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salesforce service assistant
26Feb

Salesforce Service Assistant

Salesforce Service Assistant is an AI-powered tool that helps service representatives resolve cases faster. It’s available on Service Cloud and is designed to save time for agents. How it works Benefits Helps agents resolve cases faster, Saves time for service representatives, Grounded in the organization’s knowledge base and data, and Adheres to company policies. Additional information Alongside agent guidance, the Service Assistant provides two other notable features. The first enables agents to create conversation summaries with “just a click” after using the solution to complete a case. The second allows agents to request that the assistant auto-crafts a new knowledge article when its guidance proved insufficient, based on how they resolved the query. Thanks to this second feature, the Service Assistant may get better with time, aiding agent proficiency, customer satisfaction, and – ultimately – average handling time (AHT). However, despite this capability, Salesforce has pledged to advance the solution further. Indeed, during a recent webinar, Kevin Qi, Associate Product Manager at Salesforce, teased what will come in June. Pointing to Service Cloud’s Summer ‘25 release wave, Qi said: The next phase of Service Assistant involves actionable plans. So, not only will it help guide the service rep, but it’ll also take actions to automate various steps, so it can look up orders, check eligibilities, and more to help speed up the efficiency of tackling that case. Beyond the summer, Salesforce plans to have the Assistant blend modalities, guiding customer conversations across channels to further streamline the interaction. “The Service Assistant will become even more adaptive, support more channels, including messaging and voice, being able to adapt to changes in case context,” concluded Qi. The Latest AI Solutions on Service Cloud Alongside the Service Assistant, Salesforce has released several other AI and Agentforce capabilities, embedded across Service Cloud. Qi picked out the “Freeform Instructions in Service Email Assistant” feature for special reference. “If the agent doesn’t have a template already made for a particular instance, they can type – in natural language – the sort of email they’d want to generate and have Agentforce create that email in the flow of work,” he said. That capability may prove highly beneficial in helping agents piece their thoughts together when resolving a tricky case. After all, they can note some key points – in natural language – and the feature will create a coherent customer response. Alongside this comes a solution to quickly summarize case activity for wrap-up in beta. Yet, most new features focus on improving the knowledge that feeds into AI solutions, like the Service Assistant. For starters, there’s a flow orchestrator in beta that helps contact center leaders build a process for approving new knowledge articles and updates. Additionally, there’s an “Update Knowledge Content with AI” feature. This ingests prompts and – as it says on the tin – updates the tone, style, and length of particular knowledge articles. Last comes the “Knowledge Sync to Data Cloud” tool that pulls contact center knowledge into the Salesforce customer data platform (CDP). Not only does this democratize service insights, but it also supports contact centers in grounding the Service Assistant and other AI agents. Both of these final knowledge capabilities are now generally available. 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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February 26, 2025in Data, Salesforce0 Comments
Storing and Querying Log Data with Event Log Objects
25Feb

Storing and Querying Log Data with Event Log Objects

The Event Log Object framework provides a structured way to access event data stored in Salesforce. Unlike Event Log Files, which present event data as downloadable CSV files, Event Log Objects allow users to query event logs directly using SOQL. This approach offers a more integrated and dynamic way to analyze security, performance, and user activity data within Salesforce. Key Features of Event Log Objects Availability and Requirements Enabling Event Log Objects To enable Event Log Objects, follow these steps in Setup: Querying Event Log Data Event Log Objects provide valuable insights through SOQL queries, allowing organizations to track activity across security, application performance, and user engagement. Sample Queries by Use Case Security Analysis Identify users exporting the most rows via reports: sqlCopyEditSELECT UserIdentifier, SUM(RowCount) FROM ReportEventLog WHERE Origin=’ReportExported’ AND DAY_ONLY(Timestamp) > LAST_N_DAYS:10 GROUP BY UserIdentifier ORDER BY SUM(RowCount) DESC Application Performance Monitoring (APM) Track unexpected Apex exceptions by category: sqlCopyEditSELECT ExceptionCategory, COUNT(Timestamp) FROM ApexUnexpectedExcpEventLog WHERE DAY_ONLY(Timestamp) > LAST_N_DAYS:10 GROUP BY ExceptionCategory ORDER BY COUNT(Timestamp) DESC Product Intelligence Determine the most loaded Lightning pages: sqlCopyEditSELECT COUNT(Timestamp), PageUrl FROM LightningPageViewEventLog WHERE DAY_ONLY(Timestamp) > LAST_N_DAYS:10 GROUP BY PageUrl ORDER BY COUNT(Timestamp) DESC Key Considerations Available Event Log Objects Salesforce provides a wide range of Event Log Objects, each designed to capture specific types of system events: Visualizing Log Data with CRM Analytics Organizations can transform raw log data into actionable insights using Salesforce Direct in CRM Analytics. This enables users to: Best Practices for Event Log Objects To maximize the value of Event Log Objects, follow these best practices: By effectively leveraging Event Log Objects, organizations can enhance security monitoring, troubleshoot performance issues, and gain deeper insights into Salesforce activity. For more details on Event Log Objects and their use cases, refer to the Salesforce Object Reference Guide. 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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February 25, 2025in Data, Enterprise, Hyperforce, Salesforce, Salesforce Shield0 Comments
salesforce end to end
24Feb

Salesforce and Google Announcement

Salesforce (NYSE:CRM) has entered into a deal with Google (NASDAQ:GOOGL) to offer its customer relations management software, Agentforce artificial intelligence assistants, and Data Cloud offerings through Google Cloud, the companies announced today. Google and Salesforce already have many of the same clients, and this new deal will allow for more product integration between Google Workspace and Salesforce’s customer relationship management and AI offerings. Salesforce already uses Amazon (AMZN) Web Services for much of its cloud computing. “Our mutual customers have asked us to be able to work more seamlessly across Salesforce and Google Cloud, and this expanded partnership will help them accelerate their AI transformations with agentic AI, state-of-the-art AI models, data analytics, and more,” said Thomas Kurian, CEO of Google Cloud. The deal is expected to total $2.5B over the next seven years, according to a report by Bloomberg. Salesforce and Google today announced a major expansion of their strategic partnership, delivering choice in the models and capabilities businesses use to build and deploy AI-powered agents. In today’s constantly evolving AI landscape, innovations like autonomous agents are emerging so quickly that businesses struggle to keep pace. This expanded partnership provides crucial flexibility, empowering customers to develop tailored AI solutions that meet their specific needs, rather than being locked into a single model provider. Google Cloud is at the forefront of enterprise AI innovation with millions of developers building with Google’s cutting-edge Gemini models and on Google Cloud’s AI-optimized infrastructure. This expanded partnership will empower Salesforce customers to build Agentforce agents using Gemini and to deploy Salesforce on Google Cloud. This is an expansion of the existing partnership that allows customers to use data from Data Cloud and Google BigQuery bi-directionally via zero-copy technology—further equipping customers with the data, AI, trust, and actions they need to bring autonomous agents into their businesses. Additionally, this integration empowers Agentforce agents with the ability to reference up-to-the-minute data, news, current events, and credible citations, substantially enhancing their contextual awareness and ability to deliver accurate, evidence-backed responses. For example, in supply chain management and logistics, an agent built with Agentforce could track shipments and monitor inventory levels in Salesforce Commerce Cloud and proactively identify potential disruptions using real-time data from Google Search, including weather conditions, port congestion, and geopolitical events. Availability is expected in the coming months. AI: Unlocking the Power of Choice and Flexibility with Gemini and Agentforce Businesses need the freedom to choose the best models for their needs rather than be locked into one vendor. In 2025, Google’s Gemini models will also be available for prompt building and reasoning directly within Agentforce. With Gemini and Agentforce, businesses will benefit from: For example, an insurance customer can submit a claim with photos of the damage and an audio voicemail from a witness. Agentforce, using Gemini, can then help the insurance provider deliver better customer experiences by processing all these inputs, assessing the claim’s validity, and even using text-to-speech to contact the customer with a resolution, streamlining the traditionally lengthy claims process. Availability is expected this year. Trust: Salesforce Platform deployed on Google Cloud Customers will be able to use Salesforce’s unified platform (Agentforce, Data Cloud, Customer 360) on Google Cloud’s highly secure, AI-optimized infrastructure, benefiting from features like dynamic grounding, zero data retention, and toxicity detection provided by the Einstein Trust Layer. Once Salesforce products are available on Google Cloud, customers will also have the ability to procure Salesforce offerings through the Google Cloud Marketplace, opening up new possibilities for global businesses to optimize their investments across Salesforce and Google Cloud and benefiting thousands of existing joint customers. Action: Enhanced Employee Productivity and Customer Service with AI-Powered Integrations Millions use Salesforce and Google Cloud daily. This partnership prioritizes choice and flexibility, enabling seamless cross-platform work. New and deeper connections between platforms like Salesforce Service Cloud and Google Cloud’s Customer Engagement Suite, as well as Slack and Google Workspace, will empower AI agents and service representatives with unified data access, streamlined workflows, and advanced AI capabilities, regardless of platform. Salesforce and Google Cloud are deeply integrating their customer service platforms—Salesforce Service Cloud and Google Cloud’s Customer Engagement Suite—to create a seamless and intelligent support experience. Expected later this year, this unified approach empowers AI agents in Service Cloud with: Salesforce and Google Cloud are also exploring deeper integrations between Slack and Google Workspace, boosting productivity and creating a more cohesive digital workspace for teams and organizations. The companies are currently exploring use cases such as: Expanding Partnership Capabilities and Integrations This partnership goes beyond core product integrations to deliver a more connected and intelligent data foundation for businesses. Expected availability throughout 2025: This landmark partnership between Salesforce and Google represents a strategic paradigm shift in enterprise AI deployment, emphasizing infrastructure innovation, AI capability enhancement, and enterprise value. The integration of Google Search grounding provides a unique competitive advantage, offering real-time, factual responses backed by the world’s most comprehensive search engine. The companies are committed to ongoing innovation and deeper collaboration to empower businesses with even more powerful solutions. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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February 24, 2025in Agentic AI, Artificial Intelligence, Data, Enterprise, Google, Salesforce, Salesforce Commerce Cloud, Salesforce Service Cloud, Technology0 Comments
Neuro-symbolic AI
24Feb

Neuro-symbolic AI

Neuro-Symbolic AI: Bridging Neural Networks and Symbolic Processing for Smarter AI Systems Neuro-symbolic AI integrates neural networks with rules-based symbolic processing to enhance artificial intelligence systems’ accuracy, explainability, and precision. Neural networks leverage statistical deep learning to identify patterns in large datasets, while symbolic AI applies logic and rules-based reasoning common in mathematics, programming languages, and expert systems. The Balance Between Neural and Symbolic AIThe fusion of neural and symbolic methods has revived debates in the AI community regarding their relative strengths. Neural AI excels in deep learning, including generative AI, by distilling patterns from data through distributed statistical processing across interconnected neurons. However, this approach often requires significant computational resources and may struggle with explainability. Conversely, symbolic AI, which relies on predefined rules and logic, has historically powered applications like fraud detection, expert systems, and argument mining. While symbolic systems are faster and more interpretable, their reliance on manual rule creation has been a limitation. Innovations in training generative AI models now allow more efficient automation of these processes, though challenges like hallucinations and poor mathematical reasoning persist. Complementary Thinking ModelsPsychologist Daniel Kahneman’s analogy of System 1 and System 2 thinking aptly describes the interplay between neural and symbolic AI. Neural AI, akin to System 1, is intuitive and fast—ideal for tasks like image recognition. Symbolic AI mirrors System 2, engaging in slower, deliberate reasoning, such as understanding the context and relationships in a scene. Core Concepts of Neural NetworksArtificial neural networks (ANNs) mimic the statistical connections between biological neurons. By modeling patterns in data, ANNs enable learning and feature extraction at different abstraction levels, such as edges, shapes, and objects in images. Key ANN architectures include: Despite their strengths, neural networks are prone to hallucinations, particularly when overconfident in their predictions, making human oversight crucial. The Role of Symbolic ReasoningSymbolic reasoning underpins modern programming languages, where logical constructs (e.g., “if-then” statements) drive decision-making. Symbolic AI excels in structured applications like solving math problems, representing knowledge, and decision-making. Algorithms like expert systems, Bayesian networks, and fuzzy logic offer precision and efficiency in well-defined workflows but struggle with ambiguity and edge cases. Although symbolic systems like IBM Watson demonstrated success in trivia and reasoning, scaling them to broader, dynamic applications has proven challenging due to their dependency on manual configuration. Neuro-Symbolic IntegrationThe integration of neural and symbolic AI spans a spectrum of techniques, from loosely coupled processes to tightly integrated systems. Examples of integration include: History of Neuro-Symbolic AIBoth neural and symbolic AI trace their roots to the 1950s, with symbolic methods dominating early AI due to their logical approach. Neural networks fell out of favor until the 1980s when innovations like backpropagation revived interest. The 2010s saw a breakthrough with GPUs enabling scalable neural network training, ushering in today’s deep learning era. Applications and Future DirectionsApplications of neuro-symbolic AI include: The next wave of innovation aims to merge these approaches more deeply. For instance, combining granular structural information from neural networks with symbolic abstraction can improve explainability and efficiency in AI systems like intelligent document processing or IoT data interpretation. Neuro-symbolic AI offers the potential to create smarter, more explainable systems by blending the pattern-recognition capabilities of neural networks with the precision of symbolic reasoning. As research advances, this synergy may unlock new horizons in AI capabilities. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more 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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February 24, 2025in Artificial Intelligence, Data, Generative AI, Google0 Comments
23Feb

Transforming Customer Service with Voice AI

Transforming Customer Service with Voice AI: Moving Beyond Outdated IVR Systems When customers need support, they still overwhelmingly turn to the phone — voice is used in 77% of all customer interactions. Despite the rise of digital channels, the simplicity and immediacy of speaking to a human remain unmatched, especially for complex or time-sensitive issues. Yet, for many businesses, phone support remains tied to outdated Interactive Voice Response (IVR) systems, which often frustrate customers instead of resolving their issues. In fact, 68% of customers report dissatisfaction with traditional IVR systems, citing their inability to handle complex requests, rigid menu structures, and lack of personalization. The result? Customers frequently press “0” just to bypass the system and speak with a human agent — negating the very purpose of automation. But now, Voice AI is changing that dynamic. Unlike traditional IVRs, Voice AI leverages conversational intelligence to engage customers in natural, human-like dialogues. It understands context, processes complex requests, and delivers personalized solutions — all while learning and improving over time. The result is faster resolutions, higher customer satisfaction, and a dramatically reduced workload for human agents. Why Traditional IVR Systems Fall Short Despite their widespread use, IVR systems are riddled with limitations that negatively impact both customer experience and operational efficiency. 1. High Call Deflection Rates Traditional IVR systems often lead to high call deflection rates, where customers immediately press “0” to bypass the system and speak to a human. This happens because menu-based prompts rarely address complex queries, forcing customers through frustrating navigation loops. 2. Rigid Menu Structures IVRs operate through predefined, menu-driven interactions, limiting customers to a small set of options. This structure fails to accommodate complex, multi-faceted issues, resulting in customers being transferred between departments or disconnected mid-call. 3. Poor Integration with Business Systems Many IVRs lack seamless integration with CRM, billing, or order management systems, preventing agents from accessing real-time data. As a result, customers are often forced to repeat information or receive outdated or inaccurate responses. 4. Limited Problem-Solving Capabilities Traditional IVRs are only capable of handling simple, repetitive tasks — like checking an account balance or resetting a password. For complex issues that require critical thinking, IVRs fall short, ultimately requiring human intervention. 5. Lack of Personalization IVRs treat every customer interaction the same. Without access to customer history or context, the experience feels generic and impersonal, leaving customers dissatisfied. Voice AI: The New Standard for Customer Service Voice AI transforms phone-based support by enabling natural, human-like conversations. Built on large language models (LLMs) and conversational AI, Voice AI can listen, understand, and resolve customer requests — in real time — without requiring human assistance. Here’s how Voice AI elevates the customer experience: ✅ Conversational Interactions (Not Menu-Driven) Unlike IVRs, Voice AI agents engage in fluid, natural dialogues with customers. Instead of listening to long menu prompts, customers can simply state their problem in their own words, and the AI will interpret, process, and respond accordingly. For example, a customer might say:👉 “I need to change my shipping address.”The Voice AI will: No menus. No buttons. Just fast, human-like conversations. ✅ Real-Time Data Access Voice AI integrates seamlessly with CRM platforms, order management systems, and billing tools, allowing it to pull real-time customer information. This means: This significantly reduces resolution times and minimizes the need for human escalation. ✅ Smart Escalation for Complex Cases When Voice AI encounters an issue it cannot resolve, it automatically escalates the call to a live agent — with full context of the conversation. This eliminates the need for customers to repeat themselves and ensures a seamless handoff to human support. Additionally, Voice AI can analyze customer sentiment, detecting frustration or urgency. For example: ✅ Continuous Learning and Improvement Unlike IVRs, Voice AI gets smarter over time. Every interaction feeds the AI model, allowing it to improve response accuracy, anticipate common issues, and enhance the overall customer experience. This self-learning capability reduces the workload on human agents while continually improving resolution rates. Key Benefits of Voice AI in Customer Service 🚀 Faster Resolution Times By eliminating menu-based navigation and enabling natural conversations, Voice AI resolves common customer issues in minutes, not hours. 📉 Reduced Call Transfers Voice AI minimizes the need for customers to repeat themselves or get transferred between departments, significantly improving first-call resolution rates. 🎯 Personalized Customer Experiences With access to customer history and real-time data, Voice AI can offer tailored solutions — enhancing customer satisfaction and building long-term loyalty. 📊 Scalable, 24/7 Support Unlike human agents, Voice AI can handle hundreds of concurrent calls at any hour of the day, ensuring consistent, high-quality support without increasing operational costs. Real-World Use Cases of Voice AI 1. Customer Service Automation Forward-thinking companies are using Voice AI agents to handle routine tasks like: But beyond routine tasks, Voice AI excels at resolving complex issues, like: This dramatically reduces wait times and call volumes, while ensuring faster and more effective resolutions. 2. Sentiment Analysis & Real-Time Insights Voice AI can analyze the tone and sentiment of a caller’s voice to identify frustration, urgency, or dissatisfaction. In real-time, it can: 3. Multilingual Support Voice AI supports multiple languages, allowing businesses to scale their customer service globally. Whether the caller speaks English, Spanish, or French, Voice AI can understand, respond, and resolve issues without language barriers. The Future of Customer Service is Voice AI Customer expectations have shifted — they want fast, human-like support without long wait times or clunky IVR menus. Voice AI delivers exactly that. By replacing outdated IVR systems with intelligent, conversational Voice AI, businesses can: The future of customer service doesn’t lie in pressing buttons — it lies in natural, seamless conversations powered by AI. Companies that embrace Voice AI now will not only meet rising customer expectations but will also drive significant efficiency gains across their operations. ✅ Ready to transform your customer support with Voice AI?Learn how Voice AI can help you reduce call times, increase first-call resolutions, and improve customer satisfaction — all while reducing

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February 23, 2025in Data0 Comments
salesforce agentforce rapid deployment
21Feb

Businesses Face New Challenges

Businesses Face New Challenges: AI as the Key to Better Customer Experiences and EfficiencyModern businesses are under growing pressure to deliver exceptional customer experiences while boosting operational efficiency. To meet these demands, companies are turning to AI-powered solutions at an unprecedented pace. According to Capgemini’s 2024 Report on Harnessing the Value of Generative AI, 82% of organizations plan to integrate autonomous agents into their operations within the next one to three years. Agentforce: Salesforce’s Groundbreaking SolutionDriving this transformation is Salesforce’s Agentforce, launched in late 2024. This cutting-edge platform empowers businesses to build autonomous applications capable of handling customer interactions, automating operational tasks, and enabling employees to focus on strategic priorities. Beyond Chatbots: What Sets Agentforce Apart Unlike traditional chatbots or systems reliant on manual input, Agentforce acts autonomously. It retrieves relevant data, devises actionable plans, and executes tasks seamlessly. Equipped with real-time data capabilities, it adapts dynamically while maintaining compliance with secure, customizable guidelines. Agentforce not only performs tasks efficiently but also ensures contextually relevant and insightful interactions. It transitions tasks to human employees when necessary, providing summarized interactions and actionable recommendations to ensure smooth handoffs. Revolutionizing Customer Service: 24/7 Availability Without Delays Agentforce elevates customer service by engaging with users across various communication channels using natural language. It draws from trusted sources such as CRM systems, internal knowledge bases, and external platforms to deliver accurate and timely responses. For example, customers can use Agentforce to track orders, reschedule appointments, or resolve issues via platforms like WhatsApp or Apple Business Chat. By managing routine inquiries, Agentforce allows human agents to focus on complex, high-empathy issues requiring critical thinking. Supporting Sales Teams: From Lead Nurturing to Closing Deals Sales teams often face time constraints, and Agentforce addresses this by autonomously managing repetitive tasks such as answering product questions, scheduling meetings, and following up with leads. This allows sales professionals to concentrate on high-value deals. Agentforce can also act as an AI sales coach, using CRM data to simulate role-playing scenarios tailored to specific opportunities. This enables sales teams to refine skills like negotiation and objection handling. Notably, organizations that invest in sales coaching report a 16.7% revenue increase, even with minimal managerial input. With Agentforce, this process becomes scalable, offering real-time insights and actionable feedback to enhance performance. Transforming E-Commerce: Personalized Shopping Experiences Agentforce reshapes e-commerce by delivering personalized shopping experiences. Buyer Agents assist customers with natural-language product searches, offering tailored recommendations and enabling conversational reorders via mobile platforms. For returning customers, this creates a seamless, convenient experience. For larger-scale operations, Merchant Agents leverage conversational interfaces to create promotions, analyze store performance, and recommend strategies for improving key metrics. Streamlining Marketing Campaigns with Agentforce Agentforce’s Campaign Agent redefines marketing by automating every stage of the campaign lifecycle. From generating campaign briefs and audience segments to creating personalized content and building customer journeys in Salesforce Flow, the Campaign Agent accelerates workflows with unmatched precision. Using real-time analytics, the Campaign Agent monitors performance and identifies underperforming areas, offering proactive recommendations to optimize campaigns. This eliminates reliance on manual adjustments and ensures campaigns remain agile and effective. Focusing on Strategic Work A key advantage of Agentforce is its ability to handle repetitive tasks, freeing employees to focus on more complex and strategic activities. Whether addressing intricate customer needs, negotiating major deals, or developing innovative strategies, employees can dedicate their energy to driving long-term success. By automating routine processes and providing actionable insights, Agentforce not only enhances operational efficiency but also boosts employee satisfaction. Salesforce’s Bold Vision Salesforce CEO Marc Benioff shared the company’s ambitious goal: “Our vision is to empower one billion agents with Agentforce by the end of 2025. This is what AI is meant to be.” This statement underscores Salesforce’s commitment to delivering transformative AI solutions with tangible impact for businesses worldwide. 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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February 21, 2025in Agentic AI, Data, Generative AI, Salesforce, Salesforce Flow0 Comments
Build Launch and Track Campaigns
20Feb

How to Create Professional Meeting Minutes Without MS Co-Pilot

Ever wondered how to draft professional meeting minutes without relying on MS Co-Pilot? While tools like Microsoft Teams can record meetings and generate transcripts, they often come with limitations. For instance, MS Teams requires an MS Co-Pilot subscription to analyze transcripts and create meeting minutes, and even with that, crafting effective prompts for such tools is essential for generating useful outputs. Recently, a colleague sent a meeting recording—without a transcript—and asked us to create the minutes. Here’s how we accomplished this task, step by step. Step 1: Transcribing the Meeting Recording Since AI models cannot directly process audio or video, the first step was to generate a text transcript of the recording. I used Microsoft Word’s Dictate → Transcribe feature, but encountered a roadblock: the recording exceeded the tool’s 300MB file size limit (it was 550MB). To bypass this, I extracted the audio from the video using VLC Media Player, a versatile media tool: With the audio file ready, I returned to Microsoft Word. This time, the smaller file successfully transcribed into a 45-page text document of decent quality. Step 2: Crafting a Prompt for Meeting Minutes Creating effective meeting minutes with an AI model requires a detailed, structured prompt. Think of it as giving precise instructions to a chef—vagueness leads to unsatisfactory results. I started with a simple XML-style prompt for ChatGPT (GPT-4), using tags to organize key elements: plaintextCopyEditYou are an expert in creating meeting minutes from a given transcript. Analyze the provided transcript and generate professional meeting minutes with the specified structure. <transcript> {{meeting_transcript.docx}} </transcript> <structure> – Main Points Discussed – Decisions, Resolutions, and Agreements – Summary of Differing Opinions (if any) – Action Items: Tasks assigned, responsible parties, and deadlines – Follow-Ups: Topics to revisit in future meetings </structure> <instructions> – Stick strictly to the transcript content. – Do not invent or infer information. – Keep the minutes objective, factual, and concise. – Ensure clarity and self-containment for future reference. </instructions> This prompt acted as a baseline, providing clarity and structure for the model to extract and summarize relevant details from the transcript. Step 3: Refining the Prompt Using Anthropic’s Workbench To improve the clarity and effectiveness of the prompt, I used Anthropic’s Workbench, which offers an automatic prompt enhancement tool. The goal was to refine the structure and optimize the instructions. Here’s the improved version generated by Anthropic: plaintextCopyEditYou are an expert in creating professional meeting minutes from transcripts. Analyze the provided transcript and organize the information systematically before drafting the minutes. <meeting_transcript> {{meeting_transcript.docx}} </meeting_transcript> <analysis_structure> 1. Main Points Discussed: – Key topics with relevant quotes from the transcript. 2. Decisions and Agreements: – Summary of resolutions with supporting quotes. 3. Differing Opinions (if any): – Notable disagreements or alternative viewpoints. 4. Action Items: – Tasks, responsible parties, and deadlines. 5. Follow-Up Topics: – Issues or items to revisit in future meetings. </analysis_structure> <guidelines> – Follow the analysis structure before drafting the final minutes. – Use clear, concise language and a professional tone. – Avoid unnecessary details and stick to transcript content. – Ensure the minutes are self-contained and explanatory. </guidelines> This enhanced prompt incorporated a “chain-of-thought” methodology, guiding the model to analyze and organize the information step by step before drafting the final minutes. Exploring Other Tools: OpenAI’s Prompt Improver I also tested OpenAI’s Prompt Improver in its Chat Playground, which generated a similarly refined prompt: plaintextCopyEditCreate professional meeting minutes from the provided transcript. Use the following structure and guidelines to ensure accuracy and clarity: **Transcript:** – File: {{meeting_transcript.docx}} **Structure:** – Main Points Discussed – Decisions and Agreements – Differing Opinions (if any) – Action Items – Follow-Up Topics **Instructions:** – Maintain objectivity and stick to the transcript content. – Use concise yet explanatory language. – Adhere strictly to the structure for clarity and reference. – Avoid unnecessary embellishments or personal insights. **Output Format:** – Use bullet points for clarity, with no more than one level of indentation. – Ensure the minutes are self-contained and useful for future reference. While effective, OpenAI’s output lacked the chain-of-thought methodology and example formatting provided by Anthropic’s tool, which resulted in less structured meeting minutes. Key Takeaways By following this approach, you can produce professional meeting minutes efficiently—no MS Co-Pilot subscription required. 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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February 20, 2025in AI Tools, Data, Enterprise0 Comments
Navigating the CRM Split for Drugmakers
20Feb

Navigating the CRM Split for Drugmakers

Salesforce vs. Veeva: Navigating the CRM Split for Drugmakers The long-standing partnership between Salesforce and Veeva is coming to an end, forcing pharmaceutical companies to decide which platform best suits their evolving needs. A Strategic Decision, Not Just an IT Shift As the contract between the two companies expires this September, drugmakers have until 2030 to choose their path. While some view the shift as a simple migration, industry leaders warn that this decision carries deeper strategic implications. “Sometimes this is being seen as just an IT migration—but no, if you’re just migrating, you’re missing the strategic importance of this,” said Nancy Phelan, SVP and Head of Customer Engagement at Trinity Life Sciences. “Leaders are realizing this is a much bigger decision, requiring thoughtful consideration of timing, approach, and long-term business impact.” A Messy Divorce? In some ways, the split has turned into a battle, with both companies scrambling to win over clients. By the end of December, Salesforce had reportedly poached several major customers from Veeva, which currently holds around 80% market share in life sciences. Both companies are adapting to drastic changes in the healthcare landscape, including an explosion of data, increasingly complex therapies, and evolving customer needs. From what Phelan has observed, drugmakers aren’t gravitating toward one side or the other based on company size, pipeline, or core focus. Instead, both platforms offer distinct advantages that could shape the user experience in different ways. Why the Split? Veeva’s decision to leave the Salesforce platform stems from mounting limitations and risks that made a standalone approach more appealing. According to a report by Everest Group, the separation will shrink Salesforce’s footprint in life sciences, but its broader market presence may fuel faster development of next-generation technologies. Veeva, on the other hand, is doubling down on its industry-specific capabilities, aiming to enhance its tailored solutions for pharma and biotech companies. A Changing Landscape For nearly two decades, Salesforce and Veeva have been intertwined, with Veeva building its life sciences CRM on Salesforce’s platform. Now, both companies are introducing new solutions, reflecting shifts in the pharmaceutical business model. “Companies like Pfizer or Novartis last made this decision more than 15 years ago,” Phelan noted. “Back then, specialty pharmacy complexities, field reimbursement challenges, and patient affordability concerns weren’t as prominent as they are today.” Additionally, the rise of AI and big data analytics has transformed the role of CRM platforms, making the Salesforce-Veeva decision more complex than ever. Two Roads, Two Strategies The key difference between the platforms moving forward will be how they align with drugmakers’ priorities: What’s Next for Pharma? As the transition nears, both Veeva and Salesforce are putting their best foot forward. Fortunately, pharma companies still have time to evaluate their options. “How we’re advising companies is, you’ve got a window of time and a future that is radically different from the last time you made this decision,” Phelan said. “You need to strategically assess the pieces that are important to you.” With the deadline approaching, drugmakers must determine which path aligns best with their long-term vision. 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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February 20, 2025in Data, Life Sciences Cloud, Salesforce0 Comments
Scope of Generative AI
19Feb

Generative AI Game Changer for Cybersecurity

Generative AI: A Game Changer for Cybersecurity—Both Good and Bad Generative AI is revolutionizing cybersecurity, enabling both cybercriminals and defenders to operate faster, smarter, and at a larger scale. How Hackers Leverage GenAI Cybercriminals are using generative AI to: One real-world example: In early 2024, fraudsters used a deepfake of a multinational company’s CFO to trick an employee into transferring $25 million. How Cybersecurity Teams Use GenAI for Defense Enterprise security teams are adopting generative AI to: According to a 2024 CrowdStrike survey, 64% of cybersecurity professionals are already researching or using AI tools, with 69% planning to invest further within a year. The Risks of AI in Cybersecurity Despite its benefits, AI introduces new risks: Security leaders must balance AI adoption with human oversight to maximize its defensive potential while minimizing unintended risks. As AI continues to shape the cybersecurity landscape, both attackers and defenders must adapt to stay ahead. 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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February 19, 2025in AI Tools, Data, Enterprise, Generative AI, Laws0 Comments
$15 Million to AI Training for U.S. Government Workforce
19Feb

AI Adoption in the Federal Government

AI Adoption in the Federal Government: A New Era Under the Trump Administration With a new administration in Washington and a $500 billion AI infrastructure initiative underway, the U.S. federal government may be entering a phase of accelerated AI adoption. Federal AI Expansion AI adoption grew under the Biden administration, with agencies leveraging it for fraud detection, workflow automation, and data analysis. However, experts predict that the Trump administration will further expand federal AI use. “Trump and his advisers have spoken about ‘unleashing AI,’ signaling a push for broader adoption within government agencies,” said Darrell West, a senior fellow at the Brookings Institution’s Center for Technology Innovation. As the administration scales back AI safety regulations and deepens ties with major tech firms, federal AI usage is expected to rise. However, ensuring transparency and educating the public remain crucial for building trust in government AI applications. AI Governance Framework The foundation for federal AI governance was established under Trump’s first term, with executive orders EO 13859 (2019) and EO 13960 (2020). EO 13960 mandated an annual AI use case inventory, significantly expanding under Biden—from 710 cases in 2023 to 2,133 in 2024. Reggie Townsend, VP of Data Ethics at SAS and a National AI Advisory Committee (NAIAC) member, emphasized the importance of this transparency: “The inventory was a crucial first step in building public trust.” Biden’s EO 14110 (2023) introduced stronger AI guardrails, requiring agencies to designate chief AI officers, disclose safety-related AI use cases, and implement risk management guidelines. However, on his first day in office, Trump rescinded EO 14110, signaling a shift toward deregulation. AI Applications in Government The 2024 federal AI inventory reported 2,133 AI use cases across 41 agencies. The Department of Health and Human Services (HHS) led with 271 cases, reflecting a 66% increase from the previous year. Key applications include: Harvard Kennedy School adjunct lecturer Bruce Schneier anticipates even broader AI integration in government, from automating reports to drafting legislation and conducting audits. Despite growing interest, the federal government lags behind the private sector in AI adoption, especially for generative AI, due to concerns over bias, reliability, and transparency. AI Under a Second Trump Term Trump’s return to office in 2025 signals an AI policy shift favoring reduced oversight and enhanced global AI leadership. “Federal AI adoption will accelerate under Trump,” West said, citing efforts to integrate major tech figures into federal initiatives. Notably, Trump appointed xAI owner Elon Musk to lead the newly rebranded Department of Government Efficiency, formerly the U.S. Digital Service. This agency is tasked with modernizing federal technology, reducing costs, and driving deregulation. With EO 14110 rescinded, the scope of AI governance under Trump remains uncertain. “Will he eliminate all guardrails, or keep some protections? That’s something to watch,” West noted. Big Tech’s Role in Federal AI Trump’s inauguration underscored tech industry influence, with Elon Musk, Mark Zuckerberg, Jeff Bezos, and Sundar Pichai in attendance. Major tech firms, including Amazon, Google, and Microsoft, each contributed $1 million to the event, while OpenAI CEO Sam Altman made a personal $1 million donation. Some companies are aligning with the administration’s stance on AI and content moderation. Meta, for instance, has replaced its fact-checking services with a community-driven model similar to X’s Community Notes and relaxed its moderation policies. A deregulated AI landscape could benefit big tech, particularly in areas like AI safety standards and data copyright issues, while advancing the administration’s vision for U.S. AI dominance. AI’s Future in Government On his second day in office, Trump announced a $500 billion AI infrastructure investment, forming Stargate—a coalition of OpenAI, SoftBank, MGX, and Oracle—to expand AI infrastructure nationwide. “This will be the largest AI infrastructure project in history,” Trump declared, emphasizing the need for AI leadership against global competitors like China. However, West warned that accelerated adoption must be managed carefully: “It’s critical that AI is implemented fairly, with privacy and security safeguards in place.” Building AI Literacy Effective AI deployment requires education within federal agencies. “Many government workers lack AI expertise, making it difficult to procure and implement AI solutions effectively,” West said. NAIAC’s Townsend advocates for structured AI training, tailored to different federal roles. Public AI literacy is also crucial, with initiatives like the National AI Research Resource (NAIRR) promoting equitable access to AI education and development. “The public must be informed enough to hold the government accountable on AI issues,” Townsend concluded. As AI adoption accelerates, striking a balance between innovation, oversight, and public trust will define the next phase of federal AI policy. 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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February 19, 2025in Data, Generative AI, Google, Public Sector Solutions, Technology0 Comments
ai trust layer
18Feb

Gen AI Trust Layers

Addressing the Generative AI Production Gap with Trust Layers Despite the growing excitement around generative AI, only a small percentage of projects have successfully moved into production. A key barrier is the persistent concern over large language models (LLMs) generating hallucinations—responses that are inconsistent or completely disconnected from reality. To address these issues, organizations are increasingly adopting AI trust layers to enhance reliability and mitigate risk. Understanding the Challenge Generative AI models, like LLMs, are powerful tools trained on vast amounts of unstructured data, enabling them to answer questions and complete tasks based on text, documents, recordings, images, and videos. This capability has revolutionized the creation of chatbots, co-pilots, and even semi-autonomous agents. However, these models are inherently non-deterministic, meaning they don’t always produce consistent outputs. This lack of predictability leads to the infamous phenomenon of hallucination—what the National Institute of Standards and Technology (NIST) terms “confabulation.” While hallucination is a byproduct of how generative models function, its risks in mission-critical applications cannot be ignored. Implementing AI Trust Layers To address these challenges, organizations are turning to AI trust layers—frameworks designed to monitor and control generative AI behavior. These trust layers vary in implementation: Galileo: Building AI Trust from the Ground Up Galileo, founded in 2021 by Yash Sheth, Atindriyo Sanyal, and Vikram Chatterji, has emerged as a leader in developing AI trust solutions. Drawing on his decade of experience at Google building LLMs for speech recognition, Sheth recognized early on that non-deterministic AI systems needed robust trust frameworks to achieve widespread adoption in enterprise settings. The Need for Trust in Mission-Critical AI “Sheth explained: ‘Generative AI doesn’t give you the same answer every time. To mitigate risk in mission-critical tasks, you need a trust framework to ensure these models behave as expected in production.’ Enterprises, which prioritize privacy, security, and reputation, require this level of assurance before deploying LLMs at scale. Galileo’s Approach to Trust Layers Galileo’s AI trust layer is built on its proprietary foundation model, which evaluates the behavior of target LLMs. This approach is bolstered by metrics and real-time guardrails to block undesirable outcomes, such as hallucinations, data leaks, or harmful outputs. Key Products in Galileo’s Suite Sheth described the underlying technology: “Our evaluation foundation models are dependable, reliable, and scalable. They run continuously in production, ensuring bad outcomes are blocked in real time.” By combining these components, Galileo provides enterprises with a trust layer that gives them confidence in their generative AI applications, mirroring the reliability of traditional software systems. From Research to Real-World Impact Unlike vendors who quickly adapted traditional machine learning frameworks for generative AI, Galileo spent two years conducting research and developing its Generative AI Studio, launched in August 2023. This thorough approach has started to pay off: A Crucial Moment for AI Trust Layers As enterprises prepare to move generative AI experiments into production, trust layers are becoming essential. These frameworks address lingering concerns about the unpredictable nature of LLMs, allowing organizations to scale AI while minimizing risk. Sheth emphasized the stakes: “When mission-critical software starts becoming infused with AI, trust layers will define whether we progress or regress to the stone ages of software. That’s what’s holding back proof-of-concepts from reaching production.” With Galileo’s innovative approach, enterprises now have a path to unlock the full potential of generative AI—responsibly, securely, and at 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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February 18, 2025in Data, Enterprise, Generative AI, Google, Salesforce, Technology0 Comments
ChatGPT 5.0 is Coming
17Feb

ChatGPT Search

OpenAI’s ChatGPT Search: Everything You Need to Know ChatGPT Search is OpenAI’s generative AI-powered search engine, designed to provide real-time information while eliminating the limitations of traditional language models’ knowledge cutoffs. It combines conversational AI with real-time web search, offering up-to-date insights, summaries, and more. Here’s a deep dive into what makes ChatGPT Search unique and how it compares to existing solutions like Google. Overcoming Knowledge Cutoffs Earlier iterations of OpenAI’s models, like GPT-4 (October 2023 cutoff) and GPT-3 (September 2021 cutoff), lacked the ability to access real-time data, a significant drawback for users seeking the latest information. By integrating live search capabilities, ChatGPT Search resolves this issue. Unlike traditional search engines like Google, which continuously crawl and update web indexes, ChatGPT combines the strengths of its GPT-4o model with live web access, bridging the gap between generative AI and real-time search. What Is ChatGPT Search? Launched on October 31, 2024, after being prototyped as “SearchGPT,” ChatGPT Search pairs OpenAI’s advanced language models with live web search. Initially available to ChatGPT Plus and Team users, it will expand to Enterprise, Education, and free-tier users by early 2025. Key Features of ChatGPT Search How Does It Work? ChatGPT Search leverages the following technologies: Accessing ChatGPT Search ChatGPT Search is accessible through multiple platforms: Why ChatGPT Search Challenges Google While Google dominates the search market, OpenAI’s ChatGPT Search introduces key differentiators: AI-Powered Search Engine Comparison Search Engine Platform Integration Publisher Collaboration Ads Cost ChatGPT Search OpenAI infrastructure Strong media partnerships Ad-free Free (Premium tiers planned) Google AI Overviews Google infrastructure SEO-focused partnerships Ads included Free Bing AI Microsoft infrastructure SEO-focused partnerships Ads included Free Perplexity AI Independent, standalone Basic attribution Ad-free Free; $20/month premium You.com Multi-mode AI assistant Basic attribution Ad-free Free; premium available Brave Search Independent index Basic attribution Ad-free Free The Roadmap for ChatGPT Search OpenAI has ambitious plans to refine and expand ChatGPT Search, including: Conclusion ChatGPT Search marks a pivotal shift in how users interact with AI and access information. By combining the generative power of GPT-4o with real-time search, OpenAI has created a tool that rivals traditional search engines with conversational AI, summarized insights, and ad-free functionality. As OpenAI continues to refine the platform, ChatGPT Search is poised to redefine the way we find and interact with information—offering a glimpse into the future of search. 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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February 17, 2025in Data, Enterprise, Generative AI, Google0 Comments
deepseek deep dive
15Feb

DeepSeek iOS App Poses Major Privacy Risks

Security Alert: DeepSeek iOS App Poses Major Privacy Risks Cybersecurity researchers at NowSecure have issued a stark warning about the iOS version of DeepSeek, currently the third most popular app on the App Store. Their analysis reveals serious security flaws, making the app a major privacy risk that users should delete immediately. According to NowSecure’s findings, DeepSeek: Additionally, DeepSeek relies on ByteDance’s Volcano Engine, tying it to TikTok’s parent company, further raising privacy and regulatory concerns. For personal devices, this poses a significant security risk. For company-owned iPhones, the risks are even greater, especially regarding data privacy and compliance. US Regulators Take Action DeepSeek’s security risks have drawn scrutiny from U.S. lawmakers concerned about national security and data privacy. Representatives Josh Gottheimer (D-NJ) and Darin LaHood (R-IL) have introduced the No DeepSeek on Government Devices Act, seeking to ban the app from government-issued phones. While the full text of the bill is not yet available, legislators cite research indicating that DeepSeek’s code is “directly linked to the Chinese Communist Party” and capable of transmitting user data to China Mobile, a Chinese state-owned telecom firm sanctioned by the U.S. For those concerned about data security, the safest approach is to remove DeepSeek from your device and, if necessary, switch to a locally-run model that does not transmit data externally. HPE Warns Employees of Data Breach Meanwhile, Hewlett Packard Enterprise (HPE) has notified employees of a nation-state attack that may have compromised personal data. In a letter sent to staff, HPE disclosed that an unauthorized party accessed its cloud email environment, potentially exposing employee information. While the impact appears limited—only ten employees were affected, according to Massachusetts’ data breach report—the breach raises concerns about targeted cyberattacks on enterprise tech firms. HPE had previously disclosed a similar attack in January 2024, attributing it to Russia’s Cozy Bear hacking group, which is known for infiltrating high-profile networks. Reports suggest this latest breach also targeted Microsoft Office 365 accounts, highlighting ongoing threats to corporate cloud environments. Takeaway From DeepSeek’s security risks to HPE’s cyberattack, these incidents underscore the importance of data privacy, secure app usage, and robust enterprise security measures. Whether for personal or corporate security, staying informed and taking proactive steps is critical in today’s evolving digital landscape. 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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February 15, 2025in Data, Enterprise, Laws0 Comments
The Hidden Risks of Over-Reliance on AI
14Feb

The Hidden Risks of Over-Reliance on AI

Are Marketers Trusting AI Too Much? How to Avoid Losing Your Strategic Edge AI tools have revolutionized how marketers approach research, content creation, and decision-making. However, an overreliance on these tools could undermine critical thinking and strategic planning, leaving marketers vulnerable in a fast-evolving landscape. Here’s how to balance the power of automation with human insight. The Rise of AI in Search and Marketing In late December, SEO consultancy Previsible shared a striking report: Google’s search dominance has plateaued and is now being challenged by AI-assisted search tools. These tools, such as ChatGPT, Claude, and Google’s own AI-enhanced search, are growing in popularity due to their ability to deliver contextually relevant and personalized results. Unlike traditional search, which relies on keyword matching, AI-driven search processes intent and context. This shift is reshaping how users find information and make decisions. How AI Is Changing User Behavior The increasing sophistication of AI tools brings both opportunities and risks. Users often trust AI-generated outputs without question, assuming they’re accurate and complete. Traditional search, by contrast, forces users to critically analyze and filter multiple sources. This blind trust in AI mirrors the concept of “System 1 thinking,” as described by Nobel Prize-winning psychologist Daniel Kahneman in Thinking, Fast and Slow. As AI models like ChatGPT operate primarily as “System 1 thinkers,” users risk adopting a similar approach, bypassing critical analysis in favor of convenience. The Hidden Risks of Over-Reliance on AI Younger marketers may be especially at risk of falling into this trap. Many are using AI tools like ChatGPT to summarize information or generate ideas, often without questioning the accuracy of the outputs. For B2B marketers, the allure of AI lies in its speed and perceived accuracy. However, this reliance on automation could lead to a generation of marketers who lack the ability—or inclination—to think strategically. The danger is clear: unchecked dependence on AI tools could foster a “groupthink” mentality, where creativity and critical thinking are sidelined. Without intervention, marketing departments risk becoming overly reliant on tools that were designed to enhance human efforts, not replace them. How Marketing Leaders Can Address This Threat To counter this trend, marketing leaders must actively promote the development of strategic skills. Here’s how: In a world increasingly driven by AI, marketers who can blend automation with strategic thinking will be best positioned for success. Using AI to Enhance, Not Replace, Strategic Thinking AI should empower marketers to make better decisions—not serve as the sole decision-maker. As one professor aptly put it, “Use AI to become a better student, not to be the student.” The key is balance. By combining the intuitive capabilities of AI with the deliberate, analytical approach of System 2 thinking, marketers can leverage technology without sacrificing creativity or strategy. In short, AI is a tool—not a replacement for human ingenuity. Those who recognize this distinction will thrive in an increasingly automated 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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February 14, 2025in AI Tools, Google, Technology0 Comments
No-Code Generative AI
11Feb

Generative-Driven Development

Nowhere has the rise of generative AI tools been more transformative than in software development. It began with GitHub Copilot’s enhanced autocomplete, which then evolved into interactive, real-time coding assistants like Aider and Cursor that allow engineers to dictate changes and see them applied live in their editor. Today, platforms like Devin.ai aim even higher, aspiring to create autonomous software systems capable of interpreting feature requests or bug reports and delivering ready-to-review code. At its core, the ambition of these AI tools mirrors the essence of software itself: to automate human work. Whether you were writing a script to automate CSV parsing in 2005 or leveraging AI today, the goal remains the same—offloading repetitive tasks to machines. What makes generative AI tools distinct, however, is their focus on automating the work of automation itself. Framing this as a guiding principle enables us to consider the broader challenges and opportunities generative AI brings to software development. Automate the Process of Automation The Doctor-Patient Strategy Most contemporary generative AI tools operate under what can be called the Doctor-Patient strategy. In this model, the GenAI tool acts on a codebase as a distinct, external entity—much like a doctor treats a patient. The relationship is one-directional: the tool modifies the codebase based on given instructions but remains isolated from the architecture and decision-making processes within it. Why This Strategy Dominates: However, the limitations of this strategy are becoming increasingly apparent. Over time, the unidirectional relationship leads to bot rot—the gradual degradation of code quality due to poorly contextualized, repetitive, or inconsistent changes made by generative AI. Understanding Bot Rot Bot rot occurs when AI tools repeatedly make changes without accounting for the macro-level architecture of a codebase. These tools rely on localized context, often drawing from semantically similar code snippets, but lack the insight needed to preserve or enhance the overarching structure. Symptoms of Bot Rot: Example:Consider a Python application that parses TPS report IDs. Without architectural insight, a code bot may generate redundant parsing methods across multiple modules rather than abstracting the logic into a centralized model. Over time, this duplication compounds, creating a chaotic and inefficient codebase. A New Approach: Generative-Driven Development (GDD) To address the flaws of the Doctor-Patient strategy, we propose Generative-Driven Development (GDD), a paradigm where the codebase itself is designed to enable generative AI to enhance automation iteratively and sustainably. Pillars of GDD: How GDD Improves the Development Lifecycle Under GDD, the traditional Test-Driven Development (TDD) cycle (red, green, refactor) evolves to integrate AI processes: This complete cycle eliminates the gaps present in current generative workflows, reducing bot rot and enabling sustainable automation. Over time, GDD-based codebases become easier to maintain and automate, reducing error rates and cycle times. A Day in the Life of a GDD Engineer Imagine a GDD-enabled workflow for a developer tasked with updating TPS report parsing: By embedding AI into the development process, GDD empowers engineers to focus on high-level decision-making while ensuring the automation process remains sustainable and aligned with architectural goals. Conclusion Generative-Driven Development represents a significant shift in how we approach software development. By prioritizing architecture, embedding automation into the software itself, and writing GenAI-optimized code, GDD offers a sustainable path to achieving the ultimate goal: automating the process of automation. As AI continues to reshape the industry, adopting GDD will be critical to harnessing its full potential while avoiding the pitfalls of bot rot. 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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February 11, 2025in AI Tools, Generative AI, Laws0 Comments

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