Artificial Intelligence Archives - gettectonic.com - Page 11
AI tools for research

AI Tools for Research

10 AI Tools for Research Work This is a great list of resarch tools aided by AI. 1. Opinly AI AI-powered competitor research and analysis. Features: Link: Opinly AI 2. PDF Parser Tool to analyze, visualize, and communicate data for insights. Features: Link: PDF Parser 3. Heyday Turn data into insights. Features: Link: Heyday 4. Wisio Supercharge your academic writing with AI. Features: Link: Wisio 5. Silatus Fact-based research automation with AI. Features: Link: Silatus 6. Elicit An AI-powered research analysis assistant. Features: Link: Elicit 7. Eightify Learn from YouTube videos efficiently. Features: Link: Eightify 8. Aomni Helps B2B sellers build better buyer relationships and save time on prep work throughout the sales cycle. Features: Link: Aomni 9. Consensus An AI-powered search engine for research paper insights. Features: Link: Consensus 10. SciSpace by Typeset Chat with PDFs and conduct your literature review faster. Features: Link: SciSpace by Typeset Shushant Lakhyani writes and publishes Learn AI for free. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI Training Options

AI Training Options

As AI adoption accelerates, AI certifications and courses have proliferated, providing deeper knowledge of this rapidly evolving technology. AI Training Options. Numerous AI certifications cover the basics, so we’ve narrowed the field to 10 of the most diverse and comprehensive programs. AI Training Options Artificial intelligence is poised to become the key technology that drives business transformation and gives companies a competitive edge. According to a recent forecast by the International Data Corporation, global spending on AI—including AI-enabled applications, infrastructure, and related services—will more than double to $632 billion by 2028, growing at a compound annual rate of 29% between 2024 and 2028. AI helps businesses boost productivity by automating processes such as robotics and autonomous vehicles, while also supporting existing workforces with technologies like assisted and augmented intelligence. Companies are integrating AI across various sectors, including finance, healthcare, retail, smart home devices, fraud detection, and security surveillance. Why AI certifications are important: 10 of the best AI certifications and courses: Each certification offers unique benefits, whether you’re a beginner or an experienced professional aiming to stay ahead in AI-driven industries. Content updated September 2024. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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How Good is Our Data

How Good is Our Data?

Generative AI promises to significantly reshape how you manage your customer relationships, but it requires data that is accurate, updated, accessible, and complete. Why is this important? You may do something differently this quarter than you did last quarter, based on the latest data. But if your data is outdated or incorrect, that’s what the AI will use.  Generative AI focuses on creating new and original content, chat responses, designs, synthetic content or even deepfakes. It’s particularly valuable in creative fields and for novel problem-solving, as it can autonomously generate many types of new outputs. Generative Artificial Intelligence models often present inaccurate information as though it were correct. This is often caused by limited information in the system, biases in training data, and issues with the algorithm. These are commonly called ‘hallucinations‘ and they present a huge problem. When training your models for generative AI, you should first ensure high information excellence from top to bottom. To get your information house in order, remove duplicates, outliers, errors, and other things that can negatively affect how you make decisions. Then connect your data sources — marketing, sales, service, commerce – into a single record, updated in real time, so the AI can make the best recommendations.   McKinsey recently wrote, “Companies that have not yet found ways to harmonize and provide ready access to their information will be unable to unlock much of generative AI’s potentially transformative power.” Why is data important in generative AI? Aside from the cost factor, poor information quality can introduce unnecessary and harmful noise into the generative AI systems and models, leading to misleading answers, nonsensical output, or overall lower efficacy. What is high-quality data for AI? High-quality information is essential for AI systems to deliver meaningful results. Data quality possesses several key attributes: Accuracy: High-quality information is free from errors and inaccuracies. Inaccurate information can mislead AI models and produce unreliable outputs. Is AI 100 percent accurate? Because AI will still rely on your data for decision making and accuracy depends on the quality of your information. AI machines must be well-programmed to make sure the machine is making decisions based on the correct, available information. Also, privacy and security of the data are paramount. AI machines need to access information that is encrypted and secure. Understand that Generative AI is most effective at creating new data based on existing patterns and examples, with a focus on text and image data. Generative AI is most suitable for generating new data based on existing patterns and examples. It doesn’t actually think for itself. Yet. Known Limitations Of Generative AI Large language models (LLMs) are prone to “hallucinations” – generating fictitious information, presented as factual or accurate. This can include citations, publications, biographical information, and other information commonly used in research and academic papers. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Salesforce Pro Suite

Salesforce Pro Suite

Revolutionizing CRM: Introducing Salesforce Pro Suite In today’s dynamic business technology landscape, Salesforce has established itself as a leader in customer relationship management (CRM) solutions. The launch of Salesforce Pro Suite marks a significant milestone in their mission to empower businesses with cutting-edge tools designed to optimize operations, enhance customer engagement, and drive growth. This article explores the features, benefits, and potential of Salesforce Pro Suite, showcasing why it stands out as a transformative solution for businesses of all sizes. What is Salesforce Pro Suite? Salesforce Pro Suite is a comprehensive collection of integrated tools and services designed to augment the capabilities of Salesforce’s CRM platform. Tailored for modern businesses—from startups to large enterprises—it incorporates advanced functionalities such as artificial intelligence (AI), automation, and data analytics to boost productivity, foster collaboration, and facilitate informed decision-making. Unlock growth and deepen customer relationships with Pro Suite—the all-in-one CRM suite with marketing, sales, service, and commerce tools that scale with your business. Get the flexibility to automate tasks and customize your CRM to fit your specific needs with Pro Suite. Key Features of Salesforce Pro Suite Benefits of Salesforce Pro Suite Use Cases of Salesforce Pro Suite What Can You Do with Pro Suite? Conclusion Salesforce Pro Suite represents a significant advancement in CRM technology, offering a comprehensive suite of tools that cater to the diverse needs of modern businesses. By harnessing AI, automation, and advanced analytics, Pro Suite empowers organizations to optimize operations, enhance customer engagement, and make informed, data-driven decisions. Whether you’re a small startup or a large enterprise, Salesforce Pro Suite provides the scalability, flexibility, and security required to thrive in today’s competitive landscape. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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steps to embrace ai

Steps to Embrace AI

The world is evolving rapidly, with AI playing a transformative role. Despite concerns about AI’s impact on jobs, it has the potential to empower and simplify our lives. Rather than replacing humans, AI can automate routine tasks, allowing individuals to focus on more creative and value-added work. The future lies in human-AI collaboration, requiring us to prepare for a shift in roles and responsibilities.

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Generative AI Prompts with Retrieval Augmented Generation

Generative AI Cheat Sheets

Wanted to utilize this insight to share a link to some incredible AI cheat sheets compiled by Medium. Generative AI Cheat Sheets. Top 8 Cheat Sheets on AI Whether you need assistance building a Powerpoint Presentation, AI for enterprise, machine learning, podcast enhancement tools, large language models, efficient ChatGPT prompts, efficient use of emojis, journeys, or more. This list is pretty inclusive. Tectonic would like to share one additional tool we have been using internally. Fireflies. Firflies helps teams transcribe, summarize, search, and analyze voice conversations. When ChatGPT made its debut in late 2022, it sparked global recognition of the transformative capabilities of artificial intelligence (AI). This groundbreaking chatbot represents one of the most significant advancements in AI history. Unlike traditional AI systems that analyze or categorize existing data, generative AI has the remarkable ability to create entirely new content, spanning text, images, audio, synthetic data, and more. This innovation is poised to revolutionize human creativity and productivity across industries, including business, science, and society as a whole. From ChatGPT to DALL-E, the latest wave of generative AI applications has emerged from foundation models, sophisticated machine learning systems trained on massive datasets encompassing text, images, audio, or a combination of these data types. Recent advancements now enable companies to develop specialized models for image and language generation based on these foundation models, most of which are large language models (LLMs) trained on natural language. The power of these models lies not only in their scale but also in their adaptability to diverse tasks without the need for task-specific training. Techniques like zero-shot learning and in-context learning allow models to make predictions and generate responses even in domains they haven’t been explicitly trained on. As a result, companies can leverage these models to address a wide range of challenges, from customer service automation to product design. The introduction of pre-trained foundation models with unprecedented adaptability is expected to have profound implications. According to Accenture’s 2023 Technology Vision report, 97% of global executives believe that foundation models will revolutionize how and where AI is applied, enabling seamless connections across different data types. To thrive in this evolving landscape, businesses must leverage the full potential of generative AI. To expedite implementation, organizations can readily access foundation models through APIs. However, customization and fine-tuning are necessary to tailor these models to specific use cases and maximize their effectiveness. By harnessing generative AI, companies can enhance efficiency, drive innovation, and gain a competitive edge in the market. As generative AI continues to evolve, its impact will only multiply. Companies will increasingly rely on these technologies to streamline workflows, optimize processes, and unlock new opportunities for growth and innovation. With the global AI market projected to reach nearly trillion by 2030, the future holds immense potential for companies to leverage generative AI in solving complex problems and driving transformative change. Generative AI encompasses various machine learning techniques, including transformer models, generative adversarial networks (GANs), and variational autoencoders (VAEs). These technologies underpin a wide range of applications, from natural language processing to image generation, enabling businesses to approach tasks in innovative ways. While generative AI presents unprecedented opportunities, it also raises ethical and security concerns. It is essential for companies to adopt responsible AI practices and ensure the safe and ethical use of these technologies. By embracing generative AI and investing in the necessary infrastructure and talent, businesses can unlock its full potential and drive sustainable growth in the digital era. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Healthcare Cloud Marketplace

Healthcare Cloud Marketplace

Healthcare Cloud Computing Market: A Comprehensive Overview and Future Outlook Vantage Market Research Report: Insights into Healthcare Cloud Computing by 2030 WASHINGTON, D.C., February 6, 2024 /EINPresswire.com/ — The global Healthcare Cloud Marketplace was valued at USD 38.25 billion in 2022 and is projected to grow at a compound annual growth rate (CAGR) of 18.2% from 2023 to 2030, reaching approximately USD 145.86 billion by 2030, according to Vantage Market Research. This technology allows healthcare organizations to utilize cloud-based services for data storage, management, and analysis, providing numerous benefits such as cost efficiency, scalability, flexibility, security, and interoperability. It enhances healthcare delivery by enabling seamless data access and sharing across various locations, devices, and networks. Additionally, cloud computing supports the integration of advanced technologies like artificial intelligence, big data analytics, telehealth, and mobile health, driving progress in disease diagnosis, treatment, and prevention. Market Dynamics The market’s growth is fueled by several key factors, including the increasing demand for healthcare IT solutions, the rising prevalence of chronic diseases, the widespread adoption of electronic health records (EHRs), and evolving payment models and regulatory frameworks. The exponential increase in healthcare data, encompassing patient records, imaging scans, and research findings, necessitates scalable storage and analysis solutions. Cloud computing meets this need by providing flexible and scalable infrastructure, accommodating data growth without overburdening IT systems. The rise of telehealth and remote patient monitoring further boosts the demand for secure, cloud-based platforms that facilitate efficient data exchange. However, stringent data privacy regulations like HIPAA and GDPR require robust security measures, compelling healthcare organizations to seek cloud providers that offer strong compliance and access controls. This need for a balance between agility and security shapes the healthcare cloud computing market’s future trajectory. Leading Companies in the Global Healthcare Cloud Computing Market Market Segmentation By Product: By Deployment: By Component: By Pricing Model: By Service Model: Key Trends and Opportunities The healthcare cloud computing market is witnessing significant trends, including the adoption of hybrid and multi-cloud models, which combine the benefits of both public and private clouds. The integration of artificial intelligence (AI) and machine learning (ML) into cloud-based healthcare applications is opening new avenues for personalized medicine, clinical decision support, and drug discovery. Moreover, blockchain technology is emerging as a solution to enhance data security and patient privacy, addressing critical industry concerns. Key Findings: Opportunities: Healthcare Cloud Marketplace The healthcare cloud computing market is poised for robust growth, driven by the increasing demand for scalable and secure data management solutions. As healthcare organizations navigate challenges related to data privacy and security, robust cloud solutions and supportive government policies will be essential in unlocking the full potential of cloud computing in 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

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AI Capability Maturity Model

AI Capability Maturity Model

The AI Capability Maturity Model (AI CMM), devised by the Artificial Intelligence Center of Excellence within the GSA IT Modernization Centers of Excellence (CoE), functions as a standardized framework for federal agencies to evaluate their organizational and operational maturity levels. It is equally useful for private organizations in aligning them with predefined objectives. Instead of imposing normative capability assessments, the AI CMM concentrates on illuminating significant milestones indicative of maturity levels along the AI journey. The AI Capability Maturity Model focuses primarily on the development of AI capabilities within an organization. It evaluates an organization’s maturity across four main areas: data, algorithms, technology, and people. Serving as a valuable tool, the AI CMM assists organizations in shaping their unique AI roadmap and investment strategy. The outcomes derived from AI CMM analysis empower decision-makers to identify investment areas that address immediate goals for rapid AI adoption while aligning with broader enterprise objectives in the long run. Maturity vs capability models A maturity model tends to measure activities, such as whether a certain tool or process has been implemented. In contrast, capability models are outcome-based, which means you need to use measurements of key outcomes to confirm that changes result in improvements. AI development rooted in sound software practices underpins much of the content discussed in this and other chapters. Though not explicitly delving into agile development methodology, Dev(Sec)Ops, or cloud and infrastructure strategies, these elements are fundamental to the successful development of AI solutions. The AI CMM elaborates on how a robust IT infrastructure leads to the most successful development of an organization’s AI practice. What are the maturity levels of AI? What are the maturity levels of Artificial Intelligence? Or it can be measured this way. AI Maturity Model Why is AI maturity important? The AI Maturity Assessment is a process designed to help organizations evaluate their current AI capabilities, identify gaps and areas for improvement, and develop a roadmap to build a more effective AI program. Organizational Maturity Areas Organizational maturity areas represent the capacity to embed AI capabilities across the organization. Two approaches, top-down and user-centric, offer distinct perspectives on organizational maturity. Top-Down, Organizational View Bottom-Up, User-centric View Operational Maturity Areas Operational maturity areas represent organizational functions impacting the implementation of AI capabilities. Each area is treated as a discrete capability for maturity evaluation, yet they generally depend on one another. PeopleOps CloudOps DevOps SecOps DataOps MLOps AIOps AI Capability Maturity Model This comprehensive overview of organizational and operational maturity areas underlines the multifaceted nature of AI implementation and the critical role played by diverse elements in ensuring success across different layers of an organization. How AI is transforming the world? AI-powered technologies such as natural language processing, image and audio recognition, and computer vision have revolutionized the way we interact with and consume media. With AI, we are able to process and analyze vast amounts of data quickly, making it easier to find and access the information we need. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Unfolding AI Revolution

Unfolding AI Revolution

Ways the AI Revolution is Unfolding The transformative potential of artificial intelligence (AI) is being explored by James Manyika, Senior VP of Research, Technology, and Society at Google, and Michael Spence, Nobel laureate in economics and professor at NYU Stern School of Business, in their recent article, “The Coming AI Economic Revolution: Can Artificial Intelligence Reverse the Productivity Slowdown?” Published in Foreign Affairs, the article outlines the conditions necessary for an AI-powered economy to thrive, including policies that augment human capabilities, promote widespread adoption, and foster organizational innovation. Manyika and Spence highlight AI’s potential to reverse stagnating productivity growth in advanced economies, stating, “By the beginning of the next decade, the shift to AI could become a leading driver of global prosperity.” However, the authors caution that this economic revolution will require robust policy frameworks to prevent harm and unlock AI’s full potential. Here are the key insights from their analysis: 1. The Great Slowdown The rapid advancements in AI arrive at a critical juncture for the global economy. While technological innovations have surged, productivity growth has stagnated. For instance, total factor productivity (TFP), a key contributor to GDP growth, grew by 1.7% in the U.S. between 1997 and 2005 but has since slowed to just 0.4%. This slowdown is exacerbated by aging populations and shrinking labor forces in major economies like China, Japan, and Italy. Without a transformative force like AI, economic growth could remain stifled, characterized by higher inflation, reduced labor supply, and elevated capital costs. 2. A Different Digital Revolution Unlike the rule-based automation of the 1990s digital revolution, AI has shattered previous technological constraints. Advances in AI now enable tasks that were previously unprogrammable, such as pattern recognition and decision-making. AI systems have surpassed human performance in areas like image recognition, cancer detection, and even strategic games like Go. This shift extends the impact of technology to domains previously thought to require exclusively human intuition and creativity. 3. Quick Studies Generative AI, particularly large language models (LLMs), offers exceptional versatility, multimodality, and accessibility, making its economic impact potentially transformative: Applications range from digital assistants drafting documents to ambient intelligence systems that automate homes or generate health records based on patient-clinician interactions. 4. Creative Instruction Despite its promise, AI has drawn criticism for issues like bias, misinformation, and the potential for job displacement. Critics highlight that AI systems may amplify societal inequities or produce unreliable outputs. However, research suggests that AI will primarily augment work rather than eliminate it. While about 10% of jobs may decline, two-thirds of occupations will likely see AI enhancing specific tasks. This shift emphasizes collaboration between humans and intelligent machines, requiring workers to develop new skills. Studies, such as MIT’s Work of the Future task force, reinforce that automation will not lead to a jobless future but rather to evolving roles and opportunities. 5. With Us, Not Against Us The full benefits of AI will not materialize if its deployment is left solely to market forces. Proactive measures are necessary to maximize AI’s positive impact while mitigating risks. This includes fostering widespread adoption of AI in ways that empower workers, enhance productivity, and address societal challenges. Policies should prioritize accessibility and equitable diffusion to ensure AI serves as a force for inclusive economic growth. 6. The Real AI Challenge Generative AI has the potential to spark a productivity renaissance at a time when the global economy urgently needs it. Yet, Manyika and Spence caution that AI could exacerbate existing economic disparities if not guided effectively. They argue that focusing solely on existential threats overlooks the broader risks posed by inequitable AI deployment. Instead, a positive vision is needed—one that prioritizes AI as a tool for global economic progress, equitable growth, and generational prosperity. “Harnessing the power of AI for good will require more than simply focusing on potential damage,” the authors conclude. “It will demand effective measures to turn that vision into reality.” The unfolding AI revolution offers immense opportunities, but realizing its full potential requires thoughtful action. By addressing risks and fostering innovation, AI could reshape the global economy for the better. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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How to Implement AI for Business Transformation

How to Implement AI for Business Transformation

Harnessing the Power of AI for Business Transformation The age of artificial intelligence (AI) is here. How to Implement AI for Business Transformation? Once a niche technology confined to research labs and the realm of science fiction, AI has now become a mainstream force. Today, an estimated 35% of businesses are leveraging AI to enhance products, boost efficiency, and gain a competitive edge. However, for companies yet to begin their AI journey, the path to implementation can seem daunting. So how can organizations navigate the complexities of AI and unlock its potential to drive success? This comprehensive guide is designed to empower businesses to confidently adopt AI. We’ll break down what AI is, assess your organization’s readiness, help you develop a robust AI strategy, and explore how to implement and integrate AI across operations. Ultimately, this insight will show you how to embrace AI for continuous innovation, helping automate tasks, uncover insights, and future-proof your business. AI Era Demands an Intelligent Data Infrastructure AI consulting services and digital transformation partners like Tectonic underscore the technology’s immense value, helping organizations evaluate, implement, and scale AI initiatives. However, knowing where to start and who to trust can be challenging. This guide will provide best practices for planning and executing AI projects, helping you make informed decisions when selecting solutions and partners. By the end, your organization will be equipped with the knowledge and confidence needed to make AI a powerful competitive advantage. Understanding the AI Landscape Before diving into AI implementation, it’s important to understand what artificial intelligence is and the wide array of applications it offers. What is Artificial Intelligence? Artificial intelligence (AI) refers to software and machines designed to perform tasks that typically require human intelligence—such as visual perception, speech recognition, decision-making, and language translation. AI is already deeply integrated into many everyday products and services, including: Machine Learning Basics At the core of most AI systems is machine learning (ML), which involves training algorithms on vast datasets, enabling them to learn from examples without being explicitly programmed for every scenario. There are three main types of machine learning: Beyond ML, fields like natural language processing (NLP) focus on understanding human language, while computer vision analyzes visual content such as images and video. Real-World AI Applications Understanding the fundamentals of AI helps organizations align their needs with its capabilities. Common business use cases for AI include: Armed with this knowledge, businesses can better evaluate how AI fits into their goals and operations. Developing a Comprehensive AI Strategy Once you understand the AI landscape, the next step is developing a strategic plan to guide implementation. Establishing an AI Vision and Objectives AI adoption must align with clear financial and operational goals. Leadership teams should identify: Aligning stakeholders and executive leaders around specific use cases will drive urgency, investment, and commitment. AI Ethics and Governance AI adoption also requires guidelines for ethical usage, transparency, and accountability. Organizations should consider: Establishing these frameworks early ensures responsible and transparent AI usage. Resourcing an AI Program AI implementation requires the right talent and resources. Budget considerations should include: A Phased AI Adoption Roadmap Rather than attempting to scale AI all at once, organizations should adopt a phased approach: This roadmap balances short-term impact with long-term scalability. Choosing the Right AI Implementation Approach With your strategy in place, the next decision is how to implement AI. Three primary approaches are: The choice depends on your organization’s internal capabilities, desired level of customization, and timeline. Integrating AI into Your Operations Successful AI implementation requires careful planning and integration with existing operations. Develop an Integration Plan Consider how AI will interact with existing systems and workflows: Address Security and Privacy Ensure that AI systems comply with data privacy regulations and security protocols, especially when handling sensitive information. Drive Adoption Through Training Help staff understand how AI will augment their roles by providing training on how the algorithms work and how to interact with AI systems effectively. Monitor for Model Decay Implement processes to monitor and retrain models as needed to ensure continued performance and reliability. Embracing AI for Continuous Improvement AI should be viewed as an ongoing investment, driving continuous improvement across the organization. Encourage a Data-Driven Culture Empower teams to identify new AI use cases and experiment with AI-driven solutions. Provide the tools and frameworks to facilitate this culture of innovation. Foster Responsible AI Ensure that AI systems are transparent, accountable, and designed to augment human decision-making responsibly. Commit to Reskilling As AI capabilities evolve, continually upskill employees to ensure your workforce remains at the forefront of technological advancements. Unlocking the Future of AI The potential of AI to revolutionize businesses is clear. However, achieving success requires more than just technical capabilities. It demands thoughtful planning, strategic alignment, and a commitment to continuous improvement. By following this guide, your organization can confidently implement AI to unlock powerful data-driven insights, automate tasks, and achieve lasting competitive advantage. The future of AI is full of possibilities—are you ready to seize them? Tectonic is ready to help. How to Implement AI for Business Transformation Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Jobs AI Could Take

Jobs AI Could Take

AI’s Impact on Creative Professions AI is poised to replace designers, writers, illustrators, filmmakers, and many other professional roles within the creative services industry. This transition may occur rapidly or gradually, but the inevitability remains. Disregard the notion that AI cannot perform deep-feeling work due to its lack of emotions. Similarly, dismiss the idea that the value of human-crafted work will surge as AI becomes more prevalent. Jobs AI Could Take and jobs it can’t. There are a few artisans, like hand-dipped candle makers, who still charge good prices for a handmade product. And there are many companies that mass produce candles far cheaper. However, the artisians are a small fraction compared to the thousands of craftsmen who existed before the advent of factories. Once AI has full access to publisher archives and the gamut of human emotions documented therein, it will be capable of replicating feelings just as easily. Historically, artisans displaced by machines have faced similar fears. Earning a living as a creative has always been challenging, and this transition will not be any different. Let’s face it. Each of the four Industrial Revolutions have caused fears, displaced workers, and created new jobs. Interestingly, the first three were each about a century apart. And the fourth only 4 short decades. Adapting to AI in Creative Roles Creatives will still possess their passions and skills, but the key question is how to make a living. Embracing AI and integrating it into your workflow is crucial. AI will transform the creative process. Your painting or drawing or creating may not continue to be your full time income, but it will become more valuable as less and less people are doing it. For instance, clients are using AI to expedite writing and strategy development, create packaging designs, and generate variations. In filmmaking, AI assists with storyboarding and editing. Even illustrators are using AI for initial sketches, with a foreseeable shift to using it for final art. Combining roles like writing and design with AI support is becoming more common. AI could enable writers to become proficient in design and illustration, and vice versa. Filmmakers may find new ways to expand their roles, potentially making existing categories less distinct. Current AI Developments in Creative Fields AI is already driving business decisions, with corporations reducing costs by replacing human roles. Here are some current trends: Emphasizing Human Connections Despite AI’s rise, human interaction remains irreplaceable. Profits depend on people making purchases, and corporate employees need trusted individuals to help them sell products and services. Maintaining personal relationships is crucial for success in a world increasingly influenced by AI. Focusing on real, person-to-person connections is essential. Building and nurturing relationships with fellow professionals and clients can provide lasting benefits. Engage in genuine interactions, listen actively, and develop strong interpersonal skills. Sharing stories and demonstrating vulnerability and empathy can strengthen these connections. Leveraging Personal Strengths and Jobs AI Could Take Continue honing your craft and keeping your community informed about your work. Focus on clients who value your expertise and are likely to invest in your services. Opportunities may lie with corporations or wealthy individuals who require your unique skills. Exploring alternative income sources beyond the corporate world can also be beneficial. Some creatives are transitioning to selling their work to communities with shared interests. Ultimately, your unique talents and personal touch are irreplaceable. By embracing AI while maintaining strong human connections, you can navigate the changing landscape and continue to thrive in your creative career. Jobs AI Could Take and why that might not be a terrible thing. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI's Impact on the Workforce

AI’s Impact on the Workforce

According to McKinsey, generative AI has the potential to contribute between $2.6 trillion and $4.4 trillion in value to the global economy across various industries, spanning banking, retail, high tech, healthcare, and life sciences. Its impact is expected to reach diverse professions, including customer operations, marketing and sales, software engineering, and research and development. The influence of AI on the workforce is significant. A report by Goldman Sachs suggests that AI could replace the equivalent of 300 million full-time jobs, affecting a quarter of work tasks in the US and Europe. However, it also brings forth new job opportunities and a productivity boom. Despite concerns about job displacement, AI is anticipated to generate numerous new opportunities. Roles like prompt engineer and AI product manager are emerging, with a Salesforce-sponsored IDC white paper predicting a surge in demand for positions such as data architects, AI ethicists, and AI solutions architects over the next 12 months. The report also forecasts the creation of 11.6 million new jobs within the Salesforce ecosystem alone over the next six years. Recent advancements in generative AI, exemplified by products like ChatGPT with 100 million monthly active users in two months, have reignited discussions about automation’s impact on jobs. While the extent of disruption remains unknown, developers, users, and policymakers should consider its effects on workers. To address challenges and opportunities, Majority Leader Chuck Schumer has launched a SAFE Innovation Framework, emphasizing worker security. The Biden administration is developing a National AI Strategy to address economic and job impacts. For individuals in the workforce, there’s an opportunity to cultivate existing skills and acquire new ones through platforms like Salesforce’s Trailhead, Coursera, and LinkedIn. AI’s impact on jobs involves eliminating repetitive tasks, allowing individuals to focus on more strategic and creative aspects of their roles. In fields like sales, customer service, marketing, healthcare, finance, and graphic design, AI will transform roles and create new opportunities. Chris Poole, AI Technical Consulting Lead in Salesforce’s global AI practice, envisions AI becoming ingrained in every aspect of our lives, contributing to fascinating evolution across various fields. The scale of AI adoption’s impact on workers, especially with generative AI tools, remains uncertain. Potential effects include replacing, complementing, or freeing workers for more productive tasks, or creating new jobs. A Goldman Sachs estimate suggests that about two-thirds of current jobs are exposed to some degree of AI automation, with generative AI potentially substituting up to one-fourth of current work. McKinsey Global Institute estimates that 29.5 percent of all hours worked could be automated by 2030. Regarding job impact, professional occupations associated with clerical work in finance, law, and business management are most exposed to AI. However, AI is also concurrently creating many new jobs. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Cloud Based Business Solutions

What Can’t Generative AI Do?

There is a tremendous amount of discussion about all the capabilities of generative AI, but once in a while it doesn’t hurt to look at the other side of the AI coin. The USC Library published a piece in October of 2023 that did just that. In addition to the identified limitations discussed below, generative AI may be susceptible to issues that have yet to be uncovered or fully grasped. Clearly we learn new things about it every day. What Can’t Generative AI do? Large language models (LLMs) are susceptible to “hallucinations,” producing fictional information presented as factual or accurate. This includes citations, publications, biographical details, and other data commonly used in research and academic papers. Furthermore, answers generated by LLMs may be incorrect, often presented as correct or authoritative. ChatGPT has been known to “make things up” when it’s last data load didn’t cover the time frame asked to generate content about. The fundamental structure of generative AI models, coupled with frequent updates, makes content reproduction challenging. This poses a significant challenge in research and academia, where reproducibility is crucial for establishing credibility. Generative AI models don’t function as databases of knowledge. Instead, they attempt to synthesize and replicate the information they were trained on. This complexity makes it exceptionally difficult to validate and properly attribute the sources of their content. Generative AI models have no reason to believe any information it has is inaccurate. When asked to tell me how the sky was purple, ChatGPT explained both why the daytime sky is normally blue and reasons from volcanic ash to pollution that it might “appear” purple. But when asked who owns Twitter, ChatGPT refers to it as a publicly traded entity as it’s last data load was prior to Elon Musk purchasing and renaming Twitter to X. Is the Data Up to Date? Many common generative AI tools lack internet connectivity and cannot update or verify the content they generate. Additionally, the nature of generative AI models, especially when provided with simple prompts, can lead to content that is overly simplistic, of low quality, or overly generic. When asked for the weather forecast, Chat GPT replied, “I’m sorry, but I don’t have the capability to provide real-time information, including current weather forecasts. Weather conditions can change rapidly, and it’s important to get the most up-to-date information from a reliable source.” Several generative AI models, including ChatGPT, are trained on data with cutoff dates. Thus resulting in outdated information or an inability to provide answers about current events. In some instances, the data cutoff date may not be explicitly communicated to the user. The capabilities of generative AI are obviously limited by outdated data. Data Privacy Precautions: Exercise extra caution when dealing with private, sensitive, or identifiable information, whether directly or indirectly, regardless of using a generative AI service or hosting your own model. While some generative AI tools permit users to set their data retention policies, many collect user prompts and data, presumably for training purposes. USC researchers, staff, and faculty should particularly avoid sharing student information (a potential FERPA violation), proprietary data, or other controlled/regulated information. Salesforce recognizes this. According to their news and insights page, companies are actively embracing generative AI to power business growth. Building trustworthy generative AI requires a firm foundation at the inception of AI development. Salesforce published an overview of their five guidelines for the ethical development of generative AI that builds on their Trusted AI Principles and AI Acceptable Use Policy. The guidelines focus on accuracy, safety, transparency, empowerment, and sustainability – helping Salesforce AI engineers create ethical generative AI from the start. Additional Considerations: Apart from providing direct access to generative AI tools, many companies are integrating generative AI functionality into existing products and application. Tools such as Google Workspace, Microsoft Office, Notion, and Adobe Photoshop to name a few. Extra care should be taken when using these tools for research and academic work. Be careful especially to avoid the use of auto-completion for sentences or generating text without explicit permission. When working with images or videos, clearly communicate and attribute the use of generative AI assistance. Detecting Generative AI: In an effort to counter undisclosed and inappropriate uses of generative AI content, many organizations are developing generative AI detectors. These tools use AI to flag content created by generative AI. However, these tools can be unreliable and have erroneously flagged student content as AI-generated when it was created by a human. Relying solely on these tools to identify the origin of an assignment or work is not advisable. I played with one such tool using content solely written by generative AI. Amazingly it received a 99% human generated score. I rewrote the content in my own words and the score dropped by 20%. In April 2023, Turnitin introduced a preview of their AI detection tool, available to USC instructors via the Turnitin Feedback Studio. When in doubt, professors should engage with their students to better understand if and how generative AI tools were used. This interaction provides an essential opportunity for both parties to discuss the nuances of the technology. Thereby they can address any questions or concerns. Determining how and when the capabilities of generative AI is useful for you, is not ever going to be a cut and dry process. By Shannan Hearne, Tectonic Salesforce Marketing Consultant Like1 Related Posts 50 Advantages of Salesforce Sales Cloud According to the Salesforce 2017 State of Service report, 85% of executives with service oversight identify customer service as a Read more Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more Salesforce Data Studio Data Studio Overview Salesforce Data Studio is Salesforce’s premier solution for audience discovery, data acquisition, and data provisioning, offering access Read

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2024 AI and Machine Learning Trends

2024 AI and Machine Learning Trends

In 2023, the AI landscape experienced transformative changes following the debut of ChatGPT in November 2022, a landmark event for artificial intelligence. 2024 AI and Machine Learning Trends ahead, AI is set to dramatically alter global business practices and drive significant advancements across various sectors. Organizations are shifting their focus from experimental initiatives to real-time applications, reflecting a more mature understanding of AI’s capabilities while still being intrigued by generative AI technologies. Key AI and Machine Learning Trends for 2024 Here are the top trends shaping the AI and machine learning landscape for 2024: 1. Agentic AIAgentic AI is evolving from reactive to proactive systems. Unlike traditional AI that primarily responds to user inputs, these advanced AI agents demonstrate autonomy, proactivity, and the ability to independently set and pursue goals. 2. Open-Source AIOpen-source AI is democratizing access to sophisticated AI models and tools by offering free, publicly accessible alternatives to proprietary solutions. This trend has seen significant growth, with notable competitors like Mistral AI’s Mixtral models and Meta’s Llama 2 making strides in 2023. 3. Multimodal AIMultimodal AI integrates various types of inputs—such as text, images, and audio—mimicking human sensory capabilities. Models like GPT-4 from OpenAI showcase this ability, enhancing applications in fields like healthcare by improving diagnostic precision. 4. Customized Enterprise Generative AI ModelsThere is a rising interest in bespoke generative AI models tailored to specific business needs. While broad tools like ChatGPT remain widely used, niche-specific models are increasingly popular for their efficiency in addressing specialized requirements. 5. Retrieval-Augmented Generation (RAG)RAG combines text generation with information retrieval to boost the accuracy and relevance of AI-generated content. By reducing model size and leveraging external data sources, RAG is well-suited for business applications that require up-to-date factual information. 6. Shadow AIShadow AI, which refers to user-friendly AI tools used without formal IT approval, is gaining traction among employees seeking quick solutions or exploring new technologies. While it fosters innovation, it also raises concerns about data privacy and security. Looking Ahead to 2024 These trends highlight AI and machine learning’s expanding role across industries in 2024. Organizations must adapt to these advancements to remain competitive, balancing innovation with strong governance frameworks to ensure security and compliance. Staying informed about these developments will be crucial for leveraging AI’s transformative potential in the coming year. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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The Promise of AI in Health Outcomes

The Promise of AI in Health Outcomes

As President Biden has highlighted, artificial intelligence (AI) holds tremendous promise and potential peril. This is especially true in healthcare. On October 30, the President underscored his commitment by signing a landmark Executive Order aimed at governing AI development and use to improve health outcomes for Americans while safeguarding their security and privacy. The Biden-Harris Administration is leveraging every tool at its disposal to advance responsible AI in healthcare. However, U.S. government action alone cannot achieve the bold vision laid out by the President. By integrating AI into their platform, Salesforce aims to empower public health organizations with actionable insights and predictive analytics. From disease surveillance to population health management, AI-driven solutions have the potential to revolutionize how we approach public health initiatives. Therefore, policy priorities include managing and measuring the environmental impacts of AI by requiring emissions disclosures, adding environmental impact as a risk factor, and establishing efficiency standards for high-risk AI systems. In response to the Administration’s leadership, leading healthcare providers and payers have announced voluntary commitments to the safe, secure, and trustworthy use of AI in healthcare. These commitments build on ongoing efforts by the Department of Health and Human Services (HHS), the AI Executive Order, and earlier commitments from 15 leading AI companies to develop models responsibly. Today, 28 providers and payers have joined these commitments, including Allina Health, Bassett Healthcare Network, Boston Children’s Hospital, Curai Health, CVS Health, Devoted Health, Duke Health, Emory Healthcare, Endeavor Health, Fairview Health Systems, Geisinger, Hackensack Meridian, HealthFirst (Florida), Houston Methodist, John Muir Health, Keck Medicine, Main Line Health, Mass General Brigham, Medical University of South Carolina Health, Oscar, OSF HealthCare, Premera Blue Cross, Rush University System for Health, Sanford Health, Tufts Medicine, UC San Diego Health, UC Davis Health, and WellSpan Health. The commitments align with the “FAVES” principles—Fair, Appropriate, Valid, Effective, and Safe. Under these principles, companies commit to informing users when they receive content that is largely AI-generated and not reviewed by humans. They will adhere to a risk management framework to monitor and address potential harms of AI applications. Additionally, they pledge to develop AI solutions responsibly, advancing health equity, expanding access to care, making care affordable, improving care coordination, reducing clinician burnout, and enhancing patient experiences. Healthcare is an essential service, and quality care can be a matter of life and death. AI-enabled tools used for clinical decisions must undergo appropriate testing, risk mitigations, and human oversight to avoid costly or dangerous errors. AI diagnoses can be biased if not trained on diverse data, and AI’s data-collection capabilities could create privacy risks. Addressing these risks is crucial. Despite these risks, AI holds enormous potential to benefit patients, doctors, and hospital staff. AI can help doctors deliver higher-quality, more empathetic care and cut healthcare costs by hundreds of billions of dollars annually. It can also help patients make more informed health choices by better understanding their conditions and needs. Consider some examples: Each year, hospitals produce 3.6 billion medical images worldwide. AI helps doctors analyze images more quickly and effectively, detecting signs of breast cancer, lung nodules, and other conditions earlier than ever before. AI is also streamlining drug development, matching drug targets with new molecules faster and cheaper, translating to better care for patients. Additionally, new generative AI applications can alleviate clinician burnout by automating data extraction, form population, note recording, and patient communications. The Promise of AI in Health Outcomes To understand AI applications and the necessary risk-mitigation measures, the Biden-Harris Administration has engaged with healthcare providers, payers, academia, civil society, and other stakeholders. These engagements have informed the Administration’s approach, including the President’s October AI Executive Order, which tasks HHS with a wide range of actions to advance safe, secure, and trustworthy AI. These actions include developing frameworks, policies, and potential regulations for responsible AI deployment, documenting AI-related safety incidents, prioritizing grants for innovation in underserved communities, and ensuring compliance with nondiscrimination laws in AI deployment in healthcare. The private-sector commitments announced today are a critical step in our whole-of-society effort to advance AI for the health and well-being of Americans. These 28 providers and payers have stepped up, and we hope more will join these commitments in the coming weeks. The Promise of AI in Health Outcomes has been addressed by governments everywhere. In March 2024, Salesforce strengthened its AI commitment to healthcare. Salesforce’s Einstein 1 Platform powers Einstein Copilot with your healthcare organization’s unique data and metadata from Data Cloud to capture and summarize patient details, quickly update patient and member information, and automate manual processes Assessment Generation digitizes paper assessments and surveys to capture and track patient data Customers like Baptist Health South Florida and HarmonyCares are using Salesforce to personalize patient interactions and create a single, unified view of each patient Today, Salesforce announced AI and data innovations for CRM to help make healthcare operations more efficient and personalized. Einstein Copilot: Health Actions, a conversational AI assistant that will deliver trusted AI responses grounded with your healthcare organization’s own trusted and private data, Assessment Generation, and Data Cloud for Health help automate and streamline clinical summaries, deliver more personalized communication, and help compile tailored patient assessments faster for care teams, all from a single platform. These new innovations are powered by Salesforce’s Einstein 1 Platform, which helps organizations safely unlock their data to create better patient experiences and augment employee productivity. Why it matters: Nearly a quarter of U.S. healthcare spending is wasted on administrative costs, presenting a potential cost savings of up to $320 billion for healthcare organizations, according to McKinsey and Co. AI could be the solution, with recent Forrester data revealing that 82% of healthcare data leaders say AI is a top focus area that will drive operational efficiency.  Content updated April 2024. 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

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