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agentforce testing center
08Dec

Agentforce Testing Center

Salesforce has launched the Agentforce Testing Center, a groundbreaking tool for testing, deploying, and managing AI agents at scale. This innovative platform uses synthetically generated data to ensure AI agents perform accurate actions and deliver precise responses while providing comprehensive monitoring of usage and feedback. A New Era of AI Testing The Agentforce Testing Center operates within Salesforce’s enterprise-grade platform, integrated seamlessly with Data Cloud. It introduces no-code tools that allow organizations to test AI agents securely in sandbox environments, ensuring minimal risk to live production systems. By enabling robust testing and optimization, this tool helps businesses confidently deploy AI agents tailored to their needs. According to Adam Evans, EVP and GM for the Salesforce AI Platform, the Agentforce Testing Center is a critical tool for modern enterprises: “Agentforce is helping businesses create a limitless workforce. CIOs need tools for testing and monitoring agentic systems. This offering marks the next evolution of lifecycle management, enabling businesses to deploy trusted AI agents in a secure, repeatable way.” Key Capabilities of the Agentforce Testing Center Industry Implications Keith Kirkpatrick, Research Director at The Futurum Group, emphasized the importance of this offering: “To build trust in AI, businesses need robust tools to pressure test autonomous agents. Salesforce’s Testing Center delivers this capability, ensuring AI agents can reason, retrieve data, and adapt effectively.” Enhancing AI Agent Strategies The Agentforce Testing Center’s integration with Salesforce tools like DevOps Center and Change Sets further simplifies deployment workflows. Features like User Acceptance Testing (UAT) and real-time alerts in the Digital Wallet give teams deeper control and visibility into AI performance, ensuring business readiness before live deployment. As Salesforce continues to invest in AI innovation, including recent acquisitions to enhance Data Cloud security, the Agentforce Testing Center positions the CRM leader as a pioneer in responsible and scalable AI deployment. This offering exemplifies how businesses can create a “limitless workforce” while maintaining trust, transparency, and efficiency in AI operations. 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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December 8, 2024in Agentforce Platform, Data, Enterprise, Salesforce
rise of digital workers
04Dec

Rise of Digital Workers

The Rise of Digital Workers: Unlocking a New Era of Opportunity Over the past two years, advancements in artificial intelligence have sparked a revolution in how humans work, live, and connect. While impressive generative AI models have garnered significant attention, a new paradigm of autonomous AI agents is emerging, promising transformative changes to industries and societies alike. Unlike traditional “predictive AI,” which analyzes data for recommendations, and “generative AI,” which creates content based on learned patterns, autonomous AI agents go a step further. These agents operate independently, executing tasks, making decisions, and even negotiating with other agents. This evolution introduces an intelligent digital workforce capable of scaling operations, reducing costs, and enhancing productivity. Consider a large retailer during the holiday season. Instead of relying on human workers or pre-programmed software to address customer inquiries or update inventory, autonomous agents can seamlessly manage customer interactions, monitor stock levels, reorder items, and coordinate shipping—all without human intervention. This level of automation represents a groundbreaking shift, enabling businesses to operate on an unprecedented scale. Expanding the Reach of Digital Labor Autonomous AI agents are breaking traditional barriers of human availability and physical constraints, enabling businesses to scale globally and more efficiently. These digital workers are not limited by geography, opening opportunities previously restricted to specific locations. However, this shift comes with challenges. Ensuring trust, accountability, and transparency in AI systems is critical. Equally important is investing in human-centric skills such as creativity, critical thinking, and adaptability, which remain uniquely human. Sustainability is another concern, as AI-driven technologies place increasing demands on energy and resources. By addressing these issues, societies can unlock the full potential of digital labor while safeguarding the planet and human values. Transforming Everyday Lives Beyond businesses, autonomous agents are poised to transform personal lives. Personalized agents can act as tutors for students, guiding them through their learning journeys. For individuals, these agents can manage everyday tasks, from scheduling appointments to coordinating complex logistics. In healthcare, AI agents are already alleviating administrative burdens on providers. For example, intelligent agents can handle patient communications, monitor progress, and schedule follow-ups, freeing doctors and nurses to focus on complex cases. Such innovations hold the potential to revolutionize patient care and improve outcomes across the board. Navigating Disruption and Change Like any transformative technology, the rise of autonomous agents will bring disruptions. Some industries will struggle to adapt, and jobs will inevitably evolve—or, in some cases, disappear. History shows, however, that technological revolutions often create far more opportunities than they displace. For example, the U.S. workforce grew by over 100 million jobs between 1950 and 2020, many in industries that didn’t exist before. The key lies in preparing workers for new roles through education and training. Autonomous agents are essential in addressing global challenges such as labor shortages and stagnant productivity growth. They amplify human capabilities, driving innovation and boosting economic output. For example, in the third quarter of 2024, U.S. productivity rose by 2.2%, fueled in part by AI advancements. Driving Innovation and Collaboration AI agents are also fostering innovation, sparking the creation of new companies and industries. More than 5,000 AI-focused startups have emerged in the past decade in the U.S. alone. This trend mirrors the technological revolutions driven by past innovations like microchips, the internet, and smartphones. However, effectively harnessing agentic AI requires collaboration among governments, businesses, nonprofits, and academia. Initiatives like the G7’s framework for AI accountability and the Bletchley Declaration emphasize transparency, safety, and data privacy, offering critical guardrails as AI adoption accelerates. A Vision for the Future Autonomous agents represent a powerful force for change, offering unprecedented opportunities for businesses and individuals alike. By leveraging these technologies responsibly and investing in human potential, societies can ensure a future of abundance and progress. As Marc Benioff, CEO of Salesforce, emphasizes, “AI has the potential to elevate every company, fuel economic growth, uplift communities, and lead to a future of abundance. If trust is our north star, agents will empower us to make a meaningful impact at an unprecedented scale.” 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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December 4, 2024in Agentforce Platform, Artificial Intelligence, Data, Generative AI, Salesforce, Technology
AI-Checking Agents
02Dec

AI-Checking Agents

Introducing AI-Checking Agents: The Next Frontier in Software Quality Assurance The software industry has continually evolved in its pursuit of better quality assurance (QA) methods. While traditional approaches like unit testing and manual QA offer foundational tools, they often fail to meet the growing complexity of modern software. Automated testing and DevOps practices have helped, but these methods are still time-intensive, costly, and limited in scope. AI-Checking Agents. Enter AI-Checking Agents — an innovative solution leveraging generative AI to revolutionize software testing and quality assurance. These agents promise unprecedented coverage, speed, and efficiency, addressing the challenges of today’s demanding software ecosystems. Why AI-Checking Agents? Traditional QA methods fall short in delivering exhaustive coverage for the diverse behaviors and interactions of modern software. AI-Checking Agents close this gap by introducing: Synthetic Users: Revolutionizing User Experience (UX) Testing One of the most groundbreaking features of AI-Checking Agents is the ability to create synthetic users. These AI-driven personas simulate real-world user interactions, offering a novel approach to UX analysis. Key Features of Synthetic Users: UX Insights Delivered by Synthetic Users: Benefits of AI-Checking Agents in QA Integrating AI-Checking Agents with Existing QA Practices AI-Checking Agents are not a replacement for traditional methods but a powerful complement to existing practices: Transforming the Development Process AI-Checking Agents not only streamline QA but also enhance the overall development process: The Future of Quality Assurance AI-Checking Agents represent a paradigm shift in software testing, blending the best of AI-driven insights with traditional QA practices. By integrating these agents into their workflows, development teams can achieve: In a world of ever-evolving software demands, AI-Checking Agents are the key to achieving unparalleled speed, depth, and precision in quality assurance. 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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December 2, 2024in Generative AI
26Nov

Salesforce Agents are Transforming Internal Workflows

How Salesforce Agents are Transforming Internal Workflows Salesforce CIO and Executive Vice President Juan Perez, with three decades of IT leadership experience, is leading the charge in deploying generative AI solutions like Agentforce within Salesforce. Perez’s approach reflects lessons learned during his tenure at UPS, where he oversaw IT operations for a global enterprise. His strategies emphasize scalability, data strategy, and modernization to support growth, with AI now playing a pivotal role. UPS Lessons Applied to Salesforce Perez draws on his UPS experience in managing IT at scale to navigate Salesforce’s needs as a growing enterprise. At UPS, he managed a complex, global IT organization supporting diverse operations, from running an airline to ensuring timely package delivery. Similarly, Salesforce’s IT strategy prioritizes scalable solutions, robust data strategies, and AI integration. “Salesforce intelligently realized the importance of leveraging its own technologies, including AI, to modernize and support growth,” Perez explains. Generative AI’s Transformative Potential Perez views generative AI (GenAI) as a transformative force on par with the internet’s emergence in the 1990s. By reducing the time spent on data analysis and decision-making, AI enables teams to focus on actions that improve productivity and customer service. While GenAI isn’t a solution in itself, Perez sees it as an enabler that amplifies human efforts. Evaluating and Integrating AI in Salesforce’s Stack Salesforce adopts a rigorous, multi-step approach to evaluate new technologies, including large language models (LLMs) and generative AI tools. Perez outlines a “filtering mechanism” for implementation: This structured approach ensures AI investments are both impactful and sustainable. Measuring AI’s ROI To quantify the impact of AI, Salesforce evaluates metrics like lines of code generated using AI tools and time saved through automation. In one example, approximately 26% of production-ready code in a recent deployment was AI-generated. This efficiency is factored into planning and budgeting, allowing resources to be reallocated to other initiatives. Mitigating “Shadow AI” Risks Perez warns against “shadow AI,” where decentralized or unmanaged AI implementations can lead to security, data privacy, and investment inefficiencies. He stresses the need for visibility and governance to prevent these risks. To address this, Salesforce has established an AI Council that is evolving into an Agentforce Center of Excellence. This body ensures responsible development, aligns projects with organizational goals, and maintains oversight of AI implementations across the enterprise. Responsible and Scalable AI Adoption Salesforce’s commitment to using its own products extends to Agentforce, a generative AI suite designed to streamline internal workflows. With a focus on governance, scalability, and measurable impact, Salesforce sets a benchmark for AI adoption. As Perez explains, “We ensure our AI solutions are safe, effective, and capable of driving significant value while remaining aligned with our strategic goals.” By combining rigorous evaluation, measurable outcomes, and proactive governance, Salesforce demonstrates how AI can transform workflows while mitigating risks. 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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November 26, 2024in AI Tools, Data, Enterprise, Generative AI, Salesforce, Technology
human centered ai
23Nov

Human-Centered AI

Be the change you want to see in the artificial intelligence world. Or scramble to catch up. Hope Is Not Lost for Human-Centered AIHow designers can lead the charge in creating AI that truly benefits humanity. The rapid proliferation of Artificial Intelligence (AI) brings with it a range of ethical and societal concerns. From inherent biases in datasets to fears of widespread job displacement, these challenges often feel like inevitable trade-offs as AI becomes deeply embedded in our lives. However, hope remains. Human-centered AI—designed to be fair, transparent, and genuinely beneficial—is not only possible but achievable when crafted with intentionality. For UX professionals, this is an opportunity to drive the creation of AI systems that empower rather than overshadow human capabilities. A Quick Note on AI Literacy To make meaningful contributions to AI product development, designers need a foundational understanding of how AI works. While a PhD in machine learning isn’t necessary, being an informed practitioner is essential. Think of learning about AI like learning to invest. At first, it seems daunting—what even is an ETF? But with time, the jargon and processes become familiar. Similarly, while you don’t need to be a machine-learning expert to work with AI, understanding its basics is critical. AI refers broadly to a computer’s ability to mimic human thought, while machine learning (ML)—a subset of AI—enables systems to learn from data. Unlike traditional programming, where explicit instructions are coded line by line, ML models identify patterns within training datasets. These models then function as “black boxes,” generating outputs based on user inputs—though the inner workings are often opaque. Understanding these fundamentals empowers designers to bridge the gap between AI’s technical potential and its real-world application. Design-Led AI Ideally, designers are involved from the very beginning of AI product development—during the discovery phase. Here, we evaluate whether AI is the right solution for a given problem, ensuring user needs drive decisions rather than the allure of flashy tech. Key questions to ground AI solutions in user needs include: Basic AI literacy allows designers to make informed judgments and collaborate effectively with engineers. Engaging early ensures that AI solutions are designed to adapt to users—not the other way around. But what happens when design isn’t brought in until after AI decisions have been made? Design-Guarded AI Even when AI is a foregone conclusion, designers can still shape outcomes by focusing on the two areas where users interact directly with AI: inputs and outputs. Input Design Whether inputs involve transaction data, images, or text prompts, the method of collection must be intuitive and user-friendly. Established design principles, such as affordances, help ensure clarity and simplicity. For example: Frequent user testing ensures input methods align with real workflows and pain points. The result? Streamlined, user-centric experiences that reduce friction and save time. Output Design Designing outputs requires a focus on transparency and mitigating automation bias—the tendency to over-rely on AI. Users must understand that AI is fallible. For instance: AI should act as a collaborator, not an authority. Outputs must empower users to make informed choices while supporting their next steps within a seamless workflow. Ethics Must Take Center Stage No discussion of human-centered AI is complete without addressing ethics. Designers must champion transparency, inclusivity, and fairness throughout the product lifecycle. Questions around bias, privacy, and unintended consequences must be raised early and revisited often. While ethical considerations may sometimes conflict with short-term business goals, prioritizing them is essential for building AI that serves humanity in the long term. These conversations won’t always be easy—but they are necessary. As designers, we have the tools and responsibility to ensure AI remains a force for good. By advocating for human-centered design principles, we can help shape an AI-powered future that enhances human potential rather than undermining it. 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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November 23, 2024in Artificial Intelligence, Data, Generative AI
AI platform for automated task management
18Nov

AI platform for automated task management

Salesforce Doubles Down on AI Innovation with Agentforce Salesforce, renowned for its CRM software used by over 150,000 businesses, including Amazon and Walmart, continues to push the boundaries of innovation. Beyond its flagship CRM, Salesforce also owns Slack, the popular workplace communication app. Now, the company is taking its AI capabilities to the next level with Agentforce—a platform that empowers businesses to build and deploy AI-powered digital agents for automating tasks such as creating sales reports and summarizing Slack conversations. What Problem Does Agentforce Solve? Salesforce has been leveraging AI for years, starting with the launch of Einstein in 2016. Einstein’s initial capabilities were limited to basic, scriptable tasks. However, the rise of generative AI created an opportunity to tackle more complex challenges, enabling tools to make smarter decisions and interpret natural language. This evolution led to a series of innovations—Einstein GPT, Einstein Copilot, and now Agentforce—a flexible platform offering prebuilt and customizable agents designed to meet diverse business needs. “Our customers wanted more. Some wanted to tweak the agents we offer, while others wanted to create their own,” said Tyler Carlson, Salesforce’s VP of Business Development. The Technology Behind Agentforce Agentforce is powered by Salesforce’s Atlas Reasoning Engine, developed in-house to drive smarter decision-making. The platform integrates with AI models from leading providers like OpenAI, Anthropic, Amazon, and Google, offering businesses a variety of tools to choose from. Slack, which Salesforce acquired in 2021, plays a pivotal role as a testing ground for these AI agents. Currently in beta, Agentforce’s Slack integration allows businesses to implement automations directly where employees work, enhancing usability. “Slack makes these tools easy to use and accessible,” Carlson noted. How Agentforce Stands Out Customizing AI for Business Needs With tools like Agentbuilder, businesses can create AI agents tailored to specific tasks. For instance, an agent could prioritize and sort incoming emails, respond to HR inquiries, or handle customer support using internal data. One standout example is Salesforce’s partnership with Workday to develop an AI-powered service agent for employee questions. Driving Results and Adoption Salesforce has already seen promising results from early trials, with Agentforce resolving 90% of customer inquiries autonomously. The company aims to expand adoption and functionality, allowing these agents to handle even larger workloads. “We’re building a bigger ecosystem of partners and skills,” Carlson emphasized. “By next year, we want Agentforce to be a must-have for businesses.” With Agentforce, Salesforce continues to cement its role as a leader in AI innovation, helping businesses work smarter, faster, and more effectively. 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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November 18, 2024in Agentforce Platform, Data, Generative AI, Google, Salesforce, Technology
13Nov

AI’s Impact on Future Information Ecosystems

AI’s Impact on Future Information Ecosystems The proliferation of generative AI technology has ignited a renewed focus within the media industry on how to strategically adapt to its capabilities. Media professionals are now confronted with crucial questions: What are the most effective ways to leverage this technology for efficiency in news production and to enhance audience experiences? Conversely, what threats do these technological advancements pose? Is legacy media on the brink of yet another wave of disintermediation from its audiences? Additionally, how does the evolution of technology impact journalism ethics? AI’s Impact on Future Information Ecosystems. In response to these challenges, the Open Society Foundations (OSF) launched the AI in Journalism Futures project earlier this year. The first phase of this ambitious initiative involved an open call for participants to develop future-oriented scenarios that explore the potential driving forces and implications of AI within the broader media ecosystem. The project sought to answer questions about what might transpire among various stakeholders in 5, 10, or 15 years. As highlighted by Nick Diakopoulos, scenarios are a valuable method for capturing a diverse range of perspectives on complex issues. While predicting the future is not the goal, understanding a variety of plausible alternatives can significantly inform current strategic thinking. Ultimately, more than 800 individuals from approximately 70 countries contributed short scenarios for analysis. The AI in Journalism Futures project subsequently utilized these scenarios as a foundation for a workshop, which refined the ideas outlined in their report. Diakopoulos emphasizes the importance of examining this broad set of initial scenarios, which OSF graciously provided in anonymized form. This analysis specifically explores (1) the various types of impacts identified within the scenarios, (2) the associated timeframes for these impacts—whether they are short, medium, or long-term, and (3) the global differences in focus across regions, highlighting how different parts of the world emphasized distinct types of impacts. While many additional questions could be explored regarding this data—such as the drivers of impacts, final outcomes, severity, stakeholders involved, or technical capabilities emphasized—this analysis focuses primarily on impacts. Refining the Data The initial pool of 872 scenarios underwent a rigorous process of cleaning, filtering, transformation, and verification before analysis. Firstly, scenarios shorter than 50 words were excluded from consideration, resulting in 852 scenarios for analysis. Additionally, 14 scenarios that were not written in English were translated using Google Sheets. To enable geographic and temporal analysis, the country of origin for each scenario writer was mapped to their respective continents, and the free-text “timeframe” field was converted into numerical representations of years. Next, impacts were extracted from each scenario using an LLM (GPT-4 in this case). The prompts for the LLM were refined through iteration, with a clear definition established for what constitutes an “impact.” Diakopoulos defined an impact as “a significant effect, consequence, or outcome that an action, event, or other factor has in the scenario.” This definition encompasses not only the ultimate state of a scenario but also intermediate outcomes. The LLM was instructed to extract distinct impacts, with each impact represented by a one-sentence description and a short label. For instance, one impact could be described as, “The proliferation of flawed AI systems leads to a compromised information ecosystem, causing a general doubt in the reliability of all information,” labeled as “Compromised Information Ecosystem.” To ensure the accuracy of this extraction process, a random sample of five scenarios was manually reviewed to validate the extracted impacts against the established definition. All extracted impacts passed the checks, leading to confidence in scaling the analysis across the entire dataset. This process resulted in the identification of 3,445 impacts from the 852 scenarios. AI’s Impact on Future Information Ecosystems A typology of impact types was developed based on the 3,445 impact descriptions, utilizing a novel method for qualitative thematic analysis from a Stanford University study. This approach clusters input texts, synthesizes concepts that reflect abstract connections, and produces scoring definitions to assess the relevance of each original text. For example, a concept like “AI Personalization” might be defined by the question, “Does the text discuss how AI personalizes content or enhances user engagement?” Each impact description was then scored against these concepts to tabulate occurrence frequencies. Impacts of AI on Media Ecosystems Through this analytical approach, 19 impact themes emerged, along with their corresponding scoring definitions: Interestingly, many scenarios articulated themes around how AI intersects with fact-checking, trust, misinformation, ethics, labor concerns, and evolving business models. Although some concepts may not be entirely distinct, this categorization offers a meaningful overview of the key ideas represented in the data. Distribution of Impact Themes Comparing these findings with those in the OSF report reveals some discrepancies. For instance, while the report emphasizes personalization and misinformation, these themes were less prevalent in the analyzed scenarios. Moreover, themes such as the rise of AI agents and audience fragmentation were mentioned but did not cluster significantly in the analysis. To capture potentially interesting but less prevalent impacts, the clustering was rerun with a smaller minimum cluster size. This adjustment yielded hundreds more concept themes, revealing insights into longer-tail issues. Positive visions for generative AI included reduced language barriers and increased accessibility for marginalized audiences, while concerns about societal fragmentation and privacy were also raised. Impacts Over Time and Around the World The analysis also explored how the impacts varied based on the timeframe selected by writers and their geographic locations. Using a Chi-Squared test, it was determined that “AI Personalization” trends towards long-term implications, while both “AI Fact-Checking” and “AI and Misinformation” skew toward shorter-term issues. This suggests that scenario writers perceive misinformation impacts as imminent threats, likely reflecting ongoing developments in the media landscape. When examining the distribution of impacts by region, it was found that “AI Fact-Checking” was more frequently noted by writers from Africa and Asia, while “AI and Misinformation” was less prevalent in scenarios from African writers but more so in those from Asian contributors. This indicates a divergence in perspectives on AI’s role in the media ecosystem.

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November 13, 2024in AI Tools, Data, Generative AI, Google, Salesforce, Technology
Enterprises are Adopting AI-powered Automation Platforms
12Nov

Enterprises are Adopting AI-powered Automation Platforms

The rapid pace of AI technological advancement is placing immense pressure on teams, often leading to disagreements due to the unrealistic expectations businesses have for the speed and agility of new technology implementation. A staggering 88% of IT professionals report that they are unable to keep up with the flood of AI-related requests within their organizations. Executives from UiPath, Salesforce, ServiceNow, and ManageEngine offer insights into how enterprises can navigate these challenges. Leading enterprises are adopting AI-powered automation platforms that understand, automate, and manage end-to-end processes. These platforms integrate seamlessly with existing enterprise technologies, using AI to reduce friction, eliminate inefficiencies, and enable teams to achieve business goals faster, with greater accuracy and efficiency. This year’s innovation drivers include tools such as Intelligent Document Processing, Communications Mining, Process and Task Mining, and Automated Testing. “Automation is the best path to deliver on AI’s potential, seamlessly integrating intelligence into daily operations, automating backend processes, upskilling employees, and revolutionizing industries,” says Mark Gibbs, EMEA President, UiPath. Jessica Constantinidis, Innovation Officer EMEA at ServiceNow, explains, “Intelligent Automation blends Robotic Process Automation (RPA), Artificial Intelligence (AI), and Machine Learning (ML) with well-defined processes to automate decision-making outcomes.” “Hyperautomation provides a business-driven, disciplined approach that enterprises can use to make informed decisions quickly by analyzing process and data feedback within the organization,” adds Constantinidis. Thierry Nicault, AVP and General Manager at Salesforce Middle East, emphasizes that while companies are eager to embrace AI, the pace of change often leads to confusion and stifles innovation. He notes, “By deploying AI and Hyperintelligent Automation tools, organizations can enhance productivity, visibility, and operational transformation.” Automation is driving growth and innovation across industries. AI-powered tools are simplifying processes, improving business revenues, and contributing to economic diversification. Ramprakash Ramamoorthy, Director of AI Research at ManageEngine, highlights how Hyperintelligent Automation, powered by AI, uses tools like Natural Language Processing (NLP) and Intelligent Document Processing to detect anomalies, forecast business trends, and empower decision-making. The IT Pushback Despite enthusiasm for AI, IT professionals are raising concerns. A Salesforce survey revealed that 88% of IT professionals feel overwhelmed by the influx of AI-related requests, with many citing resource constraints, data security concerns, and data quality issues. Business stakeholders often have unrealistic expectations about how quickly new technologies can be implemented, creating friction. According to Constantinidis of ServiceNow, many organizations lack transparency across their business units, making it difficult to fully understand their processes. As a result, automating processes becomes challenging. She adds, “Before full hyperautomation is possible, issues like data validation, classification, and privacy must be prioritized.” Automation platforms need accurate data, and governance is crucial in managing what data is used for AI models. “You need AI skills to teach and feed the data, and you also need a data specialist to clean up your data lake,” Constantinidis explains. Gibbs from UiPath stresses that automation must be designed in collaboration with the business users who understand the processes and systems. Once deployed, a feedback loop ensures continuous improvement and refinement of automated workflows. Ramamoorthy from ManageEngine notes that adopting Hyperintelligent Automation alongside existing workflows poses challenges. Enterprises must evaluate their technology stack, considering the costs, skills required, and the potential benefits. Strategic Integration of AI and Automation To successfully implement Hyperintelligent Automation tools, enterprises need a blend of IT and business skills. Mark Gibbs of UiPath points out, “These skills ensure organizations can effectively implement, manage, and optimize hyperintelligent technologies, aligning them with organizational goals.” Salesforce’s Nicault adds, “Enterprises must empower both IT and business teams to embrace AI, fostering innovation while ensuring the technology delivers real value.” Business skills are equally crucial, including strategic planning, process analysis, and change management. Ramamoorthy emphasizes that these competencies help identify automation opportunities and align them with business goals. According to Bassel Khachfeh, Digital Solutions Manager at Omnix, automation must be implemented with a focus on regulatory and compliance needs specific to the industry. This approach ensures the technology supports future growth and innovation. Transforming Customer Experiences and Business Operations As automation evolves, it’s transforming not only back-end processes but also customer experiences and decision-making at every level. Constantinidis from ServiceNow explains that hyperintelligence enables enterprises to predict outcomes and avert crises by trusting AI’s data accuracy. Gibbs from UiPath adds that automation allows enterprises to unlock untapped opportunities, speeding up the transformation of manual processes and enhancing business efficiency. AI is already making an impact in areas like supply chain management, regulatory compliance, and customer-facing processes. Ramamoorthy of ManageEngine notes that AI-powered NLP is revolutionizing enterprise chatbots and document processing, enabling businesses to automate complex workflows like invoice handling and sentiment analysis. Khachfeh from Omnix highlights how Cognitive Automation platforms elevate RPA by integrating AI-driven capabilities, such as NLP and Optical Character Recognition (OCR), to further streamline operations. Looking Ahead Hyperintelligent Automation, driven by AI, is set to revolutionize industries by enhancing efficiency, driving innovation, and enabling smarter decision-making. Enterprises that strategically adopt these tools—by integrating IT and business expertise, prioritizing data governance, and continuously refining their automated workflows—will be best positioned to navigate the complexities of AI and achieve sustainable growth. 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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November 12, 2024in Agentforce Platform, Artificial Intelligence, Data, Enterprise, Salesforce, Technology
healthcare Can prioritize ai governance
11Nov

Healthcare Can Prioritize AI Governance

As artificial intelligence gains momentum in healthcare, it’s critical for health systems and related stakeholders to develop robust AI governance programs. AI’s potential to address challenges in administration, operations, and clinical care is drawing interest across the sector. As this technology evolves, the range of applications in healthcare will only broaden.

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November 11, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Laws, Life Sciences Cloud, Salesforce, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Technology
Pioneering AI-Driven Customer Engagement
11Nov

Pioneering AI-Driven Customer Engagement

With Salesforce at the forefront of the AI revolution, Agentforce, introduced at Dreamforce, represents the next phase in customer service automation. It integrates AI and human collaboration to automate repetitive tasks, freeing human talent for more strategic activities, ultimately improving customer satisfaction. Tallapragada emphasized how this AI-powered tool enables businesses, particularly in the Middle East, to scale operations and enhance efficiency, aligning with the region’s appetite for growth and innovation.

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November 11, 2024in Agentforce Platform, Data, Generative AI, Hyperforce, Salesforce Development Services
healthcare Can prioritize ai governance
07Nov

AI Data Privacy and Security

Three Key Generative AI Data Privacy and Security Concerns The rise of generative AI is reshaping the digital landscape, introducing powerful tools like ChatGPT and Microsoft Copilot into the hands of professionals, students, and casual users alike. From creating AI-generated art to summarizing complex texts, generative AI (GenAI) is transforming workflows and sparking innovation. However, for information security and privacy professionals, this rapid proliferation also brings significant challenges in data governance and protection. Below are three critical data privacy and security concerns tied to generative AI: 1. Who Owns the Data? Data ownership is a contentious issue in the age of generative AI. In the European Union, the General Data Protection Regulation (GDPR) asserts that individuals own their personal data. In contrast, data ownership laws in the United States are less clear-cut, with recent state-level regulations echoing GDPR’s principles but failing to resolve ambiguity. Generative AI often ingests vast amounts of data, much of which may not belong to the person uploading it. This creates legal risks for both users and AI model providers, especially when third-party data is involved. Cases surrounding intellectual property, such as controversies involving Slack, Reddit, and LinkedIn, highlight public resistance to having personal data used for AI training. As lawsuits in this arena emerge, prior intellectual property rulings could shape the legal landscape for generative AI. 2. What Data Can Be Derived from LLM Output? Generative AI models are designed to be helpful, but they can inadvertently expose sensitive or proprietary information submitted during training. This risk has made many wary of uploading critical data into AI models. Techniques like tokenization, anonymization, and pseudonymization can reduce these risks by obscuring sensitive data before it is fed into AI systems. However, these practices may compromise the model’s performance by limiting the quality and specificity of the training data. Advocates for GenAI stress that high-quality, accurate data is essential to achieving the best results, which adds to the complexity of balancing privacy with performance. 3. Can the Output Be Trusted? The phenomenon of “hallucinations” — when generative AI produces incorrect or fabricated information — poses another significant concern. Whether these errors stem from poor training, flawed data, or malicious intent, they raise questions about the reliability of GenAI outputs. The impact of hallucinations varies depending on the context. While some errors may cause minor inconveniences, others could have serious or even dangerous consequences, particularly in sensitive domains like healthcare or legal advisory. As generative AI continues to evolve, ensuring the accuracy and integrity of its outputs will remain a top priority. The Generative AI Data Governance Imperative Generative AI’s transformative power lies in its ability to leverage vast amounts of information. For information security, data privacy, and governance professionals, this means grappling with key questions, such as: With high stakes and no way to reverse intellectual property violations, the need for robust data governance frameworks is urgent. As society navigates this transformative era, balancing innovation with responsibility will determine whether generative AI becomes a tool for progress or a source of new challenges. While generative AI heralds a bold future, history reminds us that groundbreaking advancements often come with growing pains. It is the responsibility of stakeholders to anticipate and address these challenges to ensure a safer and more equitable AI-powered world. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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November 7, 2024in Data, Generative AI, Laws
10 Top AI Jobs in 2025
01Nov

10 Top AI Jobs in 2025

10 Top AI Jobs in 2025 As we approach 2025, the demand for AI expertise is on the rise. Companies are seeking professionals with a strong background in AI, paired with practical experience. This insight explores 10 of the top AI jobs, the skills they require, and the industries that are driving AI adoption. If you are of the camp worrying about artificial intelligence replacing you, read on to see how you can leverage AI to upskill your career. AI is increasingly becoming an integral part of our lives, influencing various sectors from healthcare and finance to manufacturing, retail, and education. It is automating routine tasks, enhancing user experiences, and improving decision-making processes. AI is transitioning from data centers into everyday devices such as smartphones, IoT devices, and autonomous vehicles, becoming more efficient and safer thanks to advancements in real-time processing, lower latency, and enhanced privacy measures. The ethical use of AI is also at the forefront, emphasizing fairness, transparency, and accountability in AI models and decision-making processes. This proactive approach to ethics contrasts with past technological advancements, where ethical considerations often lagged behind. The rapid growth of AI translates to an increasing number of job opportunities. Below, we discuss the skills sought in AI specialists, the industries adopting AI at a fast pace, and a rundown of the 10 hottest AI jobs for 2025. Top AI Job Skills While many programmers are self-taught, the AI field demands a higher level of expertise. An analysis of 15,000 job postings found that 77% of AI roles require a master’s degree, while only 8% of positions are available to candidates with just a high school diploma. Most job openings call for mid-level experience, with only 12% for entry-level roles. Interestingly, while remote work is common in IT, only 11% of AI jobs offer fully remote positions. Being a successful AI developer requires more than coding skills; proficiency in core AI programming languages (like Python, Java, and R) is essential. Additional skills in communication, digital marketing strategies, effective collaboration, and analytical abilities are also critical. Moreover, a basic understanding of psychology is beneficial for simulating human behavior, and knowledge of AI security, privacy, and ethical practices is increasingly necessary. Industries Embracing AI Certain sectors are rapidly adopting AI technologies, including: 10 Top AI Jobs AI job roles are evolving quickly. Specialists are increasingly in demand over generalists, with a focus on deep knowledge in specific areas. Here are 10 promising AI job roles for 2025, along with their expected salaries based on job postings. As AI continues to evolve, these roles will play a pivotal part in shaping the future of various industries. Preparing for a career in AI requires a combination of technical skills, ethical understanding, and a willingness to adapt to new technologies. As we’ve seen with Salesforce a push for upskilling in artificial intelligence is here. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more 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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November 1, 2024in AI Tools, Artificial Intelligence, Data, Google, Technology
AI and Disability
28Oct

AI and Disability

Dr. Johnathan Flowers of American University recently sparked a conversation on Bluesky regarding a statement from the organizers of NaNoWriMo, which endorsed the use of generative AI technologies, such as LLM chatbots, in this year’s event. Dr. Flowers expressed concern about the implication that AI assistance was necessary for accessibility, arguing that it could undermine the creativity and agency of individuals with disabilities. He believes that art often serves as a unique space where barriers imposed by disability can be transcended without relying on external help or engaging in forced intimacy. For Dr. Flowers, suggesting the need for AI support may inadvertently diminish the perceived capabilities of disabled and marginalized artists. Since the announcement, NaNoWriMo organizers have revised their stance in response to criticism, though much of the social media discussion has become unproductive. In earlier discussions, the author has explored the implications of generative AI in art, focusing on the human connection that art typically fosters, which AI-generated content may not fully replicate. However, they now wish to address the role of AI as a tool for accessibility. Not being personally affected by physical disability, the author approaches this topic from a social scientific perspective. They acknowledge that the views expressed are personal and not representative of any particular community or organization. Defining AI In a recent presentation, the author offered a new definition of AI, drawing from contemporary regulatory and policy discussions: AI: The application of specific forms of machine learning to perform tasks that would otherwise require human labor. This definition is intentionally broad, encompassing not just generative AI but also other machine learning applications aimed at automating tasks. AI as an Accessibility Tool AI has potential to enhance autonomy and independence for individuals with disabilities, paralleling technological advancements seen in fields like the Paris Paralympics. However, the author is keen to explore what unique benefits AI offers and what risks might arise. Benefits Risks AI and Disability The author acknowledges that this overview touches only on some key issues related to AI and disability. It is crucial for those working in machine learning to be aware of these dynamics, striving to balance benefits with potential risks and ensuring equitable access to technological advancements. 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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October 28, 2024in AI Tools, Data, Generative AI
AI Strategy for Your Business
27Oct

AI Strategy for Your Business

How to Create a Winning AI Strategy for Your Business To maximize the value of AI, organizations must align their AI projects with strategic business objectives. Here’s a 10-step guide to crafting an effective AI strategy, including sample templates to support your planning. While AI adoption is on the rise, many companies still struggle to unlock its full potential. According to the 2024 IDC report Scaling AI Initiatives Responsibly, even organizations with advanced AI practices, termed “AI Masters,” face a 13% failure rate, while those still emerging in AI see a 20% failure rate. Challenges such as poor data quality and cultural resistance often contribute to these failures. To avoid these pitfalls, companies need to adopt a more deliberate and strategic approach to AI implementation. As Nick Kramer from SSA & Company states, “It’s not just about implementing the right technology; a lot of work needs to be done beforehand to succeed with AI.” What is an AI Strategy and Why is it Important? An AI strategy unifies all necessary components—such as data, technology, and talent—required to achieve business goals through AI. This includes: A well-designed AI strategy sets clear directions on how AI should be leveraged to achieve optimal outcomes within the organization. 10 Steps to Craft a Successful AI Strategy Resources for AI Strategy Templates If you’re ready to start building your AI strategy, here are several resources offering templates and guidance: By following these steps and utilizing the right resources, businesses can ensure they capture AI in ways that align with their strategic goals and maximize their competitive edge. 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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October 27, 2024in Data, Generative AI, Technology

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