator of ChatGPT, estimates that 80% of the U.S. workforce could see at least 10% of their jobs impacted by large language models (LLMs). Examples of AI Job Replacement One notable example involves a writer at a tech startup who was let go without explanation, only to later discover references to her as “Olivia/ChatGPT” in internal communications. Managers had discussed how ChatGPT was a cheaper alternative to employing a writer. This scenario, while not officially confirmed, strongly suggested that AI had replaced her role. The Writers Guild of America also went on strike, seeking not only higher wages and more residuals from streaming platforms but also more regulation of AI. Research from the Frank Hawkins Kenan Institute of Private Enterprise indicates that GenAI might disproportionately affect women, with 79% of working women holding positions susceptible to automation compared to 58% of working men. Unlike past automation that typically targeted repetitive tasks, GenAI is different—it automates creative work such as writing, coding, and even music production. For example, Paul McCartney used AI to partially generate his late bandmate John Lennon’s voice to create a posthumous Beatles song. In this case, AI enhanced creativity, but the broader implications could be more complex. Other Impacts of AI on Jobs AI’s impact on jobs goes beyond replacement. Human-machine collaboration presents a more positive angle, where AI helps improve the work experience by automating repetitive tasks. This could lead to a rise in AI-related jobs and a growing demand for AI skills. AI systems require significant human feedback, particularly in training processes like reinforcement learning, where models are fine-tuned based on human input. A May 2023 paper also warned about the risk of “model collapse,” where LLMs deteriorate without continuous human data. However, there’s also the risk that AI collaboration could hinder productivity. For example, generative AI might produce an overabundance of low-quality content, forcing editors to spend more time refining it, which could deprioritize more original work. Jobs Most Affected by AI AI Legislation and Regulation Despite the rapid advancement of AI, comprehensive federal regulation in the U.S. remains elusive. However, several states have introduced or passed AI-focused laws, and New York City has enacted regulations for AI in recruitment. On the global stage, the European Union has introduced the AI Act, setting a common legal framework for AI. Meanwhile, U.S. leaders, including Senate Majority Leader Chuck Schumer, have begun outlining plans for AI regulation, emphasizing the need to protect workers, national security, and intellectual property. In October 2023, President Joe Biden signed an executive order on AI, aiming to protect consumer privacy, support workers, and advance equity and civil rights in the justice system. AI regulation is becoming increasingly urgent, and it’s a question of when, not if, comprehensive laws will be enacted. As AI continues to evolve, its impact on the workforce will be profound and multifaceted, requiring careful consideration and regulation to ensure it benefits society as a whole. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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