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23Dec

AI Transforming Precision Medicine

How AI-Driven Data Curation is Transforming Precision Medicine Precision medicine—a healthcare approach that personalizes disease prevention and treatment based on insights into a patient’s genes, environment, and behavior—holds incredible promise. However, its success depends on high-quality, curated data from sources like electronic health records (EHRs). This reliance creates significant challenges for healthcare providers and researchers. Can artificial intelligence (AI) help address these hurdles? AI-enabled data curation is already making strides in advancing precision medicine, particularly in oncology. By analyzing vast datasets, including structured and unstructured information, AI is helping healthcare organizations accelerate research and improve patient outcomes. Data Curation Challenges in Precision Medicine Real-world data (RWD) is a key driver of precision medicine, but processing this data is fraught with challenges. According to Dr. C.K. Wang, Chief Medical Officer at COTA, Inc., EHRs provide unprecedented access to detailed patient information, enabling deeper insights into care patterns. However, much of this data resides in unstructured formats, such as clinicians’ notes, making it difficult to extract and analyze. “To transform this unstructured data into actionable insights, significant human expertise and resources are required,” Wang explained. While AI tools like COTA’s CAILIN, which uses advanced search capabilities, streamline this process, human involvement remains essential. Wang emphasized that even with the rapid advancements in AI, healthcare data curation requires expert oversight to ensure quality and reliability. “The adage ‘junk in, junk out’ applies here—without high-quality training data, AI cannot generate meaningful insights,” he noted. PHI and COTA: A Collaborative Approach to AI-Driven Curation To overcome these challenges, Precision Health Informatics (PHI), a subsidiary of Texas Oncology, partnered with COTA to enhance their data curation capabilities. The collaboration aims to integrate structured and unstructured data, including clinician notes and patient-reported outcomes, into a unified resource for precision medicine. PHI’s database, which represents 1.6 million patient journeys, provides a rich resource for hypothesis-driven studies and clinical trial enrichment. However, much of this data was siloed or unstructured, requiring advanced tools and expert intervention. Lori Brisbin, Chief Operating Officer at PHI, highlighted the importance of partnering with a data analytics leader. “COTA’s strong clinical knowledge in oncology allowed them to identify data gaps and recommend improvements,” she said. This partnership is yielding significant results, including a high data attrition rate of 87%—far surpassing the industry average of 50% for similar projects. The Role of AI in Cancer Care AI tools like CAILIN are helping PHI and COTA refine data curation processes by: Brisbin likened the role of AI to sorting images: “If you’re looking for German shepherds, AI will narrow the search but might include similar images, like wolves or huskies. Experts are still needed to validate and refine the results.” Building the Foundation for Better Outcomes The integration of high-quality RWD into analytics efforts is reshaping precision medicine. While clinical trial data offers valuable insights, it often lacks the variability seen in real-world scenarios. Adding RWD to these datasets helps expand the scope of research and ensure broader applicability. For instance, cancer care guidelines developed with RWD can account for diverse patient populations and treatment approaches. COTA’s work with PHI underscores the value of collaborative data curation, with AI streamlining processes and human experts ensuring accuracy. The Future of AI in Precision Medicine As healthcare organizations invest in data-driven innovation, AI will play an increasingly pivotal role in enabling precision medicine. However, challenges remain. Wang noted that gaps in EHR data, such as missing survival metrics, can undermine oncological outcomes research. Advances in interoperability and external data sources will be key to addressing these issues. “The foundation of our partnership is built on leveraging data insights to enhance care quality and improve operational efficiency,” Wang said. Through AI-powered tools and meaningful partnerships, precision medicine is poised to deliver transformative results, empowering providers to offer tailored treatments that improve patient outcomes at scale. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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December 23, 2024in AI Tools, Artificial Intelligence, Data
AI Agents Set to Break Through in 2025
20Dec

AI Agents Set to Break Through in 2025

2025: The Year AI Agents Transform Work and Life Despite years of hype around artificial intelligence, its true disruptive impact has so far been limited. However, industry experts believe that’s about to change in 2025 as autonomous AI agents prepare to enter and reshape nearly every facet of our lives. Since OpenAI’s ChatGPT took the world by storm in late 2022, billions of dollars have been funneled into the AI sector. Big tech and startups alike are racing to harness the transformative potential of the technology. Yet, while millions now interact with AI chatbots daily, turning them into tools that deliver tangible business value has proven challenging. A recent study by Boston Consulting Group revealed that only 26% of companies experimenting with AI have progressed beyond proof of concept to derive measurable value. This lag reflects the limitations of current AI tools, which serve primarily as copilots—capable of assisting but requiring constant oversight and remaining prone to errors. AI Agents Set to Break Through in 2025 The status quo, however, is poised for a radical shift. Autonomous AI agents—capable of independently analyzing information, making decisions, and taking action—are expected to emerge as the industry’s next big breakthrough. “For the first time, technology isn’t just offering tools for humans to do work,” Salesforce CEO Marc Benioff wrote in Time. “It’s providing intelligent, scalable digital labor that performs tasks autonomously. Instead of waiting for human input, agents can analyze information, make decisions, and adapt as they go.” At their core, AI agents leverage the same large language models (LLMs) that power tools like ChatGPT. But these agents take it further, acting as reasoning engines that develop step-by-step strategies to execute tasks. Armed with access to external data sources like customer records or financial databases and equipped with software tools, agents can achieve goals independently. While current LLMs still face reasoning limitations, advancements are on the horizon. New models like OpenAI’s “o1” and DeepSeek’s “R1” are specialized for reasoning, sparking hope that 2025 will see agents grow far more capable. Big Tech and Startups Betting Big Major players are already gearing up for this new era. Startups are also eager to carve out their share of the market. According to Pitchbook, funding deals for agent-focused ventures surged by over 80% in 2024, with the median deal value increasing nearly 50%. Challenges to Overcome Despite the enthusiasm, significant hurdles remain. 2025: A Turning Point Despite these challenges, many experts believe 2025 will mark the mainstream adoption of AI agents. A New World of Work No matter the pace, it’s clear that AI agents will dominate the industry’s focus in 2025. If the technology delivers on its promise, the workplace could undergo a profound transformation, enabling entirely new ways of working and automating tasks that once required human intervention. The question isn’t if agents will redefine the way we work—it’s how fast. By the end of 2025, the shift could be undeniable. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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December 20, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Google, Salesforce, Technology
Gen AI Unleased With Vector Database
18Dec

Knowledge Graphs and Vector Databases

The Role of Knowledge Graphs and Vector Databases in Retrieval-Augmented Generation (RAG) In the dynamic AI landscape, Retrieval-Augmented Generation (RAG) systems are revolutionizing data retrieval by combining artificial intelligence with external data sources to deliver contextual, relevant outputs. Two core technologies driving this innovation are Knowledge Graphs and Vector Databases. While fundamentally different in their design and functionality, these tools complement one another, unlocking new potential for solving complex data problems across industries. Understanding Knowledge Graphs: Connecting the Dots Knowledge Graphs organize data into a network of relationships, creating a structured representation of entities and how they interact. These graphs emphasize understanding and reasoning through data, offering explainable and highly contextual results. How They Work Strengths Limitations Applications Vector Databases: The Power of Similarity In contrast, Vector Databases thrive in handling unstructured data such as text, images, and audio. By representing data as high-dimensional vectors, they excel at identifying similarities, enabling semantic understanding. How They Work Strengths Limitations Applications Combining Knowledge Graphs and Vector Databases: A Hybrid Approach While both technologies excel independently, their combination can amplify RAG systems. Knowledge Graphs bring reasoning and structure, while Vector Databases offer rapid, similarity-based retrieval, creating hybrid systems that are more intelligent and versatile. Example Use Cases Knowledge Graphs vs. Vector Databases: Key Differences Feature Knowledge Graphs Vector Databases Data Type Structured Unstructured Core Strength Relational reasoning Similarity-based retrieval Explainability High Low Scalability Limited for large datasets Efficient for massive datasets Flexibility Schema-dependent Schema-free Challenges in Implementation Future Trends: The Path to Convergence As AI evolves, the distinction between Knowledge Graphs and Vector Databases is beginning to blur. Emerging trends include: This convergence is paving the way for smarter, more adaptive systems that can handle both structured and unstructured data seamlessly. Conclusion Knowledge Graphs and Vector Databases represent two foundational technologies in the realm of Retrieval-Augmented Generation. Knowledge Graphs excel at reasoning through structured relationships, while Vector Databases shine in unstructured data retrieval. By combining their strengths, organizations can create hybrid systems that offer unparalleled insights, efficiency, and scalability. In a world where data continues to grow in complexity, leveraging these complementary tools is essential. Whether building intelligent healthcare systems, enhancing recommendation engines, or powering semantic search, the synergy between Knowledge Graphs and Vector Databases is unlocking the next frontier of AI innovation, transforming how industries harness the power of their data. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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December 18, 2024in Artificial Intelligence, Data, Enterprise, Google
AI and UX Design
16Dec

AI and UX Design

This insight comprehensively covers how AI is transforming UX design, presenting both opportunities and challenges while emphasizing the importance of maintaining a human-centric approach. Here’s a polished and slightly condensed version, retaining the core points for better clarity and engagement: AI in UX Design: Transforming Experiences in 2024 and Beyond In 2024, artificial intelligence (AI) is redefining user experience (UX) design and research. From streamlining processes to elevating personalization, UX professionals are integrating AI into their workflows to create experiences that are more intuitive and efficient. This insight explores how AI is reshaping UX and how designers can leverage it while preserving the human touch. How AI is Revolutionizing UX Design 1. Advanced AI Technologies in UXAI technologies like machine learning (ML), natural language processing (NLP), and computer vision are empowering designers with tools to understand user behavior better, build conversational interfaces, and create accessible, adaptable designs. These innovations provide deeper insights into user preferences and help refine interfaces to align with evolving needs. 2. Automating Routine Design TasksAI is taking over repetitive tasks such as rapid prototyping, A/B testing, and user data analysis, allowing designers to focus on creative, strategic challenges. For example: 3. Enhanced PersonalizationAI-driven systems offer dynamic content delivery, adaptive interfaces, and predictive behavior modeling to craft uniquely tailored experiences. These enhancements not only engage users but also foster loyalty by addressing individual preferences in real time. Balancing AI and Human-Centric Design While AI accelerates UX processes, maintaining a human-centered approach is essential. Successful integration requires: Best Practices for AI-Driven UX Design Ethical Considerations in AI-Enhanced UX Ethics remain at the forefront of AI in UX. Key concerns include: Learning from Case Studies These examples highlight how thoughtful AI integration can transform UX into a seamless, user-friendly journey. Preparing for Future Trends Looking ahead to 2025 and beyond, AI will continue to introduce innovations like emotional recognition and generative design, enabling even more intuitive user experiences. However, challenges such as data privacy concerns and high implementation costs will persist. UX professionals must adapt by blending AI-driven insights with human creativity, ensuring that designs remain empathetic and accessible. Conclusion AI is revolutionizing UX design, offering tools to enhance efficiency, personalization, and user engagement. The key to success lies in using AI as a complement to creativity rather than a replacement. By balancing automation with human-centered principles and committing to ethical practices, businesses can harness AI to create transformative, user-focused designs that truly resonate. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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December 16, 2024in Artificial Intelligence, Data, Google
AI Agents and Consumer Trust
13Dec

AI Agents Next AI Evolution

AI agents are being hailed as the next big leap in artificial intelligence, but there’s no universally accepted definition of what they are—or what they should do. Even within the tech community, there’s debate about what constitutes an AI agent. At its core, an AI agent can be described as software powered by artificial intelligence that performs tasks once handled by human roles, such as customer service agents, HR representatives, or IT help desk staff. However, their potential spans much further. These agents don’t just answer questions—they take action, often working across multiple systems. For example, Perplexity recently launched an AI agent to assist with holiday shopping, while Google introduced Project Mariner, an agent that helps users book flights, find recipes, and shop for household items. While the idea seems straightforward, it’s muddied by inconsistent definitions. For Google, AI agents are task-based assistants tailored to specific roles, like coding help for developers or troubleshooting issues for IT professionals. In contrast, Asana views agents as digital co-workers that take on assigned tasks, and Sierra—a startup led by former Salesforce co-CEO Bret Taylor—envisions agents as sophisticated customer experience tools that surpass traditional chatbots by tackling complex problems. This lack of consensus adds to the uncertainty around what AI agents can truly achieve. Rudina Seseri, founder and managing partner at Glasswing Ventures, explains this ambiguity stems from the technology’s infancy. She describes AI agents as intelligent systems capable of perceiving their environment, reasoning, making decisions, and taking actions to achieve specific goals autonomously. These agents rely on a mix of AI technologies, including natural language processing, machine learning, and computer vision, to operate in dynamic environments. Optimists, like Box CEO Aaron Levie, believe AI agents will improve rapidly as advancements in GPU performance, model efficiency, and AI frameworks create a self-reinforcing cycle of innovation. However, skeptics like MIT robotics pioneer Rodney Brooks caution against overestimating progress, noting that solving real-world problems—especially those involving legacy systems with limited API access—can be far more challenging than anticipated. David Cushman of HFS Research likens current AI agents to assistants rather than fully autonomous entities, with their capabilities limited to helping users complete specific tasks within pre-defined boundaries. True autonomy, where AI agents handle contingencies and perform at scale without human oversight, remains a distant goal. Jon Turow, a partner at Madrona Ventures, emphasizes the need for dedicated infrastructure to support the development of AI agents. He envisions a tech stack that allows developers to focus on product differentiation while leaving scalability and reliability to the platform. This infrastructure would likely involve multiple specialized models working together under a routing layer, rather than relying on a single large language model (LLM). Fred Havemeyer of Macquarie US Equity Research agrees, noting that the most effective AI agents will combine various models to handle complex tasks. He imagines a future where agents act like autonomous supervisors, delegating tasks and reasoning through multi-step processes to achieve abstract goals. While this vision is compelling, the current state of AI agents suggests we’re still in a transitional phase. The progress so far is promising, but several breakthroughs are needed before agents can operate as envisioned—truly autonomous, multi-functional, and capable of seamless collaboration across diverse systems. This story, originally published on July 13, 2024, has been updated to reflect new developments from Perplexity and Google. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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December 13, 2024in Artificial Intelligence, Google, Salesforce, 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 Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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

AI Agents Are the Next Wave of Generative Technology

How AI Agents Are the Next Wave of Generative Technology The rise of agentic technology marks a pivotal evolution in artificial intelligence, signaling a shift from mere assistive tools to autonomous agents capable of complex, multi-step tasks. While excitement abounds, this new wave of AI also raises questions about its practical application and long-term impact. AI agents—autonomous tools designed to perform tasks independently—are rapidly gaining traction across industries. Vendors and developers are positioning them as the future of generative AI, enabling organizations to streamline workflows and unlock new efficiencies. However, concerns remain regarding the scope of tasks assigned to these agents and their return on investment (ROI). A Growing Presence at Industry Events The growing curiosity and cautious optimism surrounding AI agents were palpable at the recent AI Summit conference. “AI agents are here, and they’re scaling,” said Tim Cotten, CEO and founder of Scripted Inc., a generative AI platform for game development powered by autonomous agents. Speaking during a session on AI in game development, Cotten predicted that a third of companies present at the summit would likely adopt agent-based solutions in the near future. Cotten emphasized the transformative potential of AI agents: “Agents allow you to spread your influence while you’re asleep. They can do the job for you, generate new ideas, and even create additional agents to tackle emerging needs.” However, he also highlighted a critical challenge: ensuring agents remain focused on well-defined tasks. Overloading agents with responsibilities beyond their scope can lead to inefficiencies and diminished outcomes. Specialized Agents vs. General-Purpose Agents The debate between using specialized agents versus general-purpose agents continues to shape the discussion around agentic AI. According to Atif Khan, Chief Artificial Intelligence Officer at Semantex: “If you have a large application with different components, it’s better to deploy specialized agents for each task. For example, one agent could handle search, another documentation, and others for accounting or customer service.” Khan advocated for modular, independent agents that can be trained and refined individually, rather than a single, all-encompassing agent. This approach not only improves efficiency but also reduces the risk of “hallucinations,” or inaccuracies, that can arise when agents attempt to manage overly complex workloads. Mitigating Risks and Maintaining Oversight Despite their autonomy, AI agents still require oversight to ensure accuracy and compliance. Drayton Wade, COO at Kognitos, stressed the importance of human validation: “Organizations must determine where human review is necessary, especially in high-stakes environments like finance, where agents operate at scale and speed.” Logging agent activities and involving humans in critical decision-making processes can mitigate risks and create accountability, Wade added. Agentforce for Sales: Unlocking New Possibilities One of the most promising applications of agentic technology is in sales, where AI agents can significantly boost productivity and enhance customer experiences. Agentforce for Sales is a cutting-edge suite of tools designed to create and deploy both autonomous and assistive sales agents within Salesforce environments. These agents handle tasks such as lead qualification, pipeline building, case deflection, and sales coaching, allowing sales reps to focus on high-value activities. Types of Agentforce Sales Agents The Future of Agentic Technology AI agents are ushering in a new era of generative technology, enabling businesses to scale operations and optimize workflows. However, their success depends on thoughtful deployment, clear task delineation, and ongoing human oversight. By embracing solutions like Agentforce for Sales, organizations can maximize the potential of agentic AI, transforming how teams work and paving the way for continued innovation. The question is no longer whether to adopt AI agents but how to deploy them effectively to achieve lasting impact. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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December 8, 2024in Artificial Intelligence, Data, Generative AI, Salesforce, Technology
ai arms race
05Dec

AI Arms Race

AI Arms Race: Providers Catching Up to Payers in Claims Review The healthcare sector is in the midst of an escalating AI arms race as providers adopt the same artificial intelligence technologies payers are leveraging for claims review. Insurers currently lead this race, using AI to streamline processes such as prior authorizations, but experts predict providers will soon narrow the gap. Insurers’ AI Advantage Leading payers, including UnitedHealth, Humana, and Cigna, have integrated algorithmic decision tools to assess claims and determine coverage eligibility. These technologies allow insurers to flag services that fall outside plan criteria, ostensibly increasing efficiency. This trend is expanding, as evidenced by Blue Shield of California’s announcement of a partnership with Salesforce to pilot claims automation technology in early 2025. The nonprofit insurer claims this initiative will reduce prior authorization decision times from weeks or days to mere seconds, benefiting providers and patients alike. However, provider experiences paint a more contentious picture. Reports from lawmakers and healthcare executives suggest AI-driven claims processes lead to a surge in denials. For example, Providence CFO Greg Hoffman revealed that AI adoption by payers resulted in a 50% increase in underpayments and initial denials over two years, forcing providers to significantly increase manual interventions to resolve claims. A Battle for Balance The imbalance in AI adoption has prompted providers to take action. Experts like Jeffrey Cribbs, a vice president analyst at Gartner, see this as a forced “arms race” in which both sides are continually refining their tools. While payers focus on flagging potential exceptions, providers are working to develop systems for more efficient claims submissions and dispute resolution. Providence’s strategy includes outsourcing revenue cycle management to R1, a 10-year partnership designed to quickly address rising claims denials. Hoffman explained that building equivalent AI systems internally would take years, making partnerships essential for staying competitive in the short term. Collaboration Among Providers On the provider side, executives like Sara Vaezy, EVP and Chief Strategy Officer at Providence, emphasize the need for collaboration. She advocates for coalitions to share data and establish AI standards, which would allow providers to compete more effectively. Panelists at HLTH echoed this sentiment. Amit Phull, Chief Physician Experience Officer at Doximity, argued that AI could eventually “level the playing field” for providers by reducing the time required for claims documentation. Deloitte principal consultant Bill Fera added that AI would allow providers to quickly analyze policies and determine whether a patient qualifies for coverage under plan terms. The Road Ahead Despite the current disparity, experts believe AI will eventually equalize the claims review process. Providers are beginning to invest in tools that will help them handle vast amounts of data efficiently, offering clarity in disputes and cutting down documentation time. “It’s still early innings,” Phull said, “but the technology is going to go a long way toward leveling that playing field.” For now, however, insurers maintain the upper hand. As providers navigate the complexities of AI adoption, partnerships and collaboration may prove critical in ensuring they remain competitive in this rapidly evolving landscape. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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December 5, 2024in Artificial Intelligence, Data, Salesforce, Technology
computer vision models
05Dec

Computer Vision Models

Introduction to Visual Language Models (VLMs): The Future of Computer Vision Achieving a 28% Boost in Multimodal Image Search Accuracy with VLMs Until recently, artificial intelligence models were narrowly focused—either excelling in understanding text or interpreting images, but rarely both. This siloed approach limited their potential to process and connect different types of data. The development of general-purpose language models, like GPTs, marked a significant leap forward. These models transitioned AI from task-specific systems to powerful, versatile tools capable of handling a wide range of language-driven tasks. Yet, despite their advancements, language models and computer vision systems evolved separately, like having the ability to hear without seeing—or vice versa. Visual Language Models (VLMs) bridge this gap, combining the capabilities of language and vision to create multimodal systems. In this article, we’ll explore VLMs’ architecture, training methods, challenges, and transformative potential in fields like image search. We’ll also examine how implementing VLMs revolutionized an AI-powered search engine. What Are Visual Language Models (VLMs)? VLMs represent the next step in the evolution of AI: multimodal models capable of processing multiple data types, including text, images, audio, and video. Why Multimodal Models? The rise of general-purpose approaches has outpaced narrow, specialized systems in recent years. Here’s why: At their core, VLMs receive input in the form of images and text (called “instructs”) and produce textual responses. Their range of applications includes image classification, description, interpretation, and more. For example: These tasks showcase VLMs’ ability to tackle a variety of challenges, including zero-shot and one-shot scenarios, where minimal prior training is required. How Do VLMs Work? Core Components VLMs typically consist of three main components: Workflow Adapters: The Key to Multimodality Adapters facilitate interaction between the image encoder and LLM. There are two primary types: Training VLMs Pre-Training VLMs are built on pre-trained LLMs and image encoders. Pre-training focuses on linking text and image modalities, as well as embedding world knowledge from visual data. Three types of data are used: Alignment Alignment fine-tunes VLMs for high-quality responses. This involves: Quality Evaluation Evaluating VLM performance involves: Revolutionizing Image Search with VLMs Incorporating VLMs into search engines transforms user experience by integrating text and image inputs. Previous Pipeline VLM-Powered Pipeline With VLMs, image and text inputs are processed together, creating a streamlined, accurate system that outperforms traditional pipelines by 28%. Conclusion Visual Language Models are a game-changer for AI, breaking down barriers between language and vision. From multimodal search engines to advanced problem-solving, VLMs unlock new possibilities in computer vision and beyond. The future of AI is multimodal—and VLMs are leading the way. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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December 5, 2024in Artificial Intelligence, Data, Technology
rise of digital workers
04Dec

Rise of Digital Workers

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

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December 4, 2024in Agentforce Platform, Artificial Intelligence, Data, Generative AI, Salesforce, Technology
AI is Revolutionizing Salesforce
29Nov

AI is Revolutionizing Salesforce

AI is Revolutionizing Salesforce: Transforming Sales Teams in the Era of AI Artificial Intelligence (AI) is reshaping the sales landscape, disrupting traditional processes, and redefining how businesses interact with customers. The rapid adoption of AI-native systems is altering how data is captured, how sales teams engage, and how the entire sales cycle is structured. The shift toward AI-driven solutions is fueling unprecedented opportunities for sales organizations to achieve more by doing less manual work. Success in this transformation will favor those who use AI to make smarter, data-driven decisions, shifting the focus from activities to meaningful achievements. From Rolodex to Real-Time Insights: The Evolution of Sales The history of sales is one of continual evolution. From the bartering days of ancient commerce to the introduction of Rolodexes in the mid-20th century, and later to early CRM tools like Act! and Siebel Systems, the industry has always innovated to meet changing customer needs. Salesforce’s arrival in 1999 brought CRM to the cloud, empowering sales teams with unparalleled accessibility. Yet, all these systems had one thing in common—they relied on human input. Logging calls, updating lead statuses, and noting feedback all depended on sales reps’ diligence. That dependency is now being disrupted by AI, which captures and processes data autonomously. AI-Native Systems: Capturing Context Without Human Input AI-native systems represent a seismic shift. Unlike traditional CRMs, these systems capture data in real-time without relying on human intervention. From emails and Slack messages to Zoom calls and social media interactions, AI aggregates unstructured data into actionable insights. This creates a rich, context-driven record of customer behavior, reducing reliance on manual entry and unlocking deeper understanding. Automating the Mundane: Eliminating Data Entry AI is erasing the inefficiencies of manual processes. Sales development representatives (SDRs) once spent countless hours cold-calling, sending follow-ups, and updating records—a monotonous grind that yielded limited value. Today, AI automates these tasks, enabling SDRs to focus on high-impact activities like relationship-building and deal-closing. This automation, often referred to as intelligent pipeline management, identifies prospects, crafts personalized outreach, and schedules meetings—effortlessly managing the early stages of the sales funnel. AI as a Partner: Voice Agents and Real-Time Coaching AI is not just automating tasks; it’s enhancing human performance. AI-powered voice agents can now assist sales reps during live calls by offering real-time coaching. When a prospect raises an objection, the AI provides instant suggestions based on historical data, empowering salespeople to respond more effectively. This real-time guidance helps sales teams navigate complex conversations with confidence, boosting close rates and accelerating results. Personalization at Scale: Tailored Engagement Across Pipelines Personalization has long been a cornerstone of effective sales, but AI has made it scalable. AI tools analyze customer behaviors and preferences, allowing sales teams to tailor messages, proposals, and outreach at an individual level—even for thousands of prospects. From detecting website visits to auto-generating customized content, AI enables hyper-relevant interactions that build stronger connections with leads and customers. Breaking Down Silos: Unifying Sales, Marketing, and Customer Success AI is bridging organizational divides. Historically, sales, marketing, and customer success operated in silos, each pursuing independent goals. AI aligns these functions around a shared understanding of the customer, fostering collaboration and a unified go-to-market strategy. By consolidating data from every customer touchpoint into a single system of record, AI empowers teams to work together seamlessly, ensuring a consistent and coordinated customer experience. Systems of Record for the AI Age: The Importance of Context Unlike traditional CRMs that rely on structured fields, AI-powered systems excel at capturing unstructured data—conversations, social media mentions, and survey responses. These systems provide the context sales teams need to make better decisions. This rich contextual data benefits not just sales but also product development, marketing, and customer success teams, enabling them to refine strategies and create more responsive organizations. Redefining Metrics: From Activities to Achievements Traditional sales metrics often emphasized activity—calls made, emails sent, meetings booked. AI is shifting the focus to outcomes. By tracking the quality and impact of interactions, rather than the volume, sales leaders can better understand what drives success and optimize their strategies accordingly. The Future of Sales: Empowered by AI AI is not replacing salespeople; it’s empowering them. By automating repetitive tasks and delivering actionable insights, AI frees up teams to focus on building relationships, solving problems, and closing deals. To thrive in this new era, organizations must embrace AI as a core part of their strategy. The question for sales leaders is no longer whether to adopt AI but how quickly they can leverage it to gain a competitive edge. Embrace the future of sales—where intelligent systems drive outcomes, and human ingenuity takes center stage. AI is revolutionizing Salesforce by helping businesses improve customer relationships, streamline operations, and make better decisions: Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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November 29, 2024in AI Tools, Artificial Intelligence, Data, Google, Salesforce
human centered ai
23Nov

Human-Centered AI

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

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November 23, 2024in Artificial Intelligence, Data, Generative AI
Value-Based Care Technologies
22Nov

Value-Based Care Technologies

Essential Technologies for Value-Based Care Success As healthcare providers increasingly adopt value-based care, they must invest in the right technologies and resources to succeed in this model, which incentivizes high-quality, cost-effective care. Value-Based Care Technologies tie reimbursement to care quality, making providers accountable for patient outcomes while providing resources to enhance care. As of 2021, nearly 60% of healthcare payments were already tied to value-based models, according to the Health Care Payment Learning and Action Network (HCP LAN). While partnerships can initiate value-based care, providers must invest in the right technology to fully achieve the intended outcomes. Health Information Exchange (HIE) A robust health information exchange (HIE) is fundamental to value-based care, as it enables providers and payers to access high-quality data seamlessly. HIE allows healthcare professionals to share patients’ medical information electronically across organizations, promoting care coordination by giving providers a comprehensive view of patient needs. For patients, HIE enables more informed involvement in their care by making their health data accessible across specialists, labs, and pharmacies. While joining an HIE may involve new technology investments and workflow adjustments, it ultimately enhances provider access to critical health data. Population Health Management Tools Population health management tools help providers assess health outcomes within groups rather than focusing on individuals alone. These tools aggregate and analyze data, allowing practices to identify high-risk patients and create targeted interventions. This not only enhances health outcomes but can also reduce costs by avoiding expensive treatments. Patient engagement tools, such as telehealth and remote patient monitoring, are essential in population health management, especially for monitoring high-risk patients when in-person care is not feasible. Digital surveys integrated within patient portals can provide insights into social determinants of health, adding a broader context to patient needs. Data Analytics Data analytics transform healthcare data into actionable insights across four types: descriptive, diagnostic, predictive, and prescriptive. Providers can use these analytics to reduce hospital readmissions, predict diseases, and identify chronic illnesses. Data integration and risk stratification capabilities are especially valuable in value-based care, enabling providers to track patient health outcomes effectively and prioritize high-risk cases. Artificial Intelligence & Machine Learning AI and machine learning support many data analytics functions, helping identify patient needs and easing administrative burdens. Given staffing shortages and burnout—reported by 63% of physicians in 2021, according to the American Medical Association (AMA)—AI can automate tasks like documentation, charting, and scheduling, allowing providers to focus more on patient care. Additionally, AI-driven automation in revenue cycle management tasks, such as billing and coding, can reduce the administrative workload associated with value-based care. Price Transparency Technology Price transparency empowers patients to seek cost-effective care, a core principle of value-based models. When providers comply with transparency regulations, patients can better understand their costs and make informed decisions. For providers, leveraging price transparency tools ensures compliance and facilitates partnerships with payers by enabling more effective negotiation, which supports the overall goals of value-based care. As healthcare continues shifting to value-based models, investing in these technologies is critical for providers aiming for long-term success. While these tools rdo equire substantial investment, they are essential for improving patient outcomes, optimizing care quality, and ensuring sustainability in value-based care. When evaluating and choosing healthcare technology tools, contact Tectonic for help. Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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November 22, 2024in AI Tools, Artificial Intelligence, Data, Life Sciences Cloud, Salesforce, Salesforce Development Services, Salesforce Foundations, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Salesforce Implementation Services, Salesforce Optimization, Technology

AI Data Quality

Data quality problems can rapidly derail an AI initiative, even if other aspects of the project are well planned. Mislabeled data points, an unbalanced sample or difficulty accessing stored data can lead models to generate incorrect predictions or fail after they’ve been deployed to production. Unfortunately, these challenges aren’t always obvious at the outset, which can lead teams to waste time and resources developing models that don’t work as intended. That’s why understanding data quality issues is essential for AI projects: By planning to prevent common problems, AI teams can build more reliable, scalable and safe systems. 9 common data quality issues in AI projects Understanding potential problems is an important early step in an AI project. Explore nine of the most frequently encountered data quality issues, such as biased or inconsistent data, sparsity and data silos. 1. Inaccurate, incomplete and improperly labeled data Many AI projects fail because their models rely on inaccurate, incomplete or improperly labeled data. Source data must be cleaned, prepared and labeled correctly. Data cleanliness is such an issue that the data preparation industry emerged to address it. While it might seem straightforward to clean gigabytes of data, imagine having petabytes or zettabytes to clean. Traditional approaches don’t scale, which has resulted in new AI-powered tools to help spot and clean data issues. 2. Too much data Because data is crucial to AI projects, you might think that more data is always better, but you can have too much data. A portion of data is usually unusable or irrelevant. Having to extract useful data from a more extensive data set wastes resources. Extra data might result in noise that can cause machine learning systems to learn from nuances and variances in the data rather than the more significant overall trend. Illustrated list of six elements of data quality: accuracy, completeness, consistency, timeliness, uniqueness and validity. Accurate, complete, consistent, timely, unique and valid data reduces problems in AI system development and improves model performance. 3. Too little data On the flip side, having too little data presents problems. Training a model on a small data set might produce acceptable results in a test environment. However, bringing a model from a proof-of-concept or pilot stage into production requires more data. In general, training on small data sets can produce models that demonstrate low complexity, bias or overfitting, leading to inaccuracy when working with new data. 4. Biased data Data can be selected from more extensive data sets in ways that don’t accurately convey the trends or distribution of the broader data set. In other ways, data could be derived from older information that might have resulted from human bias. Or there could be some issues with how data is collected or generated, resulting in a biased final outcome. 5. Unbalanced data Although everyone wants to minimize or eliminate bias from their data, this is much easier said than done. Unbalanced data sets can significantly hinder the performance of machine learning models by overrepresenting data from one community or group, while unnecessarily reducing the representation of another. Some approaches to fraud detection use unbalanced data sets. Most transactions are not fraudulent, so only a small portion of a data set represents fraud. If a model trained on this fraudulent data receives significantly more examples from one class versus another, the results will be biased toward the class with more examples. Conducting thorough exploratory data analysis is essential to discovering and solving such issues early. 6. Data silos Related to the issue of unbalanced data is the issue of data silos, where only a certain group or a limited number of individuals in an organization can access a data set. Data silos can result from technical challenges, restrictions in integrating data sets, or issues with proprietary or security access control of data. Data silos are also the product of structural breakdowns at organizations where only certain groups have access to certain data, as well as cultural issues where lack of collaboration among departments prevents data sharing. Regardless of the reason, data silos can limit the ability of those at a company working on AI projects to gain access to comprehensive data sets, possibly lowering the quality of results. 7. Inconsistent data Not all data is created equal. Just because you’re collecting information doesn’t mean you can — or should — use it. Training a model on clean but irrelevant data results in the same issues as training systems on poor-quality data. Inconsistent data goes hand in hand with irrelevant data. In many circumstances, the same records exist multiple times in different data sets but with different values, resulting in duplicates and inconsistencies. When dealing with multiple data sources, inconsistency indicates a data quality problem. 8. Data sparsity Data sparsity occurs when data is missing or when a data set contains insufficient specific expected values. Data sparsity can affect the performance of machine learning algorithms and their ability to calculate accurate predictions. If data sparsity is not identified, it can result in models being trained on noisy or insufficient data, reducing the effectiveness or accuracy of results. 9. Data labeling issues One of the fundamental types of machine learning, supervised machine learning, requires data to be labeled with correct metadata for machines to derive insights. Data labeling is complex and requires human resources to put metadata on various data types. This can be complex and expensive. Improperly labeled data is a challenge for in-house AI projects. Accurately labeled data ensures that machine learning systems establish reliable models for pattern recognition, forming the foundations of every AI project. Good-quality labeled data is paramount to accurately training the AI system on the data it is being fed. Why is data quality important in AI projects? Data quality is foundational to the success of AI projects because it directly affects machine learning models’ accuracy and reliability. High-quality data helps AI systems learn accurate patterns and generalize well to new information, leading to better performance in real-world contexts. Conversely, low-quality data leads to

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November 19, 2024in Artificial Intelligence, Data
gettectonic.com