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AI and Disability
28Oct

AI and Disability

Dr. Johnathan Flowers of American University recently sparked a conversation on Bluesky regarding a statement from the organizers of NaNoWriMo, which endorsed the use of generative AI technologies, such as LLM chatbots, in this year’s event. Dr. Flowers expressed concern about the implication that AI assistance was necessary for accessibility, arguing that it could undermine the creativity and agency of individuals with disabilities. He believes that art often serves as a unique space where barriers imposed by disability can be transcended without relying on external help or engaging in forced intimacy. For Dr. Flowers, suggesting the need for AI support may inadvertently diminish the perceived capabilities of disabled and marginalized artists. Since the announcement, NaNoWriMo organizers have revised their stance in response to criticism, though much of the social media discussion has become unproductive. In earlier discussions, the author has explored the implications of generative AI in art, focusing on the human connection that art typically fosters, which AI-generated content may not fully replicate. However, they now wish to address the role of AI as a tool for accessibility. Not being personally affected by physical disability, the author approaches this topic from a social scientific perspective. They acknowledge that the views expressed are personal and not representative of any particular community or organization. Defining AI In a recent presentation, the author offered a new definition of AI, drawing from contemporary regulatory and policy discussions: AI: The application of specific forms of machine learning to perform tasks that would otherwise require human labor. This definition is intentionally broad, encompassing not just generative AI but also other machine learning applications aimed at automating tasks. AI as an Accessibility Tool AI has potential to enhance autonomy and independence for individuals with disabilities, paralleling technological advancements seen in fields like the Paris Paralympics. However, the author is keen to explore what unique benefits AI offers and what risks might arise. Benefits Risks AI and Disability The author acknowledges that this overview touches only on some key issues related to AI and disability. It is crucial for those working in machine learning to be aware of these dynamics, striving to balance benefits with potential risks and ensuring equitable access to technological advancements. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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October 28, 2024in AI Tools, Data, Generative AI
What Should Enterprises Build with Agentic AI?
25Oct

What Should Enterprises Build with Agentic AI?

The rise of agentic AI has dominated recent discussions in enterprise technology, sparking debates over its transformative potential and practical applications. Just weeks ago, few had heard of the term. Now, every tech vendor is racing to stake their claim in this emerging space, positioning agentic AI as the successor to AI co-pilots. While co-pilots assist users with tasks, agentic AI represents the next step: delegating tasks to intelligent agents capable of independent execution, akin to assigning work to a junior colleague. But beyond the buzz, the pressing questions remain: Cutting Through the Hype Recent launches provide a snapshot of how enterprises are beginning to deploy agentic AI. Salesforce’s Agentforce, Asana’s AI Studio, and Atlassian’s Rovo AI Assistant all emphasize the ability of these agents to streamline workflows by interpreting unstructured data and automating complex tasks. These tools promise flexibility over previous rigid, rule-based systems. For example, instead of painstakingly scripting every step, users can instruct an agent to “follow documented policies, analyze data, and propose actions,” reserving human approval for final execution. However, the performance of these agents hinges on data quality and system robustness. Salesforce’s Marc Benioff, for instance, critiques Microsoft’s Copilot for lacking a robust data model, emphasizing Salesforce’s own structured approach as a competitive edge. Similarly, Asana and Atlassian highlight the structured work graphs underpinning their platforms as critical for accurate and reliable outputs. Key Challenges Despite the promise, there are significant challenges to deploying agentic AI effectively: Early Wins and Future Potential Early adopters are seeing value in high-volume, repetitive scenarios such as customer service. For example: However, these successes represent low-hanging fruit. The true promise lies in rethinking how enterprises work. As one panelist at Atlassian’s event noted: “We shouldn’t just use this AI to enhance existing processes. We should ask whether these are the processes we want for the future.” The Path Forward The transformative potential of agentic AI will depend on broader process standardization. Just as standardized shipping containers revolutionized logistics, and virtual containers transformed IT operations, similar breakthroughs in process design could unlock exponential gains for AI-driven workflows. For now, enterprises should: Conclusion Agentic AI holds immense potential, but its real power lies in enabling enterprises to question and redesign how work gets done. While it may still be in its early days, businesses that align their AI investments with strategic goals—and not just immediate fixes—will be best positioned to thrive in this new era of intelligent automation. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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October 25, 2024in Agentforce Platform, Data, Enterprise, Generative AI, Salesforce, Technology
Scope of Generative AI
24Oct

Scope of Generative AI

Generative AI has far more to offer your site than simply mimicking a conversational ChatGPT-like experience or providing features like generating cover letters on resume sites. Let’s explore how you can integrate Generative AI with your product in diverse and innovative ways! There are three key perspectives to consider when integrating Generative AI with your features: system scope, spatial relationship, and functional relationship. Each perspective offers a different lens for exploring integration pathways and can spark valuable conversations about melding AI with your product ecosystem. These categories aren’t mutually exclusive; instead, they overlap and provide flexible ways of envisioning AI’s role. 1. System Scope — The Reach of Generative AI in Your System System scope refers to the breadth of integration within your system. By viewing integration from this angle, you can assess the role AI plays in managing your platform’s overall functionality. While these categories may overlap, they are useful in facilitating strategic conversations. 2. Spatial Relationships — Where AI Interacts with Features Spatial relationships describe where AI features sit in relation to your platform’s functionality: 3. Functional Relationships — How AI Interacts with Features Functional relationships determine how AI and platform features work together. This includes how users engage with AI and how AI content updates based on feature interactions: Scope of Generative AI By considering these different perspectives—system scope, spatial, and functional—you can drive more meaningful conversations about how Generative AI can best enhance your product’s capabilities. Each approach offers unique value, and careful thought can help teams choose the integration path that aligns with their needs and goals. Scope of Generative AI conversations with Tectonic can assist in planning the best ROI approach to AI. Contact us today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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October 24, 2024in Data, Generative AI, Google
AI Prompts to Accelerate Academic Reading
19Oct

AI Prompts to Accelerate Academic Reading

10 AI Prompts to Accelerate Academic Reading with ChatGPT and Claude AI In the era of information overload, keeping pace with academic research can feel daunting. Tools like ChatGPT and Claude AI can streamline your reading and help you extract valuable insights from research papers quickly and efficiently. These AI assistants, when used ethically and responsibly, support your critical analysis by summarizing complex studies, highlighting key findings, and breaking down methodologies. While these prompts enhance efficiency, they should complement—never replace—your own critical thinking and thorough reading. AI Prompts for Academic Reading 1. Elevator Pitch Summary Prompt: “Summarize this paper in 3–5 sentences as if explaining it to a colleague during an elevator ride.”This prompt distills the essence of a paper, helping you quickly grasp the core idea and decide its relevance. 2. Key Findings Extraction Prompt: “List the top 5 key findings or conclusions from this paper, with a brief explanation of each.”Cut through jargon to access the research’s core contributions in seconds. 3. Methodology Breakdown Prompt: “Explain the study’s methodology in simple terms. What are its strengths and potential limitations?”Understand the foundation of the research and critically evaluate its validity. 4. Literature Review Assistant Prompt: “Identify the key papers cited in the literature review and summarize each in one sentence, explaining its connection to the study.”A game-changer for understanding the context and building your own literature review. 5. Jargon Buster Prompt: “List specialized terms or acronyms in this paper with definitions in plain language.”Create a personalized glossary to simplify dense academic language. 6. Visual Aid Interpreter Prompt: “Explain the key takeaways from Figure X (or Table Y) and its significance to the study.”Unlock insights from charts and tables, ensuring no critical information is missed. 7. Implications Explorer Prompt: “What are the potential real-world implications or applications of this research? Suggest 3–5 possible impacts.”Connect theory to practice by exploring broader outcomes and significance. 8. Cross-Disciplinary Connections Prompt: “How might this paper’s findings or methods apply to [insert your field]? Suggest potential connections or applications.”Encourage interdisciplinary thinking by finding links between research areas. 9. Future Research Generator Prompt: “Based on the limitations and unanswered questions, suggest 3–5 potential directions for future research.”Spark new ideas and identify gaps for exploration in your field. 10. The Devil’s Advocate Prompt: “Play devil’s advocate: What criticisms or counterarguments could be made against the paper’s main claims? How might the authors respond?”Refine your critical thinking and prepare for discussions or reviews. Additional Resources Generative AI Prompts with Retrieval Augmented GenerationAI Agents and Tabular DataAI Evolves With Agentforce and Atlas Conclusion Incorporating these prompts into your routine can help you process information faster, understand complex concepts, and uncover new insights. Remember, AI is here to assist—not replace—your research skills. Stay critical, adapt prompts to your needs, and maximize your academic productivity. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

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October 19, 2024in AI Tools, Data, Generative AI
AI All Grown Up
07Oct

AI All Grown Up

If you thought Salesforce had fully embraced AI, think again. The company has much more in store. AI All Grown Up and Salesforce is the educator! Alongside the announcement of the new Agentforce platform, Salesforce has teased plans to offer free premium instructor-led courses and AI certifications throughout 2025, reflecting a bold commitment to fostering AI skills and expertise. We’ve talked quite a bit over the last year about the need for AI education, and lo and behold here comes Salesforce to the rescue! AI All Grown Up Ah, they grow up so fast. Once just a baby cradeled in our arms with endless possibilities and potential. It was just like a year or so ago we heard of ChatGPT. Prior to that most people’s main exposure to artificial intelligence was their smart phones, which today we realize weren’t reall that smart. Generative, predictive and agentic AI have barreled down the pipeline increasing our vocabulary, and understanding, of what artificial intelligence can do. From generative content to sounds and images, AI continued to amaze us. Then predictive AI did our calculations faster than we could have imagined. Then agentic AI did nearly everything imaginable. AI All Grown Up. Like a very proud mentor of the process, I want to talk about Salesforce’s major contribution. Addressing the AI Skills Gap: Salesforce’s $50 Million Investment As the veritable plethora of AI tools rapidly expands, Salesforce is taking proactive steps to address the growing AI skills gap by investing $50 million into workforce upskilling initiatives. The company aims to ensure that businesses and individuals are prepared to utilize their new wave of AI tools effectively. While the full details have yet to be released, Salesforce has revealed that its premium AI courses and certifications will be made available for free via Trailhead by the end of 2025. This could mean certifications such as AI Associate and AI Specialist, which currently cost $75 and $200 respectively, may soon be offered at no cost. Gratis. Free, Salesforce has also mentioned “premium instructor-led training,” sparking speculation that AI-focused, instructor-led Trailhead Academy courses could become accessible to everyone in the Salesforce ecosystem. Expanding AI Education with Global AI Centers Salesforce’s AI upskilling push is part of a broader initiative to establish “AI Centers” across the globe. Following the opening of its first center in London in June, Salesforce is planning to launch additional AI hubs in cities like Chicago, Tokyo, Sydney, and even a pop-up center in San Francisco. These centers will host in-person premium courses and serve as gathering spaces for industry experts, partners, and customers. This initiative benefits not only the Salesforce ecosystem by increasing AI knowledge where expertise is scarce, but also aligns with Salesforce’s strategy of bringing AI-driven solutions to market through new products like Copilot Studio, Data Cloud, and the newly launched Agentforce platform. Agentforce: Salesforce’s Third Wave of AI On August 28, 2024, Salesforce introduced Agentforce, a suite of autonomous AI agents that marks a significant leap in how businesses engage with customers. Described as the “Third Wave of AI,” Agentforce goes beyond traditional chatbots, providing intelligent agents capable of driving customer success with minimal human intervention. What is Agentforce? Agentforce is a comprehensive platform designed for organizations to build, customize, and deploy autonomous AI agents across various business functions, such as customer service, sales, marketing, and commerce. These agents operate independently, accessing data, crafting action plans, and executing tasks without needing constant human oversight. It is like Artificial Intelligence just graduated highschool and is off to a world of new adventures and growth opportunities at college or university! Key Features of Agentforce: The Technology Behind Agentforce At the core of Agentforce is the Atlas Reasoning Engine, a system designed to mimic human reasoning. Here’s how it works: Customization Tools: Agent Builder Agentforce provides tools like Agent Builder, a low-code platform for customizing out-of-the-box agents or creating new ones for specific business needs. With this tool, users can: The Agentforce Partner Network Salesforce’s partner ecosystem plays a key role in Agentforce’s versatility, with contributions from companies like AWS, Google, IBM, and Workday. Together, they’ve developed over 20 agent actions available through the Salesforce AppExchange. As proud parents we watch our Artificial Intelligence child venture into the world making friends along the way. Learning social skills. Benefits and Impact of Agentforce Early Adopters and Success Stories Several companies are already benefiting from Agentforce: Availability and Pricing of Salesforce’s AI All Grown Up Agentforce for Service and Sales will be generally available on October 25, 2024, with some components of the Atlas Reasoning Engine launching in February 2025. Pricing starts at $2 per conversation, with volume discounts available. The Future of AI and Work Salesforce’s ambitious vision is to empower one billion AI agents with Agentforce by the end of 2025. This reflects their belief that the future of work will involve a hybrid workforce, where humans and AI agents collaborate to drive customer success. AI All Grown Up and We Couldn’t Be Prouder Our amazing AI child has graduated college and ventured out into the workforce. Agentforce vs. Einstein Bots: What’s the Difference? Conclusion Agentforce represents a major leap forward in AI-powered customer engagement. By providing autonomous, intelligent agents capable of managing complex tasks, Salesforce is positioning itself at the forefront of AI innovation. As businesses continue to explore ways to improve efficiency and customer satisfaction, Agentforce could redefine how organizations interact with customers and streamline their operations. If this is the Third Wave of AI, what will the fourth wave bring? Written by Tectonic’s Solutions Architect, Shannan Hearne Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business

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October 7, 2024in Agentforce Platform, AI Tools, Data, Google, Mulesoft, Salesforce, Technology

Challenges of EHR Implementation in Healthcare

Challenges of EHR Implementation and How to Overcome Them Implementing an electronic health record (EHR) system is a monumental task, with complexities that require careful planning and execution. Common challenges—such as resistance to change, data migration hurdles, cost overruns, cybersecurity risks, and patient engagement issues—can impede progress. However, understanding these obstacles and applying targeted strategies can pave the way for a smooth transition. 1. Resistance to Change The adoption of a new EHR system affects nearly every workflow in a healthcare organization, often sparking resistance among staff. Fear of change and attachment to familiar processes can hinder implementation. Solution: 2. Data Migration Issues Accurate migration of patient health records is critical, yet transitioning data between systems often presents technical and logistical challenges. Solution: 3. Cost Overruns EHR implementation costs can quickly escalate, extending beyond software and hardware expenses to include consulting fees, training, and operational adjustments. Solution: 4. Heightened Cybersecurity Risks Transitioning sensitive patient data between EHR systems increases vulnerability to breaches, ransomware, and other cybersecurity threats. Solution: 5. Patient Engagement Challenges Patients are often overlooked during EHR transitions, leading to confusion about changes in medication requests, appointment scheduling, and other interactions. Solution: Conclusion EHR implementation is undoubtedly challenging, but with proactive strategies, healthcare organizations can navigate these complexities effectively. By addressing resistance to change, ensuring seamless data migration, managing costs, bolstering cybersecurity, and engaging patients, organizations can achieve a successful EHR transition that enhances workflows, safeguards data, and improves patient outcomes. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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October 3, 2024in Data, Life Sciences Cloud, Salesforce, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud
Strawberry AI Models
28Sep

Strawberry AI Models

Since OpenAI introduced its “Strawberry” AI models, something intriguing has unfolded. The o1-preview and o1-mini models have quickly gained attention for their superior step-by-step reasoning, offering a structured glimpse into problem-solving. However, behind this polished façade, a hidden layer of the AI’s mind remains off-limits—an area OpenAI is determined to keep out of reach. Unlike previous models, the o1 series conceals its raw thought processes. Users only see the refined, final answer, generated by a secondary AI, while the deeper, unfiltered reasoning is locked away. Naturally, this secrecy has only fueled curiosity. Hackers, researchers, and enthusiasts are already working to break through this barrier. Using jailbreak techniques and clever prompt manipulations, they are seeking to uncover the AI’s raw chain of thought, hoping to reveal what OpenAI has concealed. Rumors of partial breakthroughs have circulated, though nothing definitive has emerged. Meanwhile, OpenAI closely monitors these efforts, issuing warnings and threatening account bans to those who dig too deep. On platforms like X, users have reported receiving warnings merely for mentioning terms like “reasoning trace” in their interactions with the o1 models. Even casual inquiries into the AI’s thinking process seem to trigger OpenAI’s defenses. The company’s warnings are explicit: any attempt to expose the hidden reasoning violates their policies and could result in revoked access to the AI. Marco Figueroa, leader of Mozilla’s GenAI bug bounty program, publicly shared his experience after attempting to probe the model’s thought process through jailbreaks—he quickly found himself flagged by OpenAI. Now I’m on their ban list,” Figueroa revealed. So, why all the secrecy? OpenAI explained in a blog post titled Learning to Reason with LLMs that concealing the raw thought process allows for better monitoring of the AI’s decision-making without interfering with its cognitive flow. Revealing this raw data, they argue, could lead to unintended consequences, such as the model being misused to manipulate users or its internal workings being copied by competitors. OpenAI acknowledges that the raw reasoning process is valuable, and exposing it could give rivals an edge in training their own models. However, critics, such as independent AI researcher Simon Willison, have condemned this decision. Willison argues that concealing the model’s thought process is a blow to transparency. “As someone working with AI systems, I need to understand how my prompts are being processed,” he wrote. “Hiding this feels like a step backward.” Ultimately, OpenAI’s decision to keep the AI’s raw thought process hidden is about more than just user safety—it’s about control. By retaining access to these concealed layers, OpenAI maintains its lead in the competitive AI race. Yet, in doing so, they’ve sparked a hunt. Researchers, hackers, and enthusiasts continue to search for what remains hidden. And until that veil is lifted, the pursuit won’t stop. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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September 28, 2024in Data
OpenAI’s o1 model
26Sep

OpenAI’s o1 model

The release of OpenAI’s o1 model has sparked some confusion. Unlike previous models that focused on increasing parameters and capabilities, this one takes a different approach. Let’s explore the technical distinctions first, share a real-world experience, and wrap up with some recommendations on when to use each model. Technical Differences The core difference is that o1 serves as an “agentic wrapper” around GPT-4 (or a similar model). This means it incorporates a layer of metacognition, or “thinking about thinking,” before addressing a query. Instead of immediately answering the question, o1 first evaluates the best strategy for tackling it by breaking it down into subtasks. Once this analysis is complete, o1 begins executing each subtask. Depending on the answers it receives, it may adjust its approach. This method resembles the “tree of thought” strategy, allowing users to see real-time explanations of the subtasks being addressed. For a deeper dive into agentic approaches, I highly recommend Andrew Ng’s insightful letters on the topic. However, this method comes with a cost—it’s about six times more expensive and approximately six times slower than traditional approaches. While this metacognitive process can enhance understanding, it doesn’t guarantee improved answers for straightforward factual queries or tasks like generating trivia questions, where simplicity may yield better results. Real-World Example To illustrate the practical implications, Tectonic began to deepen the understanding of variational autoencoders—a trend in multimodal LLMs. While we had a basic grasp of the concept, we had specific questions about their advantages over traditional autoencoders and the nuances of training them. This information isn’t easily accessible through a simple search; it’s more akin to seeking insight from a domain expert. To enhance our comprehension, we engaged with both GPT-4 and o1. We quickly noticed that o1’s responses were more thoughtful and facilitated a meaningful dialogue. In contrast, GPT-4 tended to recycle the same information, offering limited depth—much like how some people might respond in conversation. A particularly striking example occurred when we attempted to clarify our understanding. The difference was notable. o1 responded like a thoughtful colleague, addressing our specific points, while GPT-4 felt more like a know-it-all friend who rambled on, requiring me to sift through the information for valuable insights. Summary and Recommendations In essence, if we were to personify these models, GPT-4 would be the overzealous friend who dives into a stream of consciousness, while o1 would be the more attentive listener who takes a moment to reflect before delivering precise and relevant insights. Here are some scenarios where o1 may outperform GPT-4, justifying its higher cost: By leveraging these insights, you can better navigate the strengths of each model in your tasks and inquiries. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 26, 2024in Salesforce
Microsoft Copilot
21Sep

Microsoft Copilot

The fundamental capabilities of collaboration platforms have remained largely unchanged since the pandemic began. These platforms typically offer video conferencing, desktop sharing, and text chat, creating a virtual approximation of in-person meetings. While this setup effectively allows teams to collaborate across distances, it raises the question: Is this all there is to the collaboration experience? Enter Copilot. Microsoft is pioneering a new era of collaboration, where AI assistants help users prioritize meetings, manage follow-ups on action items, and integrate meeting outputs into future tasks. This evolution is particularly promising for knowledge workers who are overwhelmed by constant meetings. Copilot aims to redefine the collaboration experience, promising increased productivity and a more strategic approach to meetings. However, OpenAI, Microsoft’s prominent AI partner, is making moves to disrupt the enterprise space as well. OpenAI recently launched ChatGPT Enterprise, which now boasts 600,000 users, including clients from 93% of the Fortune 500. This week, OpenAI also acquired the videoconferencing startup Multi, sparking speculation that the company may integrate collaboration features directly into ChatGPT. Multi’s unique approach to videoconferencing—described as “multiplayer” and drawing parallels to gaming rather than traditional meetings—hints at a potential shift in how meetings are experienced. The Multi tool, set to be discontinued in July following the acquisition, was tailored for software developers, focusing on screen sharing and leveraging Zoom’s video capabilities. Yet, the concept of enhanced document collaboration extends beyond software developers. Integrating document collaboration with AI-driven features like summarization, and linking this to advanced language models, could revolutionize the collaboration experience. This approach promises to streamline the collaborative process, focusing on the work at hand with new functionalities. That said, not all meetings revolve around documents. Many are simply conversations—often the ones people prefer to avoid. Therefore, refining how meetings are managed and integrating them into users’ work lives will remain crucial, even as new technologies enhance screen sharing and video capabilities. So, where does this leave traditional video services? The quest for meeting equity and AI-enhanced directors will likely continue to refine the experience, striving for the “next best thing to being there.” As the collaboration platform evolves, any outdated elements will become more apparent. Ultimately, collaboration is a multifaceted experience, and technology will play a key role in its continued advancement. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 21, 2024in Enterprise, Salesforce, Technology
AI Senate Bill 1047
06Sep

AI Senate Bill 1047

California’s new AI bill has sparked intense debate, with proponents viewing it as necessary regulation and critics warning it could stifle innovation, particularly for small businesses. Senate Bill 1047, known as the Safe and Secure Innovation for Frontier Artificial Intelligence Models Act, mandates that developers of advanced AI systems costing at least $100 million to train must test their models for potential harm and put safeguards in place. It also offers whistleblower protections for employees at large AI firms and establishes CalCompute, a public cloud computing resource aimed at startups and researchers. The bill is awaiting Governor Gavin Newsom’s signature by Sept. 30 to become law. Prominent AI experts, including Geoffrey Hinton and Yoshua Bengio, support the bill. However, it has met resistance from various quarters, including Rep. Nancy Pelosi and OpenAI, who argue it could hinder innovation and the startup ecosystem. Pelosi and others have expressed concerns that the bill’s requirements might burden smaller businesses and harm California’s leadership in tech innovation. Gartner analyst Avivah Litan acknowledged the dilemma, stating that while regulation is critical for AI, the bill’s requirements might negatively impact small businesses. “Some regulation is better than none,” she said, but added that thresholds could be challenging for smaller firms. Steve Carlin, CEO of AiFi, criticized the bill for its vague language and complex demands on AI developers, including unclear guidance on enforcing the rules. He suggested that instead of focusing on AI models, legislation should address the risks and applications of AI, as seen with the EU AI Act. Despite concerns, some experts like Forrester Research’s Alla Valente support the bill’s safety testing and whistleblower protections. Valente argued that safeguarding AI models is essential across industries, though she acknowledged that the costs of compliance could be higher for small businesses. Still, she emphasized that the long-term costs of not implementing safeguards could be greater, with risks including customer lawsuits and regulatory penalties. California’s approach to AI regulation adds to the growing patchwork of state-level AI laws in the U.S. Colorado and Connecticut have also introduced AI legislation, and cities like New York have tackled issues like algorithmic bias. Carlin warned that a fragmented state-by-state regulatory framework could create a costly and complex environment for developers, calling for a unified federal standard instead. While federal legislation has been proposed, none has passed, and Valente pointed out that relying on Congress for action is a slow process. In the meantime, states like California are pushing ahead with their own AI regulations, creating both opportunities and challenges for the AI industry. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 6, 2024in Salesforce
Data Integration with AWS Glue
04Sep

Data Integration with AWS Glue

The rapid rise of Software as a Service (SaaS) solutions has led to data silos across different platforms, making it challenging to consolidate insights. Effective data analytics depends on the ability to seamlessly integrate data from various systems by identifying, gathering, cleansing, and combining it into a unified format. AWS Glue, a serverless data integration service, simplifies this process with scalable, efficient, and cost-effective solutions for unifying data from multiple sources. By using AWS Glue, organizations can streamline data integration, minimize silos, and enhance agility in managing data pipelines, unlocking the full potential of their data for analytics, decision-making, and innovation. This insight explores the new Salesforce connector for AWS Glue and demonstrates how to build a modern Extract, Transform, and Load (ETL) pipeline using AWS Glue ETL scripts. Introducing the Salesforce Connector for AWS Glue To meet diverse data integration needs, AWS Glue now supports SaaS connectivity for Salesforce. This enables users to quickly preview, transfer, and query customer relationship management (CRM) data, while dynamically fetching the schema. With the Salesforce connector, users can ingest and transform CRM data and load it into any AWS Glue-supported destination, such as Amazon S3, in preferred formats like Apache Iceberg, Apache Hudi, and Delta Lake. It also supports reverse ETL use cases, enabling data to be written back to Salesforce. Key Benefits: Solution Overview For this use case, we retrieve the full load of a Salesforce account object into a data lake on Amazon S3 and capture incremental changes. The solution also enables updates to certain fields in the data lake and synchronizes them back to Salesforce. The process involves creating two ETL jobs using AWS Glue with the Salesforce connector. The first job ingests the Salesforce account object into an Apache Iceberg-format data lake on Amazon S3. The second job captures updates and pushes them back to Salesforce. Prerequisites: Creating the ETL Pipeline Step 1: Ingest Salesforce Account Object Using the AWS Glue console, create a new job to transfer the Salesforce account object into an Apache Iceberg-format transactional data lake in Amazon S3. The script checks if the account table exists, performs an upsert if it does, or creates a new table if not. Step 2: Push Changes Back to Salesforce Create a second ETL job to update Salesforce with changes made in the data lake. This job writes the updated account records from Amazon S3 back to Salesforce. Example Query sqlCopy codeSELECT id, name, type, active__c, upsellopportunity__c, lastmodifieddate FROM “glue_etl_salesforce_db”.”account”; Additional Considerations You can schedule the ETL jobs using AWS Glue job triggers or integrate them with other AWS services like AWS Lambda and Amazon EventBridge for advanced workflows. Additionally, AWS Glue supports importing deleted Salesforce records by configuring the IMPORT_DELETED_RECORDS option. Clean Up After completing the process, clean up the resources used in AWS Glue, including jobs, connections, Secrets Manager secrets, IAM roles, and the S3 bucket to avoid incurring unnecessary charges. Conclusion The AWS Glue connector for Salesforce simplifies the analytics pipeline, accelerates insights, and supports data-driven decision-making. Its serverless architecture eliminates the need for infrastructure management, offering a cost-effective and agile approach to data integration, empowering organizations to efficiently meet their analytics needs. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 4, 2024in Salesforce
Generative AI Overview
19Aug

Generative AI Overview

Editor’s Note: AI Cloud, Einstein GPT, and other cloud GPT products are now Einstein. For the latest on Salesforce Einstein The Rise of Generative AI: What It Means for Business and CRM Generative artificial intelligence (AI) made headlines in late 2022, sparking widespread curiosity and questions about its potential impact on various industries. What is Generative AI? Generative AI is a technology that creates new content—such as poetry, emails, images, or music—based on a set of input data. Unlike traditional AI, which focuses on classifying or predicting, generative AI can produce novel content with a human-like understanding of language, as noted by Salesforce Chief Scientist Silvio Savarese. However, successful generative AI depends on the quality of the input data. “AI is only as good as the data you give it, and you must ensure that datasets are representative,” emphasizes Paula Goldman, Salesforce’s Chief Ethical and Humane Use Officer. How Does Generative AI Work? Generative AI can be developed using several deep learning approaches, including: Other methods include Variational Autoencoders (VAEs) and Neural Radiance Fields (NeRFs), which generate new data or create 2D and 3D images based on sample data. Generative AI and Business Generative AI has captured the attention of global business leaders. A recent Salesforce survey found that 67% of IT leaders are focusing on generative AI in the next 18 months, with 33% considering it a top priority. Salesforce has long been exploring generative AI applications. For instance, CodeGen helps transform simple English prompts into executable code, and LAVIS makes language-vision AI accessible to researchers. More recently, Salesforce’s ProGen project demonstrated the creation of novel proteins using AI, potentially advancing medicine and treatment development. Ketan Karkhanis, Salesforce’s Executive VP and GM of Sales Cloud, highlights that generative AI benefits not just large enterprises but also small and medium-sized businesses (SMBs) by automating proposals, customer communications, and predictive sales modeling. Challenges and Ethical Considerations Despite its potential, generative AI poses risks, as noted by Paula Goldman and Kathy Baxter of Salesforce’s Ethical AI practice. They stress the importance of responsible innovation to ensure that generative AI is used safely and ethically. Accuracy in AI recommendations is crucial, and the authoritative tone of models like ChatGPT can sometimes lead to misleading results. Salesforce is committed to building trusted AI with embedded guardrails to prevent misuse. As generative AI evolves, it’s vital to balance its capabilities with ethical considerations, including its environmental impact. Generative AI can increase IT energy use, which 71% of IT leaders acknowledge. Generative AI at Salesforce Salesforce has integrated AI into its platform for years, with Einstein AI providing billions of daily predictions to enhance sales, service, and customer understanding. The recent launch of Einstein GPT, the world’s first generative AI for CRM, aims to transform how businesses interact with customers by automating content creation across various functions. Salesforce Ventures is also expanding its Generative AI Fund to $500 million, supporting AI startups and fostering responsible AI development. This expansion includes investments in companies like Anthropic and Cohere. As Salesforce continues to lead in AI innovation, the focus remains on creating technology that is inclusive, responsible, and sustainable, paving the way for the future of CRM and business. The Future of Business: AI-Powered Leadership and Decision-Making Tomorrow’s business landscape will be transformed by specialized, autonomous AI agents that will significantly change how companies are run. Future leaders will depend on these AI agents to support and enhance their teams, with AI chiefs of staff overseeing these agents and harnessing their capabilities. New AI-powered tools will bring businesses closer to their customers and enable faster, more informed decision-making. This shift is not just a trend—it’s backed by significant evidence. The Slack Workforce Index reveals a sevenfold increase in leaders seeking to integrate AI tools since September 2023. Additionally, Salesforce research shows that nearly 80% of global workers are open to an AI-driven future. While the pace of these changes may vary, it is clear that the future of work will look vastly different from today. According to the Slack Workforce Index, the number of leaders looking to integrate AI tools into their business has skyrocketed 7x since September 2023. Mick Costigan, VP, Salesforce Futures In the [still] early phases of a major technology shift, we tend to over-focus on the application of technology innovations to existing workflows. Such advances are important, but closing the imagination gap about the possible new shapes of work requires us to consider more than just technology. It requires us to think about people, both as the customers who react to new offerings and as the employees who are responsible for delivering them. Some will eagerly adopt new technology. Others will resist and drag their feet. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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August 19, 2024in AI Tools, Artificial Intelligence, Data, Enterprise, Generative AI, Salesforce, Salesforce Einstein, Technology
Salesforce Data Cloud Pioneer
25Jul

Salesforce Data Cloud Pioneer

While many organizations are still building their data platforms, Salesforce Data Cloud Pioneer has made a significant leap forward. By seamlessly incorporating metadata integration, Salesforce has transformed the modern data stack into a comprehensive application platform known as the Einstein 1 Platform. Led by Muralidhar Krishnaprasad, executive vice president of engineering at Salesforce, the Einstein 1 Platform is built on the company’s metadata framework. This platform harmonizes metadata and integrates it with AI and automation, marking a new era of data utilization. The Einstein 1 Platform: Innovations and Capabilities Salesforce’s goal with the Einstein 1 Platform is to empower all business users—salespeople, service engineers, marketers, and analysts—to access, use, and act on all their data, regardless of its location, according to Krishnaprasad. The open, extensible platform not only unlocks trapped data but also equips organizations with generative AI functionality, enabling personalized experiences for employees and customers. “Analytics is very important to know how your business is doing, but you also want to make sure all that data and insights are actionable,” Krishnaprasad said. “Our goal is to blend AI, automation, and analytics together, with the metadata layer being the secret sauce.” Salesforce Data Cloud Pioneer In a conversation with George Gilbert, senior analyst at theCUBE Research, Krishnaprasad discussed the platform’s metadata integration, open-API technology, and key features. They explored how its extensibility and interoperability enhance usability across various data formats and sources. Metadata Integration: Accommodating Any IT Environment The Einstein 1 Platform is built on Trino, the federated open-source query engine, and Spark for data processing. It offers a rich set of connectors and an open, extensible environment, enabling organizations to share data between warehouses, lake houses, and other systems. “We use a hyper-engine for sub-second response times in Tableau and other data explorations,” Krishnaprasad explained. “This in-memory overlap engine ensures efficient data processing.” The platform supports various machine learning options and allows users to integrate their own large language models. Whether using Salesforce Einstein, Databricks, Vertex, SageMaker, or other solutions, users can operate without copying data. The platform includes three levels of extensibility, enabling organizations to standardize and extend their customer journey models. Users can start with basic reference models, customize them, and then generate insights, including AI-driven insights. Finally, they can introduce their own functions or triggers to act on these insights. The platform continuously performs unification, allowing users to create different unified graphs based on their needs. “We’re a multimodal system, considering your entire customer journey,” Krishnaprasad said. “We provide flexibility at all levels of the stack to create the right experience for your business.” The Triad of AI, Automation, and Analytics The platform’s foundation ingests, harmonizes, and unifies data, resulting in a standardized metadata model that offers a 360-degree view of customer interactions. This approach unlocks siloed data, much of which is in unstructured forms like conversations, documents, emails, audio, and video. “What we’ve done with this customer 360-degree model is to use unified data to generate insights and make these accessible across application surfaces, enabling reactions to these insights,” Krishnaprasad said. “This unlocks a comprehensive customer journey.” For instance, when a customer views an ad and visits the website, salespeople know what they’re interested in, service personnel understand their concerns, and analysts have the information needed for business insights. These capabilities enhance customer engagement. “Couple this with generative AI, and we enable a lot of self-service,” Krishnaprasad added. “We aim to provide accurate answers, elevating data to create a unified model and powering a unified experience across the entire customer journey.” Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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July 25, 2024in Einstein 1 Platform, Generative AI, Salesforce Data Cloud, Salesforce Einstein
Otter AI S-Docs and Salesforce
17May

Otter AI S-Docs and Salesforce

Numerous vendors in the enterprise software market are currently emphasizing their AI capabilities, envisioning a future where AI can address a wide array of global challenges, from healthcare to climate change. While the realization of these claims remains uncertain, the practical and impactful applications of AI in everyday scenarios often go unnoticed. There exists ample opportunity for leveraging AI tools that are readily available and require minimal setup to enhance efficiency. Otter AI S-Docs and Salesforce. One such example is S-Docs, a document automation vendor integrated natively on the Salesforce platform, which is harnessing Otter.ai, an AI transcription service, to revolutionize its sales process and product development. S-Docs is seamlessly integrating Otter.ai into its digital collaboration tools, enabling automatic transcription during sales calls. This not only aids sales representatives in navigating diverse dialects but also streamlines post-call administrative tasks, prompting quicker action. Moreover, the product development team at S-Docs is leveraging Otter.ai to analyze the transcribed content from sales calls and incorporate insights into its product feedback loop. This integration was sparked by S-Docs’ CTO, Anand Narasimhan, who discovered Otter.ai through a LinkedIn connection and recognized its potential value for the business. Initially used during team calls and sprint reviews, Otter.ai’s high transcription accuracy and insightful summaries impressed Narasimhan and his colleague, Keith Bossier, VP of Sales at S-Docs. Subsequently, Otter.ai was adopted by the sales and customer success teams, offering benefits that surpassed those of their previous provider, Gong. For the sales team, Otter.ai significantly reduces the administrative burden by providing real-time transcriptions, catch-all summaries, and key takeaways from meetings. This facilitates quicker follow-ups and enhances the overall customer experience. Buoyed by the success in sales, S-Docs is exploring avenues to expand the use of Otter.ai across its business. Bossier envisions leveraging transcripts from sales calls for onboarding new representatives, while Narasimhan explores integrating the captured content into the product development cycle. Additionally, they are collaborating with Otter.ai to introduce automated action items directly into the S-Docs platform, further streamlining operations and enhancing efficiency. As S-Docs continues to innovate and optimize its processes with Otter.ai, it exemplifies the tangible benefits of leveraging AI in practical business scenarios. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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May 17, 2024in Salesforce, Salesforce Einstein, Salesforce Optimization, Salesforce Sales Cloud

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