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15Apr

AI is Revolutionizing Clinical Trials and Drug Development

Clinical trials are a cornerstone of drug development, yet they are often plagued by inefficiencies, long timelines, high costs, and challenges in patient recruitment and data analysis. Artificial intelligence (AI) is transforming this landscape by streamlining trial design, optimizing patient selection, and accelerating data analysis, ultimately enabling faster and more cost-effective treatment development. Optimizing Clinical Trials A study by the Tufts Center for the Study of Drug Development estimates that bringing a new drug to market costs an average of $2.6 billion, with clinical trials comprising a significant portion of that expense. “The time-consuming process of recruiting the right patients, collecting data, and manually analyzing it are major bottlenecks,” said Mohan Uttawar, co-founder and CEO of OneCell. AI is addressing these challenges by improving site selection, patient recruitment, and data analysis. Leveraging historical data, AI identifies optimal sites and patients with greater efficiency, significantly reducing costs and timelines. “AI offers several key advantages, from site selection to delivering results,” Uttawar explained. “By utilizing past data, AI can pinpoint the best trial sites and patients while eliminating unsuitable candidates, ensuring a more streamlined process.” One compelling example of AI’s impact is Exscientia, which designed a cancer immunotherapy molecule in under 12 months—a process that traditionally takes four to five years. This rapid development highlights AI’s potential to accelerate promising therapies from concept to patient testing. Enhancing Drug Development Beyond clinical trials, AI is revolutionizing the broader drug development process, particularly in refining trial protocols and optimizing site selection. “A major paradigm shift has emerged with AI, as these tools optimize trial design and execution by leveraging vast datasets and streamlining patient recruitment,” Uttawar noted. Machine learning plays a crucial role in biomarker discovery and patient stratification, essential for developing targeted therapies. By analyzing large datasets, AI uncovers patterns and insights that would be nearly impossible to detect manually. “The availability of large datasets through machine learning enables the development of powerful algorithms that provide key insights into patient stratification and targeted therapies,” Uttawar explained. The cost savings of AI-driven drug development are substantial. Traditional computational models can take five to six years to complete. In contrast, AI-powered approaches can shorten this timeline to just five to six months, significantly reducing costs. Regulatory and Ethical Considerations Despite its advantages, AI in clinical trials presents regulatory and ethical challenges. One primary concern is ensuring the robustness and validation of AI-generated data. “The regulatory challenges for AI-driven clinical trials revolve around the robustness of data used for algorithm development and its validation against existing methods,” Uttawar highlighted. To address these concerns, agencies like the FDA are working on frameworks to validate AI-driven insights and algorithms. “In the future, the FDA is likely to create an AI-based validation framework with guidelines for algorithm development and regulatory compliance,” Uttawar suggested. Data privacy and security are also crucial considerations, given the vast datasets needed to train AI models. Compliance with regulations such as HIPAA, ISO 13485, GDPR, and 21CFR Part 820 ensures data protection and security. “Regulatory frameworks are essential in defining security, compliance, and data privacy, making it mandatory for AI models to adhere to established guidelines,” Uttawar noted. AI also has the potential to enhance diversity in clinical trials by reducing biases in patient selection. By objectively analyzing data, AI can efficiently recruit diverse patient populations. “AI facilitates unbiased data analysis, ensuring diverse patient recruitment in a time-sensitive manner,” Uttawar added. “It reviews selection criteria and, based on vast datasets, provides data-driven insights to optimize patient composition.” Trends and Predictions The adoption of AI in clinical trials and drug development is expected to rise dramatically in the coming years. “In the next five years, 80-90% of all clinical trials will likely incorporate AI in trial design, data analysis, and regulatory submissions,” Uttawar predicted. Emerging applications, such as OneCell’s AI-based toolkit for predicting genomic signatures from high-resolution H&E Whole Slide Images, are particularly promising. This technology allows hospitals and research facilities to analyze medical images and identify potential cancer patients for targeted treatments. “This toolkit captures high-resolution images at 40X resolution and analyzes them using AI-driven algorithms to detect morphological changes,” Uttawar explained. “It enables accessible image analysis, helping physicians make more informed treatment decisions.” To fully realize AI’s potential in drug development, stronger collaboration between AI-focused companies and the pharmaceutical industry is essential. Additionally, regulatory frameworks must evolve to support AI validation and standardization. “Greater collaboration between AI startups and pharmaceutical companies is needed,” Uttawar emphasized. “From a regulatory standpoint, the FDA must establish frameworks to validate AI-driven data and algorithms, ensuring consistency with existing standards.” AI is already transforming drug development and clinical trials, enhancing efficiencies in site selection, patient recruitment, and data analysis. By accelerating timelines and cutting costs, AI is not only making drug development more sustainable but also increasing access to life-saving treatments. However, maximizing AI’s impact will require continued collaboration among technology innovators, pharmaceutical firms, and the regulatory bodies. As frameworks evolve to ensure data integrity, security, and compliance, AI-driven advancements will further shape the future of precision medicine—ultimately improving patient outcomes and redefining healthcare. 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

April 15, 2025in Artificial Intelligence, Data, Technology0 Comments

23Mar

Unlocking Sales Potential with Data Activation

Selling has never been easy — and it’s not getting any simpler. Sales representatives are under constant pressure to research markets, navigate gatekeepers, and craft compelling pitches to win over decision-makers. But in today’s market, that’s not enough. Nearly 90% of business buyers expect personalized, insightful interactions — and delivering on that expectation requires more than persuasive messaging. It demands access to accurate, real-time data. The challenge? Sales reps often struggle to find the information they need. Instead of focusing on closing deals, they waste time chasing down customer data, piecing together fragmented insights, or working off outdated information. In fact: The root cause? Data silos. Data Silos are Crippling Sales Efficiency In most companies, critical customer data is scattered across: This fragmented data structure creates massive blind spots for sales teams. Consider this: The impact is costly — missed opportunities, slower deal cycles, and lost revenue. Without a unified approach to data management, sales teams remain limited by incomplete information, preventing them from delivering personalized, high-impact customer experiences. The Answer: Data Activation The solution isn’t just unifying your data — it’s activating it. Data activation means making your customer data accessible, actionable, and visible within your CRM so your sales team can use it in real-time. It eliminates the need to toggle between systems, request data from other teams, or work from static spreadsheets. Instead, activated data flows directly into the workflows and tools that your sales reps use every day — giving them everything they need to engage, sell, and close deals faster. Here’s what data activation looks like in practice: Data activation ensures that every team member works from the same, real-time, unified view of the customer — eliminating data silos and transforming sales productivity. Why Data Activation is a Game-Changer for Sales By bringing your unified data directly into your CRM, your sales team gains immediate access to valuable insights that drive better outcomes. Here are some powerful data types that become actionable through data activation: 1. Web Engagement Data Understand customer behavior based on their interactions with your website. Track which products or services they’ve browsed, downloaded, or engaged with — allowing your sales team to tailor conversations and offers accordingly. Use case: 2. Marketing Campaign Data Eliminate disjointed outreach by giving your sales team visibility into marketing campaigns. Sales reps can instantly see which emails, ads, or events a prospect engaged with — ensuring their outreach feels relevant, not redundant. Use case: 3. Consumption Data Track product usage, subscriptions, and consumption patterns from your ERP or product database. This data empowers sales reps to identify upsell and cross-sell opportunities or proactively prevent churn. Use case: 4. Unstructured Data (Emails, Call Logs, Chat Transcripts) Unlock insights from past customer interactions by analyzing emails, call center transcripts, chat logs, and even social media comments. Sales teams can use this data to understand sentiment, previous objections, and overall engagement history. Use case: 5. Billing and Subscription Data Integrate billing, purchase, and subscription information directly into your CRM. This allows sales reps to track contract renewals, upcoming billing cycles, or outstanding invoices — enabling more proactive and strategic outreach. Use case: 6. Third-Party Data for Enhanced Lead Scoring Enhance your lead scoring models with third-party data, such as firmographic information, buying intent signals, or demographic insights. This helps your team prioritize high-quality leads and drive faster conversions. Use case: Why Third-Party Data Tools Fall Short Many organizations attempt to solve their data challenges by investing in third-party data platforms like Snowflake, Databricks, or Redshift. While these tools excel at aggregating data, they introduce a new problem — they still create a data silo. The data sits outside of your CRM, meaning: This is why true data activation matters. It doesn’t just unify your data — it embeds it directly into your sales reps’ day-to-day tools, making insights instantly actionable. The Competitive Advantage of Data Activation By embracing data activation, your organization gains three major competitive advantages: ✅ 1. Increased Sales Productivity Sales reps no longer waste time tracking down information or switching between systems. With all customer data at their fingertips, they can spend more time building relationships and closing deals. ✅ 2. Enhanced Personalization at Scale With access to web behavior, campaign engagement, and product usage data, your team can personalize every interaction — at scale. This drives higher conversion rates and better customer experiences. ✅ 3. Smarter Forecasting and Planning By integrating billing, subscription, and past purchase data, sales managers gain accurate revenue forecasting and better visibility into growth opportunities. Activate Your Data. Unlock Your Revenue. The future of sales is not about more tools — it’s about better data accessibility. Data activation eliminates silos, unlocks powerful insights, and delivers real-time, actionable data directly into your CRM. This empowers your sales team to: The result? Faster sales, higher revenue, and exceptional customer experiences. Ready to activate your data and supercharge your sales performance? Start by bringing all your data — web, marketing, subscription, and service — directly into your CRM. Your sales team will thank you — and your revenue will show it. 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

March 23, 2025in Data, Snowflake0 Comments

18Mar

FormAssembly Gov Cloud Achieves FedRAMP High Impact Authorization

FormAssembly Gov Cloud Achieves FedRAMP High Impact Authorization, Strengthening Secure Data Collection for Federal Agencies FormAssembly, a leader in secure, forms-based data collection solutions, has announced that FormAssembly Gov Cloud is now FedRAMP High Impact Authorized, providing federal agencies and public sector organizations with a fully compliant, secure solution for data collection and process automation. FedRAMP (Federal Risk and Authorization Management Program) is a U.S. government initiative that standardizes security assessment and authorization for cloud-based technologies used by federal agencies. This milestone, achieved in partnership with FedHIVE, ensures agencies can confidently leverage FormAssembly’s no-code platform to streamline workflows while meeting the government’s most rigorous security and compliance requirements. Operating within a High Impact Virtualized Environment, FormAssembly Gov Cloud adheres to more than 420 security controls, safeguarding sensitive federal data and ensuring compliance with stringent federal regulations. “Achieving FedRAMP High Impact authorization underscores our commitment to providing government agencies with a secure, compliant solution for modernizing data collection,” said Jaineesh Davda, CIO at FormAssembly. “With FormAssembly Gov Cloud, agencies can replace outdated manual processes with automated workflows that enhance efficiency, improve citizen services, and ensure data integrity.” Empowering Federal Agencies with Secure, Automated Data Collection FormAssembly Gov Cloud is designed to meet the demanding security and compliance requirements of federally regulated environments. Agencies can confidently collect and manage Controlled Unclassified Information (CUI) while benefiting from advanced security features, including: ✅ Role-Based Access Control – Ensuring only authorized personnel access sensitive data.✅ Data Encryption – Protecting information in transit and at rest.✅ Comprehensive Audit Trails – Providing transparency and accountability in data handling. Streamlining Government Operations with Seamless Integrations Beyond security, FormAssembly Gov Cloud empowers agencies to transform manual, paper-based processes into efficient, automated workflows. Seamless integration with government-preferred platforms such as Salesforce Government Cloud, Microsoft 365, and Google Workspace enables agencies to build a secure, connected data ecosystem. With over 350 five-star reviews on the Salesforce AppExchange, FormAssembly remains the preferred digital form solution for organizations leveraging Salesforce, accelerating implementation, driving long-term adoption, and optimizing data collection at scale. Learn more about FormAssembly Gov Cloud [here] and find us on the FedRAMP Marketplace. About FormAssembly FormAssembly is a leading forms-based data collection platform trusted by organizations worldwide. Known for its ease of use, enterprise-grade security, and seamless Salesforce integration, FormAssembly empowers businesses in highly regulated industries—such as government, financial services, healthcare, higher education, and nonprofits—to streamline data workflows while maintaining strict compliance. To learn more, visit www.formassembly.com. 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

March 18, 2025in Data, Enterprise, Google, Salesforce, Salesforce Government Cloud0 Comments

01Mar

The Evolution of Data

Data’s Evolution: Once Oil Now Compute Power For years, data was dubbed the new oil, a valuable resource that could fuel innovation. However, with the rise of artificial intelligence (AI), compute power has taken center stage as the key driver of business success. Sequoia has not hesitated to outline AI’s transformative potential: “The fields that generative AI addresses — knowledge work and creative work — comprise billions of workers. If generative AI can make these workers at least 10% more efficient and/or creative, they become not only faster and more efficient but more capable than before. Therefore, generative AI has the potential to generate trillions of dollars of economic value.” — Sonya Huang, Pat Grady, Generative AI: A Creative New World While markets continue to assess the validity of this claim, the parallels to past technological revolutions—such as the internet and mobile phones—are undeniable. AI’s influence on knowledge work, particularly in data analytics, is poised to be profound, reshaping how organizations extract value from data. The Rise of Data Analytics and AI Integration The data analytics market is expanding at an unprecedented rate, with a projected CAGR of 27.3% through 2030. A decade ago, data science degrees were rare; professionals often transitioned from related analytical fields like physics, mathematics, accounting, and economics. However, the landscape has shifted dramatically. According to the National Center for Education Statistics, bachelor’s degrees in data science surged by 968% between 2020 and 2022, reflecting the growing demand for analytics expertise. Despite its rapid evolution, modern data analytics presents unique challenges. Unlike software engineering—where code is deterministic—data pipelines evolve based on ingestion processes, requiring a different approach to governance and tooling. AI’s introduction into this space offers a chance to accelerate insights, but only if organizations integrate it strategically into their data frameworks. Modernizing Data Practices: From ETL to ELTL Historically, organizations relied on extract-transform-load (ETL) processes for data management. However, inefficiencies led to the rise of extract-load-transform (ELT), empowering analysts to transform data within cloud-based environments. Developer tools like dbt further streamlined this process, enabling analysts to create data models without relying on centralized data architects. Yet, with increased speed came challenges—primarily, the proliferation of inconsistent data sources. This issue underscores the importance of a final load step (ELTL) that incorporates governance measures to ensure accuracy before presenting data to users. Without proper governance, discrepancies between dashboards can erode trust in analytics. Data Governance and the Role of the Semantic Layer To mitigate inconsistencies, organizations have embraced semantic layers—standardized frameworks that define key business metrics. dbt’s semantic layer and emerging solutions like SQLMesh help enforce governance, ensuring AI-powered analytics operate on reliable data. AI’s effectiveness hinges on a well-documented semantic layer, built on metadata that defines relationships between data objects. Many AI systems utilize retrieval-augmented generation (RAG), which retrieves relevant context before generating responses. However, for AI-driven analytics to succeed, organizations must implement safeguards such as primary key checks and join definitions, ensuring structured and accurate outputs. AI-Powered Precision Analytics The integration of AI into data analytics is driving a shift toward precision analytics—delivering exactly the right data transformation tailored to user needs. This approach mirrors the concept of precision medicine in healthcare, where treatments are customized for individuals. With AI, organizations can move beyond dashboards overloaded with filters and instead provide instant, context-aware insights. Data Objects: A New Paradigm Traditionally, analytics relied on dashboards, but AI enables a more flexible approach through “data objects.” A data object represents a distinct SQL query that answers a user’s prompt. These objects can take three primary forms: This shift allows users to generate and compare data objects dynamically, reducing reliance on static reports and fostering a more agile analytical environment. AI Data Analytics: The Next Frontier Organizations now have the opportunity to revolutionize data analytics through AI. Broadly, AI-driven analytics solutions fall into two categories: Text-to-SQL technology has made significant strides, with execution benchmarks like Spider achieving over 91% accuracy. However, new challenges such as Spider 2.0 highlight the complexity of multi-step analytical workflows. AI systems must incorporate rigorous governance mechanisms to ensure accuracy and reliability. Trust, Governance, and the Human Element Trust is paramount in AI-driven analytics. Without proper governance, AI models risk generating misleading insights, undermining confidence in data-driven decision-making. Organizations must establish clear metadata standards, enforce rigorous validation checks, and maintain a human-in-the-loop approach to AI oversight. An AI-powered data agent should function much like a skilled data analyst—asking clarifying questions, referencing well-documented schemas, and leveraging business glossaries to interpret queries accurately. When implemented correctly, AI-driven analytics can transform data accessibility, making insights available in seconds rather than weeks. The Future of AI-Driven Analytics AI is poised to revolutionize data analytics, offering real-time insights with unparalleled accuracy. However, success hinges on a strong foundation of governance, well-defined data models, and reliable metadata. Organizations that embrace AI with a strategic approach will unlock new efficiencies, reduce bottlenecks, and redefine how data powers decision-making. By integrating AI into analytics responsibly, businesses can move beyond dashboards and embrace true precision analytics—where every insight is timely, relevant, and trustworthy. 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

March 1, 2025in Artificial Intelligence, Data, Generative AI, Technology

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

February 23, 2025in Data

02Jan

Salesforce AI Research Introduces BLIP-3-Video

Salesforce AI Research Introduces BLIP-3-Video: A Groundbreaking Multimodal Model for Efficient Video Understanding Vision-language models (VLMs) are transforming artificial intelligence by merging visual and textual data, enabling advancements in video analysis, human-computer interaction, and multimedia applications. These tools empower systems to generate captions, answer questions, and support decision-making, driving innovation in industries like entertainment, healthcare, and autonomous systems. However, the exponential growth in video-based tasks has created a demand for more efficient processing solutions that can manage the vast amounts of visual and temporal data inherent in videos. The Challenge of Scaling Video Understanding Existing video-processing models face significant inefficiencies. Many rely on processing each frame individually, creating thousands of visual tokens that demand extensive computational resources. This approach struggles with long or complex videos, where balancing computational efficiency and accurate temporal understanding becomes crucial. Attempts to address this issue, such as pooling techniques used by models like Video-ChatGPT and LLaVA-OneVision, have only partially succeeded, as they still produce thousands of tokens. Introducing BLIP-3-Video: A Breakthrough in Token Efficiency To tackle these challenges, Salesforce AI Research has developed BLIP-3-Video, a cutting-edge vision-language model optimized for video processing. The key innovation lies in its temporal encoder, which reduces visual tokens to just 16–32 tokens per video, significantly lowering computational requirements while maintaining strong performance. The temporal encoder employs a spatio-temporal attentional pooling mechanism, selectively extracting the most informative data from video frames. By consolidating spatial and temporal information into compact video-level tokens, BLIP-3-Video streamlines video processing without sacrificing accuracy. Efficient Architecture for Scalable Video Tasks BLIP-3-Video’s architecture integrates: This design ensures that the model efficiently captures essential temporal information while minimizing redundant data. Performance Highlights BLIP-3-Video demonstrates remarkable efficiency, achieving accuracy comparable to state-of-the-art models like Tarsier-34B while using a fraction of the tokens: For context, Tarsier-34B requires 4608 tokens for eight video frames, whereas BLIP-3-Video achieves similar results with only 32 tokens. On multiple-choice tasks, the model excelled: These results highlight BLIP-3-Video as one of the most token-efficient models in video understanding, offering top-tier performance while dramatically reducing computational costs. Advancing AI for Real-World Video Applications BLIP-3-Video addresses the critical challenge of token inefficiency, proving that complex video data can be processed effectively with far fewer resources. Developed by Salesforce AI Research, the model paves the way for scalable, real-time video processing across industries, including healthcare, autonomous systems, and entertainment. By combining efficiency with high performance, BLIP-3-Video sets a new standard for vision-language models, driving the practical application of AI in video-based systems. 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

January 2, 2025in Artificial Intelligence, Data, Salesforce

21Dec

Deploying Large Language Models in Healthcare

Study Identifies Cost-Effective Strategies for Deploying Large Language Models in Healthcare Efficient deployment of large language models (LLMs) at scale in healthcare can streamline clinical workflows and reduce costs by up to 17 times without compromising reliability, according to a study published in NPJ Digital Medicine by researchers at the Icahn School of Medicine at Mount Sinai. The research highlights the potential of LLMs to enhance clinical operations while addressing the financial and computational hurdles healthcare organizations face in scaling these technologies. To investigate solutions, the team evaluated 10 LLMs of varying sizes and capacities using real-world patient data. The models were tested on chained queries and increasingly complex clinical notes, with outputs assessed for accuracy, formatting quality, and adherence to clinical instructions. “Our study was driven by the need to identify practical ways to cut costs while maintaining performance, enabling health systems to confidently adopt LLMs at scale,” said Dr. Eyal Klang, director of the Generative AI Research Program at Icahn Mount Sinai. “We aimed to stress-test these models, evaluating their ability to manage multiple tasks simultaneously and identifying strategies to balance performance and affordability.” The team conducted over 300,000 experiments, finding that high-capacity models like Meta’s Llama-3-70B and GPT-4 Turbo 128k performed best, maintaining high accuracy and low failure rates. However, performance began to degrade as task volume and complexity increased, particularly beyond 50 tasks involving large prompts. The study further revealed that grouping tasks—such as identifying patients for preventive screenings, analyzing medication safety, and matching patients for clinical trials—enabled LLMs to handle up to 50 simultaneous tasks without significant accuracy loss. This strategy also led to dramatic cost savings, with API costs reduced by up to 17-fold, offering a pathway for health systems to save millions annually. “Understanding where these models reach their cognitive limits is critical for ensuring reliability and operational stability,” said Dr. Girish N. Nadkarni, co-senior author and director of The Charles Bronfman Institute of Personalized Medicine. “Our findings pave the way for the integration of generative AI in hospitals while accounting for real-world constraints.” Beyond cost efficiency, the study underscores the potential of LLMs to automate key tasks, conserve resources, and free up healthcare providers to focus more on patient care. “This research highlights how AI can transform healthcare operations. Grouping tasks not only cuts costs but also optimizes resources that can be redirected toward improving patient outcomes,” said Dr. David L. Reich, co-author and chief clinical officer of the Mount Sinai Health System. The research team plans to explore how LLMs perform in live clinical environments and assess emerging models to determine whether advancements in AI technology can expand their cognitive thresholds. 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

December 21, 2024in Data, Generative AI, Technology

salesforce agentforce ai powered agentic agents

09Dec

Agentforce AI Transforming the Workplace

AI is Transforming the WorkplaceArtificial intelligence is revolutionizing how businesses operate. By driving productivity, efficiency, and scalability, AI agents are unlocking new possibilities in the modern business landscape. However, achieving AI’s full potential requires navigating a complex web of data sources, workflows, and legacy systems. Enter Agentforce, a complete AI system for enterprises unveiled at Dreamforce. This agentic platform integrates data, automation, and human collaboration into a unified solution, enabling organizations to scale autonomous AI agents across their operations using low-code tools. Here are five key takeaways from Dreamforce to help organizations leverage AI agents to transform business operations and software development. 1. An Agentic Platform is Crucial for Building Exceptional AI Agents “What we build and how we build it is changing dramatically in this world of AI.”— Alice Steinglass, Executive Vice President and General Manager, Salesforce Platform CEOs are increasingly prioritizing AI, recognizing its potential to solve complex problems and enhance experiences for employees, partners, and customers. However, many teams face significant challenges in meeting AI demands safely and effectively. Issues such as disconnected data, IT backlogs, tech debt, and limited resources create a gap between AI ambition and execution. Agentforce addresses these challenges by providing an integrated platform that connects data, AI, automation, and humans. During the Platform keynote, a live demo showcased how an Agentforce agent streamlined casting for a fictional sports reality TV show, “Hooping with the Stars.” The agent seamlessly integrated audition videos, casting data, and applicant details, enabling faster, more informed decisions. This demo highlighted the potential of an agentic platform to turn challenges into opportunities, empowering organizations to build secure, dynamic AI agents. 2. Low-Code Tools Make AI Agents Accessible to Everyone “The true value is about getting work done. Agents being able to take action is what empowers employees and drives customer success.”— Claire Cheng, Vice President of Software Engineering AI agents surpass traditional chatbots by not only answering questions but also making decisions and taking actions. This capability is transforming customer and employee experiences by eliminating repetitive tasks and enabling teams to focus on higher-value activities. For example, Wiley, a global leader in education and research, deployed Agentforce agents to handle routine inquiries. Within weeks, these agents improved case resolution rates by over 40%, outperforming the company’s previous chatbot solution. Despite their sophistication, Agentforce agents are easy to build. With no-code and low-code tools like Agent Builder and Prompt Builder, businesses can create agents tailored to their unique needs, enabling even non-technical users to innovate without technical headaches. 3. Unified Data Enhances Accuracy and Intelligence of AI Agents “By analyzing unstructured data, you can uncover deeper patterns and sentiments that structured data alone might miss, leading to better personalization and decision-making.”— Taraneh Whitefield, Senior Director, Product Marketing Data is the foundation of effective AI strategies. Salesforce’s Data Cloud has become a leader in addressing data challenges by eliminating silos and simplifying data integration. This year’s introduction of the Data Cloud Vector Database marked a significant milestone. The platform now enables the integration of unstructured data — such as PDFs, videos, and audio files — alongside structured data, offering a complete 360-degree view of customers. For example, Mascoma Bank leveraged Data Cloud to consolidate data from 66 legacy systems. This streamlined sales workflows and improved personalized recommendations, reducing manual data handling by 45 minutes per loan application. 4. API-Led Integration is Essential for Scalable AI Systems “The world of AI is truly the world of APIs.”— Param Kahlon, EVP/GM, Automation & Integration, Salesforce The success of AI depends on seamless integration. Organizations often manage hundreds of applications with unique update schedules and security requirements, making integration a critical challenge. MuleSoft’s API-led approach provides a solution, allowing organizations to unlock data from disparate systems. With the introduction of MuleSoft Topic Center, developers can now package APIs and metadata into actionable “Topics” for Agentforce agents. This innovation simplifies building agents that can interact with enterprise systems, as demonstrated in a live demo featuring Aston Martin’s VIP vehicle customization experience. 5. Better Decision-Making Depends on Empowering People with the Right Data “I’m free to focus on the things that really, really matter to me.”— Anya A’Hearn, Hall of Fame Tableau Visionary Efficient decision-making relies on fast access to actionable insights. Tools like Tableau Einstein, Tableau Pulse, and Tableau Agent empower users to interact with data through natural language queries. For instance, with Tableau Agent, users can ask questions in plain language, and the agent will create visualizations instantly. Tasks that once took hours now take minutes, freeing up time for more strategic decision-making. Empowering Autonomous Agents with Data, AI, and Automation The future of AI is here, and it hinges on the seamless integration of data, automation, and human expertise. Agentforce, alongside Salesforce Platform, Data Cloud, MuleSoft, and Tableau, is equipping organizations with the tools to harness AI’s full potential — whether it’s 24/7 customer service, automated workflows, or real-time data visualization. Discover how Agentforce can revolutionize your business today. 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

December 9, 2024in Artificial Intelligence, Data, Enterprise, Mulesoft, Salesforce

06Dec

Google’s Gemini 1.5 Flash-8B

Google’s Gemini 1.5 Flash-8B: A Game-Changer in Speed and Affordability Google’s latest AI model, Gemini 1.5 Flash-8B, has taken the spotlight as the company’s fastest and most cost-effective offering to date. Building on the foundation of the original Flash model, 8B introduces key upgrades in pricing, speed, and rate limits, signaling Google’s intent to dominate the affordable AI model market. What Sets Gemini 1.5 Flash-8B Apart? Google has implemented several enhancements to this lightweight model, informed by “developer feedback and testing the limits of what’s possible,” as highlighted in their announcement. These updates focus on three major areas: 1. Unprecedented Price Reduction The cost of using Flash-8B has been slashed in half compared to its predecessor, making it the most budget-friendly model in its class. This dramatic price drop solidifies Flash-8B as a leading choice for developers seeking an affordable yet reliable AI solution. 2. Enhanced Speed The Flash-8B model is 40% faster than its closest competitor, GPT-4o, according to data from Artificial Analysis. This improvement underscores Google’s focus on speed as a critical feature for developers. Whether working in AI Studio or using the Gemini API, users will notice shorter response times and smoother interactions. 3. Increased Rate Limits Flash-8B doubles the rate limits of its predecessor, allowing for 4,000 requests per minute. This improvement ensures developers and users can handle higher volumes of smaller, faster tasks without bottlenecks, enhancing efficiency in real-time applications. Accessing Flash-8B You can start using Flash-8B today through Google AI Studio or via the Gemini API. AI Studio provides a free testing environment, making it a great starting point before transitioning to API integration for larger-scale projects. Comparing Flash-8B to Other Gemini Models Flash-8B positions itself as a faster, cheaper alternative to high-performance models like Gemini 1.5 Pro. While it doesn’t outperform the Pro model across all benchmarks, it excels in cost efficiency and speed, making it ideal for tasks requiring rapid processing at scale. In benchmark evaluations, Flash-8B surpasses the base Flash model in four key areas, with only marginal decreases in other metrics. For developers prioritizing speed and affordability, Flash-8B offers a compelling balance between performance and cost. Why Flash-8B Matters Gemini 1.5 Flash-8B highlights Google’s commitment to providing accessible AI solutions for developers without compromising on quality. With its reduced costs, faster response times, and higher request limits, Flash-8B is poised to redefine expectations for lightweight AI models, catering to a broad spectrum of applications while maintaining an edge in affordability. 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

December 6, 2024in Data, Google

02Dec

AI-Checking Agents

Introducing AI-Checking Agents: The Next Frontier in Software Quality Assurance The software industry has continually evolved in its pursuit of better quality assurance (QA) methods. While traditional approaches like unit testing and manual QA offer foundational tools, they often fail to meet the growing complexity of modern software. Automated testing and DevOps practices have helped, but these methods are still time-intensive, costly, and limited in scope. AI-Checking Agents. Enter AI-Checking Agents — an innovative solution leveraging generative AI to revolutionize software testing and quality assurance. These agents promise unprecedented coverage, speed, and efficiency, addressing the challenges of today’s demanding software ecosystems. Why AI-Checking Agents? Traditional QA methods fall short in delivering exhaustive coverage for the diverse behaviors and interactions of modern software. AI-Checking Agents close this gap by introducing: Synthetic Users: Revolutionizing User Experience (UX) Testing One of the most groundbreaking features of AI-Checking Agents is the ability to create synthetic users. These AI-driven personas simulate real-world user interactions, offering a novel approach to UX analysis. Key Features of Synthetic Users: UX Insights Delivered by Synthetic Users: Benefits of AI-Checking Agents in QA Integrating AI-Checking Agents with Existing QA Practices AI-Checking Agents are not a replacement for traditional methods but a powerful complement to existing practices: Transforming the Development Process AI-Checking Agents not only streamline QA but also enhance the overall development process: The Future of Quality Assurance AI-Checking Agents represent a paradigm shift in software testing, blending the best of AI-driven insights with traditional QA practices. By integrating these agents into their workflows, development teams can achieve: In a world of ever-evolving software demands, AI-Checking Agents are the key to achieving unparalleled speed, depth, and precision in quality assurance. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

December 2, 2024in Generative AI

02Dec

Salesforce Announces Top Secret Gov Cloud

This advanced cloud solution is hosted on Amazon Web Services’ Top Secret cloud infrastructure. According to Salesforce’s press release, Government Cloud Premium is built with an API-first architecture, enabling agencies to leverage other data sources and systems, including proprietary AI applications, to enhance mission-critical operations.

December 2, 2024in Data

25Oct

UX Principles for AI in Healthcare

The Role of UX in AI-Driven Healthcare AI is poised to revolutionize the global economy, with predictions it could contribute $15.7 trillion by 2030—more than the combined economic output of China and India. Among the industries likely to see the most transformative impact is healthcare. However, during my time at NHS Digital, I saw how systems that weren’t designed with existing clinical workflows in mind added unnecessary complexity for clinicians, often leading to manual workarounds and errors due to fragmented data entry across systems. The risk is that AI, if not designed with user experience (UX) at the forefront, could exacerbate these issues, creating more disruption rather than solving problems. From diagnostic tools to consumer health apps, the role of UX in AI-driven healthcare is critical to making these innovations effective and user-friendly. This article explores the intersection of UX and AI in healthcare, outlining key UX principles to design better AI-driven experiences and highlighting trends shaping the future of healthcare. The Shift in Human-Computer Interaction with AI AI fundamentally changes how humans interact with computers. Traditionally, users took command by entering inputs—clicking, typing, and adjusting settings until the desired outcome was achieved. The computer followed instructions, while the user remained in control of each step. With AI, this dynamic shifts dramatically. Now, users specify their goal, and the AI determines how to achieve it. For example, rather than manually creating an illustration, users might instruct AI to “design a graphic for AI-driven healthcare with simple shapes and bold colors.” While this saves time, it introduces challenges around ensuring the results meet user expectations, especially when the process behind AI decisions is opaque. The Importance of UX in AI for Healthcare A significant challenge in healthcare AI is the “black box” nature of the systems. For example, consider a radiologist reviewing a lung X-ray that an AI flagged as normal, despite the presence of concerning lesions. Research has shown that commercial AI systems can perform worse than radiologists when multiple health issues are present. When AI decisions are unclear, clinicians may question the system’s reliability, especially if they cannot understand the rationale behind an AI’s recommendation. This opacity hinders feedback, making it difficult to improve the system’s performance. Addressing this issue is essential for UX designers. Bias in AI is another significant issue. Many healthcare AI tools have been documented as biased, such as systems trained on predominantly male cardiovascular data, which can fail to detect heart disease in women. AIs also struggle to identify conditions like melanoma in people with darker skin tones due to insufficient diversity in training datasets. UX can help mitigate these biases by designing interfaces that clearly explain the data used in decisions, highlight missing information, and provide confidence levels for predictions. The movement toward eXplainable AI (XAI) seeks to make AI systems more transparent and interpretable for human users. UX Principles for AI in Healthcare To ensure AI is beneficial in real-world healthcare settings, UX designers must prioritize certain principles. Below are key UX design principles for AI-enabled healthcare applications: Applications of AI in Healthcare AI is already making a significant impact in various healthcare applications, including: Real-world deployments of AI in healthcare have demonstrated that while AI can be useful, its effectiveness depends heavily on usability and UX design. By adhering to the principles of transparency, interpretability, controllability, and human-centered AI, designers can help create AI-enabled healthcare applications that are both powerful and user-friendly. 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

October 25, 2024in AI Tools, Data

21Oct

How Skechers Solved Its Ecommerce Challenges

Skechers Boosts Direct-to-Consumer Sales with Ecommerce Platform Upgrades Skechers, now a global brand in 2024, credits its recent ecommerce platform upgrades for saving time and increasing direct-to-consumer sales. However, it wasn’t always equipped with the right technology to support its massive growth. During Salesforce’s Dreamforce conference in San Francisco, Eric Cheng, Skechers USA Inc.’s director of ecommerce architecture, shared insights into how key technology decisions helped the brand expand and enhance its website and content capabilities. “Today, we’re present in over 180 countries worldwide,” Cheng said, speaking on stage at the Moscone Center. Skechers’ journey began in 1992, and its expansion has taken the brand across borders, reaching millions of customers worldwide. “We connect hundreds of millions of customers through our retail stores and ecommerce platform to deliver a unique experience,” Cheng noted, emphasizing the need to meet the diverse demands of each market. Skechers ranks No. 273 in the Top 1000, Digital Commerce 360’s ranking of the largest North American e-retailers by online sales, where it is categorized as an Apparel & Accessories retailer. Digital Commerce 360 projects that Skechers will reach 0.65 million in online sales by 2024. Ecommerce Platform Challenges Cheng acknowledged that Skechers’ digital transformation wasn’t immediate: “The journey did not just happen overnight; it took time and effort.” Skechers faced challenges in three key areas: content management, scalability, and customer experience. The legacy system was inadequate, lacking robust tools for efficient content delivery, previewing scheduled content, and handling localization. As Cheng described, launching a marketing page often required the content team to be on standby at midnight—an unsustainable approach for 17 countries. How Skechers Solved Its Ecommerce Challenges To overcome these hurdles, Skechers partnered with Astound Digital. Together, they implemented Salesforce Service Cloud and Manhattan Active Omni for order management. Kyle Montgomery, senior vice president of commerce at Astound Digital, joined Cheng on stage and highlighted the goal: “Their vision was to unify, supply, and scale.” This transformation enabled Skechers to bring 17 countries in Europe, Japan, and North America onto a single platform. Jennifer Lane, Salesforce’s director of success guides, also emphasized the flexibility achieved using Salesforce’s Page Designer and localization solutions from Salesforce’s AppExchange. Integrations with Thomson Reuters for tax, CyberSource for payments, and Salesforce Marketing Cloud for personalization further enhanced Skechers’ capabilities. The Results Cheng highlighted three key improvements after the ecommerce overhaul. First, content creation and localization tools improved operational efficiency by over 500%. The time to launch in new markets was dramatically reduced from five months to just a few weeks. Additionally, Skechers saw a notable sales boost, with a 24.5% increase in its direct-to-consumer segment during Q1 2023. Skechers’ success demonstrates the significant impact of a well-executed ecommerce platform upgrade, allowing the brand to scale globally while improving customer experience and operational efficiency. Contact Tectonic to learn what Salesforce can do for you. 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

October 21, 2024in Google, Salesforce, Salesforce Marketing Cloud, Salesforce Service Cloud, Technology

18Oct

GPUs and AI Development

Graphics processing units (GPUs) have become widely recognized due to their growing role in AI development. However, a lesser-known but critical technology is also gaining attention: high-bandwidth memory (HBM). HBM is a high-density memory designed to overcome bottlenecks and maximize data transfer speeds between storage and processors. AI chipmakers like Nvidia rely on HBM for its superior bandwidth and energy efficiency. Its placement next to the GPU’s processor chip gives it a performance edge over traditional server RAM, which resides between storage and the processing unit. HBM’s ability to consume less power makes it ideal for AI model training, which demands significant energy resources. However, as the AI landscape transitions from model training to AI inferencing, HBM’s widespread adoption may slow. According to Gartner’s 2023 forecast, the use of accelerator chips incorporating HBM for AI model training is expected to decline from 65% in 2022 to 30% by 2027, as inferencing becomes more cost-effective with traditional technologies. How HBM Differs from Other Memory HBM shares similarities with other memory technologies, such as graphics double data rate (GDDR), in delivering high bandwidth for graphics-intensive tasks. But HBM stands out due to its unique positioning. Unlike GDDR, which sits on the printed circuit board of the GPU, HBM is placed directly beside the processor, enhancing speed by reducing signal delays caused by longer interconnections. This proximity, combined with its stacked DRAM architecture, boosts performance compared to GDDR’s side-by-side chip design. However, this stacked approach adds complexity. HBM relies on through-silicon via (TSV), a process that connects DRAM chips using electrical wires drilled through them, requiring larger die sizes and increasing production costs. According to analysts, this makes HBM more expensive and less efficient to manufacture than server DRAM, leading to higher yield losses during production. AI’s Demand for HBM Despite its manufacturing challenges, demand for HBM is surging due to its importance in AI model training. Major suppliers like SK Hynix, Samsung, and Micron have expanded production to meet this demand, with Micron reporting that its HBM is sold out through 2025. In fact, TrendForce predicts that HBM will contribute to record revenues for the memory industry in 2025. The high demand for GPUs, especially from Nvidia, drives the need for HBM as AI companies focus on accelerating model training. Hyperscalers, looking to monetize AI, are investing heavily in HBM to speed up the process. HBM’s Future in AI While HBM has proven essential for AI training, its future may be uncertain as the focus shifts to AI inferencing, which requires less intensive memory resources. As inferencing becomes more prevalent, companies may opt for more affordable and widely available memory solutions. Experts also see HBM following the same trajectory as other memory technologies, with continuous efforts to increase bandwidth and density. The next generation, HBM3E, is already in production, with HBM4 planned for release in 2026, promising even higher speeds. Ultimately, the adoption of HBM will depend on market demand, especially from hyperscalers. If AI continues to push the limits of GPU performance, HBM could remain a critical component. However, if businesses prioritize cost efficiency over peak performance, HBM’s growth may level off. 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

October 18, 2024in Data, Technology

10Oct

Collaboration Between Humans and AI

The Future of AI: What to Expect in the Next 5 Years In the next five years, AI will accelerate human life, reshape behaviors, and transform industries—these changes are inevitable. Collaboration Between Humans and AI. For much of the early 20th century, AI existed mainly in science fiction, where androids, sentient machines, and futuristic societies intrigued fans of the genre. From films like Metropolis to books like I, Robot, AI was the subject of speculative imagination. AI in fiction often over-dramatized reality and caused us to suspend belief in what was and was not possible. But by the mid-20th century, scientists began working to bring AI into reality. A Brief History of AI’s Impact on Society The 1956 Dartmouth Summer Research Project on Artificial Intelligence marked a key turning point, where John McCarthy coined the term “artificial intelligence” and helped establish a community of AI researchers. Although the initial excitement about AI often outpaced its actual capabilities, significant breakthroughs began emerging by the late 20th century. One such moment was IBM’s Deep Blue defeating chess champion Garry Kasparov in 1997, signaling that machines could perform complex cognitive tasks. The rise of big data and Moore’s Law, which fueled the exponential growth of computational power, enabled AI to process vast amounts of information and tackle tasks previously handled only by humans. By 2022, generative AI models like ChatGPT proved that machine learning could yield highly sophisticated and captivating technologies. AI’s influence is now everywhere. No longer is it only discussed in IT circles. AI is being featured in nearly all new products hitting the market. It is part of if not the creation tool of most commercials. Voice assistants like Alexa, recommendation systems used by Netflix, and autonomous vehicles represent just a glimpse of AI’s current role in society. Yet, over the next five years, AI’s development is poised to introduce far more profound societal changes. How AI Will Shape the Future Industries Most Affected by AI Long-term Risks of Collaboration Between Humans and AI AI’s potential to pose existential risks has long been a topic of concern. However, the more realistic danger lies in human societies voluntarily ceding control to AI systems. Algorithmic trading in finance, for example, demonstrates how human decisions are already being replaced by AI’s ability to operate at unimaginable speeds. Still, fear of AI should not overshadow the opportunities it presents. If organizations shy away from AI out of anxiety, they risk missing out on innovations and efficiency gains. The future of AI depends on a balanced approach that embraces its potential while mitigating its risks. In the coming years, the collaboration between humans and AI will drive profound changes across industries, legal frameworks, and societal norms, creating both challenges and opportunities for the future. Tectonic can help you map your AI journey for the best Collaboration Between Humans and AI. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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

October 10, 2024in AI Tools, Artificial Intelligence, Data, Enterprise, Generative AI, Laws, Salesforce

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