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Salesforce Agentforce Integration

Agentforce at Work

Agentforce Salesforce Agentforce in Action: A Practical Example of Using Agents in Salesforce Autonomous Agents on the Agentforce Platform Agentforce represents a transformative shift in Salesforce’s strategy, poised to redefine how users engage with their CRM. By introducing both assistive AI—enhanced by generative AI for capabilities like summaries and sales emails—and autonomous AI, which empowers agents to automate actions without human oversight, Agentforce helps users operate more efficiently in Salesforce. Despite the excitement around Agentforce, most blogs and marketing materials focus on AI hype rather than practical applications. This insight focuses on illustrating how these tools work and the tangible value they can provide for your organization’s custom processes. Curious about setting up Agentforce agents using both out-of-the-box actions and custom actions? Let’s dive in. What is Agentforce? Agentforce is Salesforce’s conversational AI tool for CRM. In simple terms, it lets users “talk” to Salesforce. Powered by generative AI and the Atlas Reasoning Engine, Agentforce processes user input to perform tasks like summarizing data from objects, updating fields, and generating content such as emails or knowledge articles. This innovative tool is only at the beginning of its journey, likely setting the stage for a future where CRM interactions may evolve beyond traditional form-based interfaces to more intuitive chatbot-style engagement. Scenario: Managing Sales Pipeline Consider a salesperson with the daily objectives of tracking deals, managing pipeline opportunities, and identifying potential risks. Traditionally, this would require manually navigating numerous Salesforce objects, risking data inconsistencies and user errors. Agentforce’s assistive actions can streamline much of this, automating processes to identify key deals, summarize progress, and track deal risks across the pipeline. Let’s take a closer look at configuring a custom action for a pipeline summary. All powered by Salesforce Agentforce. Step-by-Step Guide to Configuring a Pipeline Summary Action Agentforce Use Cases: Getting Started Agentforce offers powerful tools for implementing AI-based functions within Salesforce, but to realize productivity gains, consider the following: Agentforce’s standard actions are a great starting point, providing immediate productivity impacts that can be enhanced as you customize actions to meet specific needs. For tailored guidance on integrating Agentforce, explore Tectonic’s Salesforce Agentforce Consulting Services. Tectonic’s expertise can support your organization in optimizing user experience, boosting productivity, and training users to responsibly leverage Agentforce’s capabilities across industries and channels. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Salesforce AI Research Introduces LaTRO

Salesforce AI Research Introduces LaTRO

Salesforce AI Research Introduces LaTRO: A Breakthrough in Enhancing Reasoning for Large Language Models Large Language Models (LLMs) have revolutionized tasks such as answering questions, generating content, and assisting with workflows. However, they often struggle with advanced reasoning tasks like solving complex math problems, logical deduction, and structured data analysis. Salesforce AI Research has addressed this challenge by introducing LaTent Reasoning Optimization (LaTRO), a groundbreaking framework that enables LLMs to self-improve their reasoning capabilities during training. The Need for Advanced Reasoning in LLMs Reasoning—especially sequential, multi-step reasoning—is essential for tasks that require logical progression and problem-solving. While current models excel at simpler queries, they often fall short in tackling more complex tasks due to a reliance on external feedback mechanisms or runtime optimizations. Enhancing reasoning abilities is therefore critical to unlocking the full potential of LLMs across diverse applications, from advanced mathematics to real-time data analysis. Existing techniques like Chain-of-Thought (CoT) prompting guide models to break problems into smaller steps, while methods such as Tree-of-Thought and Program-of-Thought explore multiple reasoning pathways. Although these techniques improve runtime performance, they don’t fundamentally enhance reasoning during the model’s training phase, limiting the scope of improvement. Salesforce AI Research Introduces LaTRO: A Self-Rewarding Framework LaTRO shifts the paradigm by transforming reasoning into a training-level optimization problem. It introduces a self-rewarding mechanism that allows models to evaluate and refine their reasoning pathways without relying on external feedback or supervised fine-tuning. This intrinsic approach fosters continual improvement and empowers models to solve complex tasks more effectively. How LaTRO Works LaTRO’s methodology centers on sampling reasoning paths from a latent distribution and optimizing these paths using variational techniques. Here’s how it works: This self-rewarding cycle ensures that the model continuously refines its reasoning capabilities during training. Unlike traditional methods, LaTRO’s framework operates autonomously, without the need for external reward models or costly supervised feedback loops. Key Benefits of LaTRO Performance Highlights LaTRO’s effectiveness has been validated across various datasets and models: Applications and Implications LaTRO’s ability to foster logical coherence and structured reasoning has far-reaching applications in fields requiring robust problem-solving: By enabling LLMs to autonomously refine their reasoning processes, LaTRO brings AI closer to achieving human-like cognitive abilities. The Future of AI with LaTRO LaTRO sets a new benchmark in AI research by demonstrating that reasoning can be optimized during training, not just at runtime. This advancement by Salesforce AI Research highlights the potential for self-evolving AI models that can independently improve their problem-solving capabilities. Salesforce AI Research Introduces LaTRO As the field of AI progresses, frameworks like LaTRO pave the way for more autonomous, intelligent systems capable of navigating complex reasoning tasks across industries. LaTRO represents a significant leap forward, moving AI closer to achieving true autonomous reasoning. 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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Digital Transformation for Life Sciences

Salesforce Winter 25 Release for Life Sciences

The Salesforce Winter ’25 release introduces groundbreaking updates tailored to the life sciences industry. Whether you’re in pharmaceuticals, biotech, or medical devices, these innovations aim to streamline operations, enhance patient care, and strengthen stakeholder relationships. Let’s explore the key enhancements: Quote and Agreement Automation Salesforce’s advancements in Quote and Agreement Automation simplify pricing and contractual negotiations for life sciences organizations. Key benefits include: These updates not only improve operational efficiency but also reduce errors and compliance risks, helping organizations stay ahead in a highly regulated industry. Financial Assistance Programs Navigating financial support for patients can be challenging, but Salesforce is making it easier. The Winter ’25 release enhances tools for managing financial assistance programs, ensuring eligible patients get the support they need. Key features include: By easing the financial burden on patients, these updates strengthen trust and foster better patient-provider relationships. Participant Recruitment and Enrollment Recruiting and enrolling participants for clinical trials is now more efficient with Salesforce’s new AI-driven tools. Notable improvements include: These updates not only shorten recruitment timelines but also enhance the overall trial experience for participants. Patient Program Outcome Management Tracking patient outcomes is vital for life sciences companies, and Salesforce’s Patient Program Outcome Management tools deliver powerful capabilities. Features include: These tools empower organizations to make data-driven decisions, improving program effectiveness and elevating patient care. Pharmacy Benefits Verification Verifying patient benefits is often a time-consuming task. The Winter ’25 release introduces automation to streamline this process. Key highlights: By simplifying benefits verification, this update enhances efficiency for providers and ensures a smoother experience for patients. Why It Matters for Life Sciences The Winter ’25 release is a leap forward for the life sciences industry, offering tools that: From automating quotes and agreements to improving patient support and trial management, these updates empower organizations to focus on delivering exceptional care and innovation. Ready to Transform Your Salesforce Experience?Tectonic is here to help you unlock the full potential of these enhancements. Whether you’re looking to optimize patient care, streamline operations, or improve outcomes, we’ll guide you every step of the way. Contact Tectonic today to get started! 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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copilots and agentic ai

Copilots and Agentic AI

Agentic AI vs. Copilots: Defining the Future of Generative AI Artificial Intelligence has rapidly evolved, progressing from simple automation to generative models, to copilots. But now, a new player—Agentic AI—has emerged, promising to redefine the AI landscape. Is Agentic AI the next logical step, or will it coexist alongside copilots, each serving distinct roles? Copilots and Agentic AI. Generative AI: Creativity with a Human Touch Since the launch of ChatGPT, generative AI has dominated tech priorities, offering businesses the ability to generate content—text, images, videos, and more—from pre-defined data. However, while revolutionary, generative AI still relies heavily on human input to guide its output, making it a powerful collaborator rather than an autonomous actor. Enter Agentic AI: Autonomy Redefined Agentic AI represents a leap forward, offering systems that possess autonomy and the ability to act independently to achieve pre-defined goals. Unlike generative AI copilots that respond to human prompts, Agentic AI makes decisions, plans actions, and learns from experience. Think of it as Siri or Alexa—enhanced with autonomy and learning capabilities. Gartner recently spotlighted Agentic AI as its top technology trend for 2025, predicting that by 2028, at least 15% of day-to-day work decisions will be made autonomously, up from virtually none today. Agentforce and the Third Wave of AI Salesforce’s “Agentforce,” unveiled at Dreamforce, is a prime example of Agentic AI’s potential. These autonomous agents are designed to augment employees by handling tasks across sales, service, marketing, and commerce. Salesforce CEO Mark Benioff described it as the “Third Wave of AI,” going beyond copilots to deliver intelligent agents deeply embedded into customer workflows. Salesforce aims to empower one billion AI agents by 2025, integrating Agentforce into every aspect of customer success. Benioff took a swipe at competitors’ bolt-on generative AI solutions, emphasizing that Agentforce is deeply embedded for maximum value. The Role of Copilots: Collaboration First While Agentic AI gains traction, copilots like Microsoft’s Copilot Studio and SAP’s Joule remain critical for businesses focused on intelligent augmentation. Copilots act as productivity boosters, working alongside humans to optimize processes, enhance creativity, and provide decision-making support. SAP’s Joule, for example, integrates seamlessly into existing systems to optimize operations while leaving strategic decision-making in human hands. This collaborative model aligns well with businesses prioritizing agility and human oversight. Agentic AI: Opportunities and Challenges Agentic AI’s autonomy offers significant potential for streamlining complex processes, reducing human intervention, and driving productivity. However, it also comes with risks. Eleanor Watson, AI ethics engineer at Singularity University, warns that Agentic AI systems require careful alignment of values and goals to avoid unintended consequences like dangerous shortcuts or boundary violations. In contrast, copilots retain human agency, making them particularly suited for creative and knowledge-based roles where human oversight remains essential. Copilots and Agentic AI The choice between Agentic AI and copilots hinges on an organization’s priorities and risk tolerance. For simpler, task-specific applications, copilots excel by providing assistance without removing human input. Agentic AI, on the other hand, shines in complex, multi-task scenarios where autonomy is key. Dom Couldwell, head of field engineering EMEA at DataStax, emphasizes the importance of understanding when to deploy each model. “Use a copilot for specific, focused tasks. Use Agentic AI for complex, goal-oriented processes involving multiple tasks. And leverage Retrieval Augmented Generation (RAG) in both to provide context to LLMs.” The Road Ahead: Coexistence or Dominance? As AI evolves, Agentic AI and copilots may coexist, serving complementary roles. Businesses seeking full automation and scalability may gravitate toward Agentic AI, while those prioritizing augmented intelligence and human collaboration will continue to rely on copilots. Ultimately, the future of AI will be defined not by one model overtaking the other, but by how well each aligns with the specific needs, goals, and challenges of the organizations adopting them. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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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2025: The Rise of AI Agents and Industry-Focused Innovation

2025: The Rise of AI Agents and Industry-Focused Innovation

Over the past few years, CX vendors have rapidly integrated generative AI (GenAI) across the customer experience landscape. This wave of innovation has brought advancements like auto-summarization, customer response recommendations, and intent analysis, especially within Contact Center as a Service (CCaaS) solutions. However, as these capabilities become standard, differentiation now hinges on more advanced AI solutions, orchestration of cross-platform workflows, and collection of industry-specific datasets. AI Agents and Industry-Focused Innovation. Agentic AI, where bots autonomously handle tasks without human intervention, is emerging as a critical differentiator. This shift is reshaping sector-specific processes. Take network providers, for instance; they can leverage agentic AI to detect service outages, create affected customer segments, and proactively send alerts. Salesforce exemplifies this trend with its Agentforce platform, which debuted at Dreamforce 2024, introducing 100 pre-configured, autonomous bots designed for specific industries. By 2025, such bots will likely proliferate, expanding across ecosystems like Workday to facilitate cross-functional automation. Toward a More Autonomous Enterprise As autonomous AI agents advance, they are poised to manage complex, multi-step workflows collaboratively. This move will help organizations move closer to an autonomous enterprise model, where human oversight drives the deployment, testing, and optimization of AI agents. In this model, collaboration platforms such as Microsoft Teams, Slack, and Zoom will serve as operational hubs for managing and refining AI-driven processes. While this full vision may take longer to achieve, 2025 promises substantial advancements in sector-specific efficiencies through AI agents. Not all industries, however, are equally poised to benefit; while healthcare, financial services, and retail lead in AI-enabled CX solutions, other sectors such as hospitality, travel, and education still lag. The Need for Sector-Specific Use Case Libraries CX vendors could empower businesses by providing industry-specific AI use case libraries, building confidence in AI-agent-driven experiences. For example, bots in the finance sector could streamline billing, invoice processing, and ledger management, while spotting and correcting errors. Other industries would benefit from AI innovations tailored to their unique challenges, but such solutions will require co-innovation across CX platforms. 2025 Strategic Technology Trends Gartner’s top technology trends for 2025 provide a framework for CIOs aiming to future-proof their organizations. These trends fall into three themes: AI imperatives, new computing frontiers, and human-machine synergy. These trends will push organizations to adopt cloud, AI, and sustainability-focused architectures, despite challenges. As AI capabilities evolve, so will the risks, emphasizing the need for robust security and ethical frameworks. Salesforce charges up its game with its Agentforce platform, which debuted at Dreamforce 2024, introducing 100 pre-configured, autonomous bots designed for specific industries. By 2025, such bots will likely proliferate, expanding across ecosystems like Workday to facilitate cross-functional automation. Preparing for 2025: Upskilling for the Future As organizations embrace these transformative trends, they must also address a persistent skill gap. Pluralsight’s recent survey reveals that 20% of organizations have deployed AI, while 55% are planning to. However, without strategic business alignment, technology adoption won’t necessarily translate to customer value. For organizations, a focus on responsible innovation and proactive skills development in AI, cloud security, and sustainability will be vital. By preparing for these 2025 trends, businesses can navigate the complexities of the tech landscape and position themselves for long-term success. AI Agents and Industry-Focused Innovation As you prepare for 2025. Tectonic can help you align your goals with your road map. Contact us today! Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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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Scarf and Salesforce

Scarf and Salesforce

Scarf Integrates Open Source Software Tracking Platform with Salesforce At KubeCon + CloudNativeCon 2024, Scarf announced the integration of its open-source software usage tracking platform with Salesforce CRM. This integration arrives as debates around the definition and economics of open source remain a hot topic in the tech community. Scarf also introduced updates to its platform, including enhanced event data correction and flagging capabilities for improved accuracy in company matching and attribution. New data filtering options were also added for more refined data exports. The Scarf platform enables IT vendors to identify organizations consuming open-source software at significant scale, presenting opportunities to offer additional support or promote commercial add-ons for open-source tools. To date, the Scarf gateway has tracked over seven billion events, connecting usage data to specific organizations via attributes such as internet addresses. Strengthening the Open Source Ecosystem Scarf CEO Avi Press emphasized the platform’s role in maintaining the economic viability of the open-source ecosystem, often in partnership with organizations like The Linux Foundation. Without these insights, fewer IT vendors would sponsor open-source projects, Press noted, which would hinder the ecosystem’s growth and sustainability. However, the open-source community frequently experiences friction. Licensing changes by IT vendors often lead to project forks, with contributors reverting to previous licensing terms, sometimes backed by cloud providers. Press believes targeted commercial value opportunities—supported by tools like Scarf—can reduce this friction by fostering more productive engagements between vendors and organizations. Challenges and Evolving Definitions in Open Source While open source remains foundational to the tech world, it continues to face ideological and practical challenges. For decades, debates over licensing models have sparked disagreements, including the current contention around defining open-source AI models. Many models fail to disclose critical training details, leading to further disputes. Ultimately, each organization must navigate these issues by adopting its own definition of open source and deciding how best to support the ecosystem. Tools like Scarf’s platform aim to bridge gaps, enabling IT vendors and organizations to collaborate more effectively, ensuring the continued growth of open source. 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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salesforce einstein insights

Salesforce Einstein Conversation Insights

Unlocking Einstein Conversation Insights in Salesforce: Setup, Integration, and Customization In this insight, we’ll guide you through setting up Einstein Conversation Insights in Salesforce, integrating it with platforms like Zoom, managing permissions, and customizing the dataflow schedule for optimal performance. As a marketer from way back when, little gets me as excited about the future of technology than marketing tools that make us smarter and faster. What is Einstein Conversation Insights? Einstein Conversation Insights (ECI) empowers teams to analyze and identify patterns, phrases, and areas of focus within voice and video interactions. By tracking terms and extracting actionable insights, managers and representatives can prioritize follow-ups and improve decision-making through detailed call logs and actionable dashboards. No longer are we hampered by the limitations of written text! Step 1: Enabling Einstein Conversation Insights To begin utilizing Einstein Conversation Insights: Step 2: Assigning Permissions To grant users access to ECI: Step 3: Connecting Recording Providers Voice Recording Providers To analyze call recordings: Video Recording Providers For video analysis, integrate your conferencing platform: Setting Up Zoom Integration To integrate Salesforce with Zoom: Once complete, users will need to link their Zoom accounts individually. A message will confirm successful setup. Click Take me there to finalize the connection. Step 4: Exploring the Conversation Insights App After linking your Zoom account, visit the Conversation Insights App under the Analytics tab. This app provides a comprehensive view of call details, recordings, and actionable insights, empowering teams to focus on strategic improvements. Step 5: Customizing Dataflow Schedule By default, ECI updates its dataflow every eight hours, refreshing your dashboards with new insights. To modify this schedule: Frequently Asked Questions 1. What are the benefits of Einstein Conversation Insights?Einstein Conversation Insights automates the transcription and analysis of calls, identifies trends, and recommends next steps to accelerate sales cycles and free up sales staff to focus on opportunity closing efforts. 2. Does ECI record calls?No, ECI does not record calls. Instead, it analyzes existing recordings from connected providers to generate insights. 3. Are there any limitations?Yes, Salesforce allows up to 100 custom insights, with each insight accommodating a maximum of 25 keywords, each up to 255 characters long. Conclusion Einstein Conversation Insights is a game-changing tool that analyzes voice and video interactions to provide actionable insights, empowering teams to make data-driven decisions. By integrating with Salesforce and platforms like Zoom, you can effortlessly track call details, identify trends, and streamline workflows. Customizing your dataflow schedule ensures your dashboards always reflect the latest information, enhancing efficiency and enabling timely decision-making. Ready to take your insights further? Start integrating Einstein Conversation Insights today! By Tectonic MarketingOpps Director, Shannan Hearne 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’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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AI Agents as Tools of Trust

AI Agents as Tools of Trust

Salesforce Report Highlights AI Agents as Tools to Rebuild Consumer Trust For businesses of any size, the to-do list never ends. Monitoring customers, understanding their needs, and delivering products and services that align with their expectations are critical. Salesforce’s latest research, however, points to a troubling trend: consumer trust is at an all-time low. Yet, the report, State of the AI Connected Customer, also suggests that AI—particularly agentic AI—could help reverse this decline. Trust in Decline The key finding of the Salesforce report is stark: consumer trust in companies has taken a significant hit. Among 15,015 surveyed consumers, 72% say they trust companies less today than they did a year ago. Compounding this is the rapid advancement of AI; 60% of respondents believe that the rise of AI increases the importance of businesses being trustworthy. One major culprit behind eroding trust is the perceived mishandling of customer data. A staggering 65% of respondents feel companies are careless with data, adding to the skepticism. While high prices remain the top reason customers abandon brands, 43% pointed to poor customer service as a major deterrent. Can AI Agents Fill the Gap? The Salesforce report suggests that AI agents—when deployed transparently—could address many of the factors driving distrust and disengagement. Younger consumers, particularly Gen Z and millennials, appear more open to interacting with AI agents. Notable insights from the research include: However, trust is non-negotiable. Transparency is a critical factor for AI adoption: As Michael Affronti, SVP and General Manager of Salesforce Commerce Cloud, explains: “AI agents can help brands deliver consistent, personalized experiences for shoppers across every channel — deepening customer loyalty and ultimately driving more sales.” Building Trust Through Transparency The research underscores the potential for AI to transform customer interactions, but it also highlights the challenges. Transparency and accountability are essential for AI systems to inspire confidence and loyalty. Salesforce’s AI solutions are designed to prioritize transparency and foster reliable consumer experiences. Features such as clear agent identification and robust escalation paths are steps in the right direction. However, companies must double down on governance frameworks and safeguards to ensure AI agents handle data responsibly. Final Thoughts While the idea of using AI to rebuild consumer trust is promising, it’s not without its challenges. Establishing trust in AI itself remains a work in progress. Consumers expect companies to prioritize not only innovation but also ethics, security, and accountability. The Salesforce report demonstrates that younger consumers are already embracing AI as a way to address today’s service expectations. For Salesforce and other companies leveraging agentic AI, the key to success will lie in balancing cutting-edge technology with meaningful protections for customer data and experiences. The future of AI-driven customer engagement isn’t just about meeting expectations—it’s about exceeding them in a way that inspires confidence and loyalty. With the right approach, AI agents could be a vital tool for restoring consumer trust in an era where skepticism runs high. 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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Commerce Cloud and Agentic AI

Gen X and Millennials Lead in Embracing Agentic AI

Gen X and Millennials Lead in Embracing Agentic AI: Salesforce Report Generation X and millennials are showing greater openness to adopting agentic artificial intelligence (AI), according to Salesforce’s State of the AI Connected Customer report. Agentic AI refers to autonomous agents capable of independently making decisions and performing tasks, learning and adapting from experiences without direct human supervision. This technology is making significant inroads across industries, with applications ranging from personalized recommendations and inventory management in retail to supply chain optimization in logistics. It also finds use in healthcare, finance, telecom, IT, and customer service. Generational Differences in AI Adoption The report highlights that millennials (57%) and Gen Xers (58%) in India are more inclined to embrace AI agents for faster and more proactive customer service compared to Gen Z (51%) and Baby Boomers (42%). These autonomous agents enhance customer experiences by delivering personalized and relevant content, which resonates more with the tech-savvy Gen X and millennial demographics. Who Are These Generations? Building Trust in the AI Era The report reveals a sharp decline in consumer trust, with trust levels at their lowest in eight years. Over half of the respondents feel companies are less trustworthy than a year ago and believe businesses mishandle customer data. Arun Parameswaran, SVP & Managing Director, Sales and Distribution at Salesforce India, emphasized the critical role of trust in AI strategies: “As we enter a new era of intelligent customer engagement, brands that prioritize trust in their AI strategies will be best positioned to deliver impactful, lasting connections.” Transparency, according to the report, is key to restoring consumer confidence in the AI-driven era. Companies that adopt responsible AI practices, particularly in the design and deployment of agentic AI, can foster stronger customer relationships. Global Perspective The findings are based on a survey of 15,015 consumers across India, Australia, Brazil, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Netherlands, Norway, Singapore, Spain, Sweden, the UK, and the US. As businesses increasingly integrate agentic AI into their operations, understanding generational attitudes and prioritizing ethical AI practices will be essential for fostering trust and delivering exceptional customer experiences. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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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Enterprises are Adopting AI-powered Automation Platforms

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 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 Won't Hurt Salesforce

AI Won’t Hurt Salesforce

Marc Benioff Dismisses AI Threats, Sets Sights on a Billion AI Agents in One Year Salesforce CEO Marc Benioff has no doubts about the transformative potential of AI for enterprise software, particularly Salesforce itself. At the core of his vision are AI agents—autonomous software bots designed to handle routine tasks, freeing up human workers to focus on more strategic priorities. “What if your workforce had no limits? That’s a question we couldn’t even ask over the past 25 years of Salesforce—or the 45 years I’ve been in software,” Benioff said during an appearance on TechCrunch’s Equity podcast. The Billion-Agent Goal Benioff revealed that Salesforce’s recently launched Agentforce platform is already being adopted by “hundreds of customers” and aims to deploy a billion AI agents within a year. These agents are designed to handle tasks across industries—from enhancing customer experiences at retail brands like Gucci to assisting patients with follow-ups in healthcare. To illustrate, Benioff shared his experience with Disney’s virtual Private Tour Guides. “The AI agent analyzed park flow, ride history, and preferences, then guided me to attractions I hadn’t visited before,” he explained. Competition with Microsoft and the AI Landscape While Benioff is bullish on AI, he hasn’t hesitated to criticize competitors—particularly Microsoft. When Microsoft unveiled its new autonomous agents for Dynamics 365 in October, Benioff dismissed them as uninspired. “Copilot is the new Clippy,” he quipped, referencing Microsoft’s infamous virtual assistant from the 1990s. Benioff also cited Gartner research highlighting data security issues and administrative flaws in Microsoft’s AI tools, adding, “Copilot has disappointed so many customers. It’s not transforming companies.” However, industry skeptics argue that the real challenge to Salesforce isn’t Microsoft but the wave of AI-powered startups disrupting traditional enterprise software. With tools like OpenAI’s ChatGPT and Klarna’s in-house AI assistant “Kiki,” companies are starting to explore GenAI solutions that can replace legacy platforms like Salesforce altogether. For example, Klarna recently announced it was moving away from Salesforce and Workday, favoring GenAI tools that enable seamless, conversational interfaces and faster data access. Why Salesforce Is Positioned to Win Despite the noise, Benioff remains confident that Salesforce’s extensive data infrastructure gives it a significant edge. “We manage 230 petabytes of customer data with robust security and sharing models. That’s what allows AI to thrive in our ecosystem,” he said. While companies may question how other platforms like OpenAI handle data, Salesforce offers an integrated approach, reducing the need for complex data migrations to other clouds, such as Microsoft Azure. Salesforce’s Own Use of AI Benioff also highlighted Salesforce’s internal adoption of Agentforce, using AI agents in its customer service operations, sales processes, and help centers. “If you’re authenticated on help.salesforce.com, you’re already interacting with our agent,” he noted. AI Startups: Threat or Opportunity? As for concerns about AI startups overtaking Salesforce, Benioff sees them as acquisition opportunities rather than existential threats. “We’ve made over 60 acquisitions, many of them startups,” he said. He pointed to Agentforce itself, which was built using technology from Airkit.ai, a startup founded by a former Salesforce employee. Salesforce Ventures initially invested in Airkit.ai before acquiring and integrating it into its platform. The Path Forward Benioff is resolute in his belief that AI won’t hurt Salesforce—instead, it will revolutionize how businesses operate. While skeptics warn of a seismic shift in enterprise software, Benioff’s strategy is clear: lean into AI, leverage data, and stay agile through innovation and acquisitions. “We’re just getting started,” he concluded, reiterating his vision for a future where AI agents expand the possibilities of work and customer experience like never before. 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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Snowflake Security and Development

Snowflake Security and Development

Snowflake Unveils AI Development and Enhanced Security Features At its annual Build virtual developer conference, Snowflake introduced a suite of new capabilities focused on AI development and strengthened security measures. These enhancements aim to simplify the creation of conversational AI tools, improve collaboration, and address data security challenges following a significant breach earlier this year. AI Development Updates Snowflake announced updates to its Cortex AI suite to streamline the development of conversational AI applications. These new tools focus on enabling faster, more efficient development while ensuring data integrity and trust. Highlights include: These features address enterprise demands for generative AI tools that boost productivity while maintaining governance over proprietary data. Snowflake aims to eliminate barriers to data-driven decision-making by enabling natural language queries and easy integration of structured and unstructured data into AI models. According to Christian Kleinerman, Snowflake’s EVP of Product, the goal is to reduce the time it takes for developers to build reliable, cost-effective AI applications: “We want to help customers build conversational applications for structured and unstructured data faster and more efficiently.” Security Enhancements Following a breach last May, where hackers accessed customer data via stolen login credentials, Snowflake has implemented new security features: These additions come alongside existing tools like the Horizon Catalog for data governance. Kleinerman noted that while Snowflake’s previous security measures were effective at preventing unauthorized access, the company recognizes the need to improve user adoption of these tools: “It’s on us to ensure our customers can fully leverage the security capabilities we offer. That’s why we’re adding more monitoring, insights, and recommendations.” Collaboration Features Snowflake is also enhancing collaboration through its new Internal Marketplace, which enables organizations to share data, AI tools, and applications across business units. The Native App Framework now integrates with Snowpark Container Services to simplify the distribution and monetization of analytics and AI products. AI Governance and Competitive Position Industry analysts highlight the growing importance of AI governance as enterprises increasingly adopt generative AI tools. David Menninger of ISG’s Ventana Research emphasized that Snowflake’s governance-focused features, such as LLM observability, fill a critical gap in AI tooling: “Trustworthy AI enhancements like model explainability and observability are vital as enterprises scale their use of AI.” With these updates, Snowflake continues to compete with Databricks and other vendors. Its strategy focuses on offering both API-based flexibility for developers and built-in tools for users seeking simpler solutions. By combining innovative AI development tools with robust security and collaboration features, Snowflake aims to meet the evolving needs of enterprises while positioning itself as a leader in the data platform and AI space. 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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