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Generative AI Trends for 2024
31Dec

Generative AI Trends for 2024

It’s hard to believe that ChatGPT is only a year old. The number of exciting new product launches over the past 12 months has been astonishing — and there’s no sign of slowing down. In fact, quite the opposite. Earlier in November, OpenAI hosted DevDay, where the company announced extensive offerings across B2C and B2B markets. Cohere has doubled down on its knowledge search capabilities and private deployments. Amazon Web Services launched PartyRock, its no-code gen AI app-building playground. Generative AI Trends for 2024 you can expect to see. We believe that last month’s activity sets the stage for 2024 in the gen AI space. Here are six major trends happening across the space: While the technology’s possibilities continue to grow, we believe there are four principles for CEOs to consider as they drive their gen AI agendas. These principles draw from our experiences building gen AI applications with our clients throughout the year, as well as decades of delivering digital and analytics transformations. Be Intentional: Set Gen AI Strategy Top-Down Gen AI is a gold rush. Everyone from shareholders to employees to boards is scrambling to deploy the latest and most powerful gen AI tools, and many large organizations have over 150 gen AI use cases on backlog. While we share their excitement and admire their ambition, allowing dozens of gen AI projects to spawn across an organization puts at-scale value creation at risk. Generative AI Trends for 2024 With recent developments in the gen AI space, the proliferation of use cases and opportunities will continue to split the already divided attention of leadership teams. C-suites must bring focus with a top-down gen AI strategy, constantly asking how the technology can create enduring strategic distance between the organization and its competitors. Here are some examples from first movers: Smart organizations are taking a 2×2 approach: identifying two fast use cases to register quick wins and excite the organization while working on two slower, more transformational use cases that will change day-to-day business operations. Reimagine Entire Domains Rather Than Isolated Use Cases During 2023, most organizations began experimenting with gen AI, building one-off prototypes and buying off-the-shelf solutions. Yet, as these solutions are rolled out to end users, organizations are struggling to capture value. For example, some organizations that invested in GitHub Copilot have yet to figure out how the value capture is passed back to the business. Organizations need to reframe from isolated use cases to the full software delivery lifecycle. Scrum teams need to commit to shipping more product features, or sales need to offer more competitive pricing to win more business. Stopping at just buying a new shiny tool means the productivity gains will not translate to bottom-line gains. This often means reimagining entire workflows and domains. This serves two purposes: 1) it creates a more seamless end-user experience by avoiding point solutions; and 2) organizations can more easily track value against clear business outcomes. For example, an insurer we worked with is reimagining its end-to-end claims process — from first notice of loss to payment. For each step along the way, the insurer has identified gen AI, digital, and analytics opportunities, while never losing sight of the claims adjuster’s experience. Ultimately, this comprehensive approach made a step-change impact on end-to-end handling time. Buy Selectively, Build Strategically Matching the pace of innovation, many new startups and software offerings are entering the market, leaving enterprises with a familiar question: “Buy or build?” On the “buy” side, organizations are wary about investing in capabilities that will eventually be available for a fraction of the cost. These organizations are also skeptical of off-the-shelf solutions, unsure if the software will perform at scale without significant customization. As these solutions mature and prove their value, “buy” strategies will continue to play a central role in any gen AI strategy. Meanwhile, some organizations find compelling business cases to “build.” These players start by identifying use cases that create strategic competitive advantages against their peers by compounding existing strengths in their domain expertise, workflow integration, or regulatory know-how. For example, deploying gen AI to accelerate drug discovery has become standard in the pharmaceutical industry. Additionally, organizations are investing in data and IT infrastructure to enable their portfolio of gen AI use cases. For many organizations, there has been little to no investment in unstructured data governance. Now is the time. Build Products, Not Proofs of Concept (POCs) With the new tooling available, a talented engineer can build a proof-of-concept over a weekend. In some cases, this might be sufficient to serve an enterprise need (e.g., a summarization chatbot). However, for most use cases in a large enterprise context, proofs-of-concept are not sufficient. They do not scale well into production and their performance degrades without the appropriate engineering and experimentation. At OpenAI’s Dev Day, engineers demonstrated how hard it is to turn a POC into a production-grade product. Initially, a demo POC only achieved 45% accuracy for a retrieval task. After a few months and numerous experiments (e.g., fine-tuning, re-ranking, metadata tagging, data labeling, model self-assessment, risk guardrails), the engineers achieved 98% accuracy. Implications of Generative AI Trends for 2024 This has two implications. First, organizations cannot seek near-perfection on every use case. They need to be selective about when it is worthwhile to invest scarce engineering talent to develop high-performance gen AI applications. For some situations, 45% accuracy may be sufficient to deliver business benefits. Second, organizations need to scale their gen AI capabilities to meet their ambitions. Most organizations have identified hundreds of gen AI use cases. Therefore, organizations are turning to reusable code components to accelerate development. Dedicated engineers, often in a Center of Excellence (COE), codify best practices into these code components, allowing subsequent gen AI efforts to build off the lessons learned from pioneering projects. We have seen these components accelerate delivery by 25% to 50%. Throughout the past year, there has been an endless stream of gen AI news and hype. The coming year will likely be similar

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December 31, 2023in Generative AI
2024 AI and Machine Learning Trends
23Dec

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 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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December 23, 2023in Generative AI
Fine Tune Your Large Language Model
04Nov

Fine Tune Your Large Language Model

Revamping Your LLM? There’s a Superior Approach to Fine Tune Your Large Language Model. The next evolution in AI fine-tuning might transcend fine-tuning altogether. Vector databases present an efficient means to access and analyze all your business data. Have you ever received redundant emails promoting a product you’ve already purchased or encountered repeated questions in different service interactions? Large language models (LLMs), like OpenAI’s ChatGPT and Google’s Bard, aim to alleviate such issues by enhancing information-sharing and personalization within your company’s operations. However, off-the-shelf LLMs, built on generic internet data, lack access to your proprietary data, limiting the nuanced customer experience. Additionally, these models might not incorporate the latest information—ChatGPT, for instance, only extends up to January 2022. To customize off-the-shelf LLMs for your company, fine-tuning requires integrating your proprietary data, but this process is costly, time-consuming, and may raise trust concerns. A superior alternative is a vector database, described as “a new kind of database for the AI era.” This database offers the benefits of fine-tuning while addressing privacy concerns, promoting data unification, and saving time and resources. Fine-tuning involves training an LLM for specific tasks, such as analyzing customer sentiment or summarizing a patient’s health history. However, it is resource-intensive and fails to resolve the fundamental issue of fragmented data across your organization. A vector database, organized around vectors that describe different types of data, can seamlessly integrate with an LLM or the prompt. By storing and organizing data with an emphasis on vectors, this database streamlines access to relevant information, eliminating the need for fine-tuning and unifying enterprise data with your CRM. This is pivotal for the accuracy, completeness, and efficiency of AI outputs. Unstructured data, comprising 90% of corporate data, poses a challenge for LLMs due to its varied formats. A vector database resolves this by allowing AI to process unstructured and structured data, delivering enhanced business value and ROI. Ultimately, a company’s proprietary data serves as the cornerstone for constructing an enterprise LLM. A vector database ensures seamless storage and processing of this data, facilitating better decision-making across all business applications. 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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November 4, 2023in Salesforce Einstein
SaaS Data Protection from Own
07Sep

Reporting With Own

In any Salesforce organization, vast amounts of data are generated constantly from sales activities, customer interactions, marketing campaigns, and more. Summarizing and digesting this information quickly is crucial, especially when presenting the big picture to leadership. This is where Salesforce reports come into play. The Salesforce Reports feature enables organizations to analyze, visualize, and summarize data in real time. By pulling data from across your Salesforce environment, reports help consolidate information into easily digestible formats, such as charts, tables, and graphs. Salesforce reports are essential for: How Historical Data Can Improve Reporting in Salesforce While real-time reports are valuable, incorporating historical data can significantly enhance reporting by offering deeper insights into your organization’s long-term performance. Here’s how: Challenges of Reporting with Historical Data in Salesforce While incorporating historical data is smart, Salesforce’s native reporting capabilities impose certain limitations: Don’t Let Salesforce Reporting Limitations Hold You Back With Own Discover, customers can effortlessly generate time-series datasets from any objects and fields over any time period in just a few clicks. These datasets can be accessed using standard query and reporting tools without requiring a data warehouse or the need to enrich existing data warehouses, overcoming Salesforce’s native limitations. 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 7, 2023in Enterprise
Swapping Human Labor for AI
27Aug

Swapping Human Labor for AI

Key Considerations for Using Generative AI Generative artificial intelligence offers a wide array of capabilities: compiling meeting agendas, drafting emails, transcribing notes, and even generating code. However, a crucial question often arises: should these tasks be performed by AI? Before Swapping Human Labor for AI, read on. John Horton, an MIT Sloan associate professor and leader of the IDE research group, who specializes in AI labor and online marketplaces, notes that effectively working with AI requires more than just knowing its functions. “It’s not a trivial task, learning how to work well with a machine,” Horton said. “There’s still the challenge of figuring out how to ask good questions or make effective requests.” Since the release of ChatGPT last fall, a powerful AI tool for answering questions, engaging in conversation, and generating text, both businesses and consumers have been exploring its potential. The critical question for employers considering replacing human labor with AI is not whether AI can perform a task but whether integrating AI with human capabilities is worthwhile. For a human-AI interaction to be effective, several factors need to align. Humans must pose the right questions and evaluate the AI’s responses promptly. Horton emphasizes, “Is that going to be more efficient than just having the person do the task directly?” During the 2023 IDE Annual Conference, Horton suggested four key questions to consider when determining the suitability of AI for various tasks: Looking ahead, Horton envisions improvements in AI technology, not only in the models themselves but also in user interfaces that simplify prompting and result evaluation. “It’s easy to imagine these tasks becoming more integrated, making the evaluation process simpler and more automated,” Horton said. In the future, AI could potentially enhance both prompting and evaluation tasks. For further insights, the original article was published on MIT Sloan on August 28, 2023. 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 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

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August 27, 2023in Generative AI
Useful ChatGPT Techniques
09Aug

Useful ChatGPT Techniques

Let’s embark on a journey through the intricate world of prompt engineering, guided by the tales of seasoned explorers who have braved the highs and lows of AI interactions. Picture these individuals as a daring voyager, charting unexplored territories to uncover the secrets of prompt mastery, all so that others may navigate these waters with ease. Useful ChatGPT Techniques. In this epic insight, our intrepid explorers shars a treasure trove of insights gleaned from their odyssey—a veritable “best of” plethora chronicling their conquests and not-so-conquests. From the peaks of success to the valleys of failure, every twist and turn in their adventure has led to the refinement of their craft. Prepare to be enthralled as they unravel the enigma of prompt design, revealing its pivotal role in shaping AI interactions. With each revelation, they unveil the power of perfect prompt design to elevate solutions, enchant customers, and conquer the myriad challenges that lie in wait. But this is no ordinary tale of technical prowess—no, dear reader, it is a grand odyssey teeming with intrigue and excitement. From the bustling streets of AI-powered applications to the untamed wilderness of off-topic queries, hallucinations, flat-out lies, and toxic language, our heroes navigate it all with cunning and finesse. Along the way, they impart their hard-earned wisdom, offering practical advice and cunning strategies to fellow travelers eager to tread the same path. With each chapter, they peel back the layers of mystery surrounding prompt engineering, illuminating the way forward for those brave enough to follow. So, dear reader, strap in and prepare for an adventure like no other. With our intrepid explorers as your guide, you’ll embark on a thrilling quest to unlock the secrets of prompt mastery and harness the full potential of AI-powered interactions. Why Prompt Design Matters Prompt design plays a crucial role in optimizing various aspects of your solution. A well-crafted prompt can: Let’s dive into the essential prompting approaches with the following table of contents: Prompts can be quite long and complex. Often, long, and carefully crafted prompts with the right ingredients can lead to a huge reduction in incorrectly processed user utterances. But always keep in mind that most prompt tokens have a price, i.e. the longer the prompt, the more expensive it is to call the API. Recently, however, there have been attempts to make prompt input tokens cheaper than output tokens. By mastering these prompting techniques, you can create prompts that not only enhance performance but also deliver exceptional customer experiences. Like Related Posts 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 How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more Sales Cloud Einstein Forecasting Salesforce, the global leader in CRM, recently unveiled the next generation of Sales Cloud Einstein, Sales Cloud Einstein Forecasting, incorporating Read more

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August 9, 2023in Salesforce Einstein
Salesforce Chat GPT
08Aug

What’s Happening with ChatGPT and Salesforce

ChatGPT and Salesforce The integration of ChatGPT with Salesforce presents an opportunity to streamline organizational processes, resulting in significant time savings. This integration not only enhances CRM data management with generative AI but also takes the customer experience to new heights. Integration Process: To integrate ChatGPT with Salesforce, the typical process involves creating an account on the OpenAI platform and generating an API key. These steps ensure successful integration by providing API access and authentication credentials. It’s crucial to treat the API key with the same security measures as a password to safeguard the integration. Salesforce and OpenAI Collaboration: While Salesforce offers access to AI through its Einstein GPT suite of products, Salesforce Admins and Developers have also begun directly integrating with OpenAI’s APIs and various AppExchange apps. They are incorporating these tools into their offerings to introduce new AI-powered features, enhancing their platforms’ capabilities. Role of ChatGPT and Salesforce Admins: ChatGPT does not aim to replace Salesforce Admins but rather to augment their effectiveness and productivity. Admins with strong analytical and administrative skills can leverage ChatGPT to expedite configuration tasks. However, mastering the collaboration with ChatGPT is essential to enhance the quality of results. Understanding ChatGPT: ChatGPT, developed by OpenAI, represents a breakthrough in AI beyond conventional chatbots. It operates on the OpenAI GPT-3.5 family of large language models, incorporating both supervised and reinforcement learning techniques. Backed by OpenAI, ChatGPT employs natural language processing (NLP) to interact conversationally, mirroring human dialogue. OpenAI, founded in San Francisco, boasts substantial support from industry leaders like Microsoft. OpenAI and GPT-4: OpenAI recently unveiled GPT-4, the latest iteration of its popular ChatGPT model. GPT-4 boasts enhanced capabilities, including image processing and increased word processing capacity. With millions of users since its launch, ChatGPT continues to evolve, offering unparalleled NLP functionality. Top Use Cases for ChatGPT in Salesforce: Explore the potential of ChatGPT and Salesforce with features and scenarios, including natural language processing, chatbots, predictive sales, personalized communication, and analysis of conversational data. Einstein GPT: Salesforce introduces Einstein GPT, the world’s first generative AI for CRM. Einstein GPT enables the generation of tailored content from CRM data, ensuring relevance in every interaction, be it emails, reports, knowledge articles, or code snippets. Community Insights: As the AI landscape, particularly ChatGPT, continues to evolve, it’s essential to understand its implications for the Salesforce ecosystem. The Salesforce community acknowledges the power of ChatGPT as a sophisticated chatbot built on advanced AI technology, poised to revolutionize various Salesforce roles and experiences. What is Salesforce doing with ChatGPT? The integration between ChatGPT and Salesforce can optimize a significant portion of organizational processes, resulting in considerable time savings. This not only enhances CRM data management, now powered by generative AI, but also elevates the customer experience to a new level. Can we integrate ChatGPT with Salesforce? This typically involves creating an account on the OpenAI platform and generating an API key. To set up your API access and authentication credentials for a successful ChatGPT for Salesforce integration, follow the steps below. Treat your API key like a password to protect the security of your integration. Is Salesforce using OpenAI? While Salesforce enables customers to access it via its Einstein GPT suite of products, Salesforce Admins and Developers have also started integrating directly with OpenAI’s APIs and the many AppExchange apps. They are now embedding it into their offerings to deliver new AI-powered features. ChatGPT has shown the world the potential of AI beyond simple chatbots. Join Tectonic on the AI journey and learn about How to use ChatGPT for Salesforce, Its use cases, Integration and how you can implement it in Salesforce. Salesforce’s new marketing message is AI + Data + CRM. It all adds up to customer magic. At the heart of this magic is Generative AI or GPT, not ChatGPT. Of course, AI advancements will mean very different things across the wide variety of Salesforce roles and day-to-day experiences. With more and more “GPT” tools being launched each week, how is the Salesforce community using the Chat variety in their day-to-day roles, and how do they see it evolving over the coming months and years? ChatGPT is nothing more than a chatbot, akin to Einstein bots. However, it’s a powerful one! Built on the OpenAI GPT-3.5 family of large language models and both supervised and reinforcement learning techniques, ChatGPT was trained on a vast amount of internet text with a cutoff in January 2022. It has been optimized for natural language processing tasks such as text generation, question answering, translation, and text classification. The technology behind ChatGPT is advanced and continues to evolve, relying on machine learning and deep learning techniques. 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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August 8, 2023in Einstein 1 Platform, Generative AI, Salesforce Einstein
ChatGPT Announces Custom Instructions
21Jul

ChatGPT Announces Custom Instructions

We’re rolling out custom instructions to empower you to tailor ChatGPT to better suit your needs. This feature will be accessible in beta starting today with the Plus plan, with availability expanding to all users over the next few weeks. Custom instructions enable you to incorporate preferences or requirements that you want ChatGPT to consider when generating responses. Based on feedback about the inconvenience of starting each ChatGPT conversation from scratch, we’ve engaged with users across 22 countries to grasp the crucial role of steerability in enabling our models to effectively adapt to diverse contexts and individual needs. Moving forward, ChatGPT will take your custom instructions into account in every conversation. The model will integrate these instructions into its responses, eliminating the need for you to repeat preferences or information in each interaction. For instance, a teacher designing a lesson plan no longer needs to reiterate that they’re teaching 3rd-grade science. Similarly, a developer preferring efficient code in a language other than Python can express it once, and ChatGPT will understand. Even tasks like grocery shopping for a large family become more seamless, with the model considering specifics like needing 6 servings in the grocery list. Plugins Incorporating instructions can also enhance your experience with plugins by providing relevant information to the plugins you utilize. For example, if you specify your city in your instructions and use a restaurant reservation plugin, ChatGPT might include your city when calling the plugin. Beta During the beta phase, ChatGPT may not always interpret custom instructions flawlessly—it might occasionally overlook instructions or apply them incorrectly. Safety We’ve adjusted our safety measures to accommodate the new ways users can instruct the model. For instance, our Moderation API is designed to prevent instructions from being saved if they violate our Usage Policies. Additionally, the model can decline or disregard instructions that lead to responses violating our policies. Privacy While we may leverage your custom instructions to enhance model performance, you can opt out of this via your data controls. Similar to ChatGPT conversations, we take steps to remove personal identifiers from custom instructions before using them to enhance model performance. Learn more about how we utilize conversations to improve model performance and your options in our Help Center. Like Related Posts 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 How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more Sales Cloud Einstein Forecasting Salesforce, the global leader in CRM, recently unveiled the next generation of Sales Cloud Einstein, Sales Cloud Einstein Forecasting, incorporating Read more

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July 21, 2023in Generative AI, Salesforce Einstein
Benioff Dreams of AI Making Messaging Intelligent
08Jun

Benioff Dreams of AI Making Messaging Intelligent

During the company’s recent earnings call, Salesforce CEO Marc Benioff spoke about the “revolutionary” impacts of generative AI, emphasizing the immense potential it holds for the tech industry. Benioff Dreams of AI Making Messaging Intelligent. Benioff highlighted Salesforce’s integration of OpenAI’s ChatGPT platform into Slack, the workplace messaging app owned by Salesforce. This integration aims to enhance Slack’s capabilities by enabling features such as conversation summarization, research assistance, and message drafting directly within the platform. Benioff expressed his vision for Slack to become intelligent itself, leveraging the wealth of data stored within the platform. He emphasized the transformative potential of generative AI in providing intelligent support to users, potentially revolutionizing customer service interactions and business operations. Salesforce’s clients, including Gucci, are already leveraging AI to enhance customer service experiences, reflecting the growing adoption of AI-driven solutions across various industries. Generative AI offers benefits such as time-saving automation of routine tasks, efficient research gathering, and concise information delivery, benefiting individuals in personal, professional, and academic contexts. However, concerns regarding the ethical implications of generative AI have also surfaced, including issues related to deepfake images, bias, and potential job displacement. Some advocate for stricter regulation and careful assessment of AI’s risks before further development. Benioff Dreams of AI Making Messaging Intelligent Despite these concerns, Benioff remains optimistic about the transformative potential of generative AI, describing it as a revolution that will reshape the world in unprecedented ways. He believes that we are only at the beginning of this AI revolution, with much more innovation and transformation yet to come. 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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June 8, 2023in Generative AI, Salesforce, Salesforce Einstein
Tableau Pulse and Tableau GPT
04Jun

Announcing Tableau Pulse and Tableau GPT

It’s fair to say that many are familiar with ChatGPT, the groundbreaking Large Language Model from OpenAI that has transformed how we work and interact with AI. At TC 2023, Tableau announced a new tool called Tableau GPT. But what exactly is Tableau GPT, and how does it fit into Tableau’s suite of products? Announcing Tableau Pulse and Tableau GPT. Tableau GPT Tableau GPT is an assistant leveraging the advanced capabilities of generative AI to simplify and democratize data analysis. Built from Einstein GPT, a Salesforce product developed in collaboration with OpenAI, Tableau GPT integrates generative AI into Tableau’s user experience. This integration aims to help users work smarter, learn faster, and communicate more effectively. During the Devs on Stage segment of the keynote at TC, Matthew Miller, Senior Director of Product Management, showcased Tableau GPT’s ability to generate calculations. For example, with a prompt like “Extract email addresses from JSON,” Tableau GPT quickly produces a calculation that users can copy into the calculation window. Tableau Pulse Tableau GPT also powers a new tool called Tableau Pulse, designed to generate powerful insights swiftly. Tableau Pulse provides “data digests” on a personalized metrics homepage, offering a curated, ‘newsfeed’-like experience of key KPIs. As users interact with Pulse, it learns to deliver more personalized results based on their interests. For example, Tableau Pulse highlights metrics that require attention, derived from recent data trends identified by Tableau GPT. The tool provides the latest metric values, visual trends, and AI-generated insights for user-selected KPIs. Tableau Pulse also enables users to ask questions about their data in natural language. For instance, when asked, “What is driving change in Appliance Sales?” Tableau Pulse responded with a brief answer and visualization. Further inquiries, such as “What else should I know about air fryers?” revealed that the “inventory fill rate” for air fryers is forecasted to fall below a set threshold, providing actionable insights that users can share across their organization. Future Impact and Availability Tableau GPT and Pulse promise to revolutionize interactions with Tableau products, enabling quicker visualization creation and making data accessible to non-technical users. Salesforce announced that Tableau Pulse and Tableau GPT would enter pilot testing later this year. When they do, we’ll be ready to share new insights. Follow us on LinkedIn to stay updated on all the latest developments and features in Tableau! 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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June 4, 2023in Generative AI
AI Drives Insights
07Apr

AI Drives Insights

Innovations from Salesforce, HubSpot, and One AI are driving deeper insights and streamlining processes. Key Takeaways: AI is transforming the way businesses operate, and customer relationship management (CRM) is no exception. AI has been influencing the CRM space for years, but its impact is now reaching new heights. By harnessing AI algorithms, modern CRM systems offer predictive analytics and deeper insights, enabling brands to understand their customers on an unprecedented level. Advanced AI-enabled CRMs even incorporate sentiment analysis to gauge customer perceptions and provide automation tools to free marketers from mundane tasks. The global AI market, currently valued at $142.3 billion, continues to expand rapidly. From 2020 to 2022, annual corporate investments in AI startups increased by $5 billion, reflecting the growing demand for AI-driven innovations. As CRM vendors introduce more AI capabilities, it’s important to understand the unique approaches each one takes to differentiate themselves and deliver specific benefits. Salesforce and Einstein GPT: A New Era with OpenAI’s ChatGPT On March 7, 2023, Salesforce introduced Einstein GPT, a generative AI technology integrated into its CRM platform. Combining real-time data from Salesforce’s Data Cloud with OpenAI’s ChatGPT, Einstein GPT allows users to input natural-language prompts to streamline tasks and decision-making. Salesforce has long invested in AI. In 2017, it launched its Einstein AI as part of Service Cloud. By 2019, Salesforce had partnered with OpenAI to explore AI research and integrate advanced models into its ecosystem. The acquisition of Slack in 2020 further strengthened its AI capabilities by incorporating advanced messaging and communication tools into the CRM environment. Marc Benioff, CEO of Salesforce, highlighted the significance of AI’s growth: “The world is experiencing one of the most profound technological shifts with real-time technologies and generative AI. This comes at a pivotal moment as every company is focused on connecting with their customers in more intelligent, automated, and personalized ways.” Einstein GPT is set to transform customer engagement, with applications across Salesforce’s various platforms, including Tableau, MuleSoft, and Slack. HubSpot CRM: AI-Powered Content Assistant A day before Salesforce’s AI announcement, HubSpot revealed its own AI-powered features: the Content Assistant and ChatSpot.ai. These tools aim to enhance CRM users’ productivity while creating stronger connections with customers. HubSpot’s Content Assistant helps marketing and sales teams ideate, create, and share content through generative AI capabilities. It can suggest blog titles, create content outlines, and assist with crafting content for blogs, emails, landing pages, and websites. ChatSpot.ai, on the other hand, offers a natural-language chat experience to simplify CRM tasks for HubSpot users. HubSpot has also invested in AI for other functions, including conversation intelligence, data enrichment, predictive analytics, and content optimization, solidifying its position in the AI-driven CRM landscape. With AI advancements from companies like Salesforce, HubSpot, and One AI, the future of CRM is poised for enhanced efficiency, automation, and personalized customer interactions. 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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April 7, 2023in Generative AI, Mulesoft, Salesforce
Einstein GPT and AI-Powered CRM
06Apr

Einstein GPT and AI-Powered CRM

As many of you are already aware, ChatGPT has become a prominent term in AI chatbot systems. It leverages predictive computing to respond to user questions and queries, spanning from recipes for favorite dishes to new song lyrics or even code writing assistance. It’s quite thrilling, isn’t it? Einstein GPT and AI-Powered CRM bring forth the world’s first generative AI tool for customer relationship management. In this insight, we’ll introduce you to Einstein GPT, a fusion of proprietary Einstein AI models with ChatGPT or other leading large language models. We’ll dig into its applications across sales, service, marketing, and development. Firstly, GPT stands for Generative Pre-Trained Transformer, an AI framework that generates text from datasets and offers human-like responses to user queries. Einstein GPT and AI-Powered CRM. Einstein GPT marks the world’s inaugural implementation of generative AI CRM technology, delivering AI-generated content across sales, service, marketing, commerce, and IT interactions, at scale. Although currently in a closed pilot phase, it’s set to transition to the BETA phase soon. Now, let’s explore how Einstein GPT can assist sales representatives in composing emails. How Einstein GPT Can Enhance Sales?Imagine you’re an Account Executive (AE) charged with engaging the prospect account, Escape LTD. You can kickstart by asking Einstein Assistant to provide an overview of the account, including recent news. It furnishes details instantly, eliminating the need for manual research (Time saver#1). Based on the information provided, it appears they are expanding operations to the US. You can delve deeper by exploring top contacts for the US expansion initiative. Mara Williams, the VP of sales, emerges as a key contact. Einstein has already identified the corresponding contact record for you (Time saver#2) and conveniently offers a “Compose Email” button to draft a personalized email to Mara instantly. Clicking on it generates a tailored email for you, ready to be copied into the email composer (Time saver#3). If you prefer a less formal tone, you can request Einstein to adjust accordingly (Time saver#4). You can also instruct Einstein to create a private Slack channel for real-time communication with Mara. Notably, it not only generates the link but also includes it in the email (Time saver#5). Once satisfied with the email, simply hit “Send,” and it’s on its way. We’ve added “Time saver#” just for fun, but truthfully, these are genuine time savers that you’ll appreciate when using Einstein GPT. As we know, an AE’s time is better spent interacting with customers than on email composition, meeting scheduling, or CRM data entry. As demonstrated above, composing emails is a breeze, showcasing how Einstein GPT streamlines sales processes. There’s much more Einstein GPT can do for Sales: Retrieve information on top contacts.Integrate sign-up forms.Extract insights about new accounts.Generate leads, and much more. As mentioned earlier, Einstein GPT extends its benefits to Service, Marketing, and Developers too: Einstein GPT for Service: Generate knowledge articles from past case notes, and auto-generate personalized agent chat replies for enhanced customer satisfaction. Einstein GPT for Marketing: Dynamically generate personalized content to engage customers across various channels. Einstein GPT for Developers: Boost developer productivity by generating code and addressing queries in languages like Apex. Pretty cool, right? We’re equally excited at Tectonic to witness Einstein GPT’s general availability. If you’re eager to learn more or need additional information about Einstein GPT, feel free to reach out— Tectonic is here to assist. 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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April 6, 2023in Generative AI, Salesforce Einstein
Slack and ChatGPT
05Mar

Slack and ChatGPT

Salesforce Inc announced its collaboration with OpenAI, the creator of ChatGPT, to integrate the chatbot technology into its Slack collaboration software and broaden the use of generative artificial intelligence across its business software. The San Francisco-based company unveiled EinsteinGPT, a technology merging its own AI capabilities with those of external partners like OpenAI. This collaboration aims to assist businesses in tasks such as drafting emails, managing customer accounts, and even generating computer code. Additionally, ChatGPT will integrate with Slack to help users summarize conversations and handle various queries. This strategic move reflects the competitive landscape among tech giants racing to enhance their platforms with generative AI, which can generate text, images, and other content based on historical data inputs. Microsoft Corp, for example, leveraging its investment in OpenAI, has integrated generative AI into its Teams product, enabling functionalities like generating meeting notes and suggesting email responses through its Viva Sales subscription. This places Teams in direct competition with Slack. Clara Shih, a general manager at Salesforce, highlighted during a press briefing that this announcement addresses the growing demand from businesses for advanced AI capabilities. She emphasized that Salesforce’s proprietary data and AI models would differentiate their offerings in the market. Salesforce’s initiative in generative AI is poised to transform customer engagement strategies for businesses, according to Shih, enabling them to innovate profoundly in their interactions with customers. In addition to this integration, Salesforce also unveiled a new fund aimed at investing in startups specializing in generative AI technologies. 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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March 5, 2023in Generative AI, Salesforce Slack
tectonic logo
14Nov

AI Large Language Models

What Exactly Constitutes a Large Language Model? Picture having an exceptionally intelligent digital assistant that extensively combs through text, encompassing books, articles, websites, and various written content up to the year 2021. Yet, unlike a library that houses entire books, this digital assistant processes patterns from the textual data it undergoes. This digital assistant, akin to a large language model (LLM), represents an advanced computer model tailored to comprehend and generate text with humanlike qualities. Its training involves exposure to vast amounts of text data, allowing it to discern patterns, language structures, and relationships between words and sentences. How Do These Large Language Models Operate? Fundamentally, large language models, exemplified by GPT-3, undertake predictions on a token-by-token basis, sequentially building a coherent sequence. Given a request, they strive to predict the subsequent token, utilizing their acquired knowledge of patterns during training. These models showcase remarkable pattern recognition, generating contextually relevant content across diverse topics. The “large” aspect of these models refers to their extensive size and complexity, necessitating substantial computational resources like powerful servers equipped with multiple processors and ample memory. This capability enables the model to manage and process vast datasets, enhancing its proficiency in comprehending and generating high-quality text. While the sizes of LLMs may vary, they typically house billions of parameters—variables learned during the training process, embodying the knowledge extracted from the data. The greater the number of parameters, the more adept the model becomes at capturing intricate patterns. For instance, GPT-3 boasts around 175 billion parameters, marking a significant advancement in language processing capabilities, while GPT-4 is purported to exceed 1 trillion parameters. While these numerical feats are impressive, the challenges associated with these mammoth models include resource-intensive training, environmental implications, potential biases, and more. Large language models serve as virtual assistants with profound knowledge, aiding in a spectrum of language-related tasks. They contribute to writing, offer information, provide creative suggestions, and engage in conversations, aiming to make human-technology interactions more natural. However, users should be cognizant of their limitations and regard them as tools rather than infallible sources of truth. What Constitutes the Training of Large Language Models? Training a large language model is analogous to instructing a robot in comprehending and utilizing human language. The process involves: Fine-Tuning: A Closer Look Fine-tuning involves further training a pre-trained model on a more specific and compact dataset than the original. It is akin to training a robot proficient in various cuisines to specialize in Italian dishes using a dedicated cookbook. The significance of fine-tuning lies in: Versioning and Progression Large language models evolve through versions, with changes in size, training data, or parameters. Each iteration aims to address weaknesses, handle a broader task spectrum, or minimize biases and errors. The progression is simplified as follows: In essence, large language model versions emulate successive editions of a book series, each release striving for refinement, expansiveness, and captivating capabilities. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more 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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November 14, 2022in Data, Salesforce, Salesforce Einstein, Technology
ChatGPT and Einstein GPT
08Nov

ChatGPT and Einstein GPT

Artificial intelligence (AI) has been rapidly advancing globally, with breakthroughs captivating professionals across various sectors. One milestone that has gained significant attention is the emergence of ChatGPT, a cutting-edge language model revolutionizing the tech landscape. This development has profoundly impacted businesses relying on Salesforce for their customer relationship management (CRM) needs. In March 2023, Salesforce unveiled its latest AI innovation, Einstein GPT, promising to transform how companies engage with their clientele. In this article, we explore what Salesforce Einstein GPT entails and how it can benefit teams across diverse industries. When OpenAI introduced ChatGPT in November 2022, they didn’t expect the overwhelming response it received. Initially positioned as a “research preview,” this AI chatbot aimed to refine existing technology while soliciting feedback from users. However, ChatGPT quickly became a viral sensation, surpassing OpenAI’s expectations and prompting them to adapt to its newfound popularity. Developed on the foundation of the GPT-3.5 language model, ChatGPT was specifically tailored to facilitate engaging and accessible conversations, distinguishing it from its predecessors. Its launch attracted a diverse user base keen to explore its capabilities, prompting OpenAI to prioritize addressing potential misuse and enhancing its safety features. As ChatGPT gained traction, it caught the attention of Salesforce, a leading CRM provider. In March 2023, Salesforce unveiled Einstein GPT, its own AI innovation, poised to transform customer engagement. Built on the GPT-3 architecture and seamlessly integrated into Salesforce Clouds, Einstein GPT promised to revolutionize how businesses interact with their clientele. Einstein GPT boasts a range of features designed to personalize customer experiences and streamline workflows. From generating natural language responses to crafting personalized content and automating tasks, Einstein GPT offers versatility and value across industries. By leveraging both Einstein AI and GPT technology, businesses can unlock unprecedented efficiency and deliver superior customer experiences. Despite its success, OpenAI acknowledges the need for ongoing refinement and vigilance, emphasizing the importance of responsible deployment and transparency in the development of AI technology. Exploring Einstein GPT Salesforce presents Einstein GPT as the premier generative AI tool for CRM worldwide. Utilizing the advanced GPT-3 architecture, Einstein GPT seamlessly integrates into all Salesforce Clouds, including Tableau, MuleSoft, and Slack. This groundbreaking technology empowers users to generate natural language responses to customer inquiries, craft personalized content, and compose entire email messages on behalf of sales personnel. With its high degree of customization, Einstein GPT can be finely tuned to meet the specific needs of various industries, use cases, and customer requirements, delivering significant value to businesses of all sizes and sectors. Objectives of Salesforce AI Einstein GPT Salesforce AI Einstein GPT is designed to achieve several key objectives: Distinguishing Einstein GPT from Einstein AI Einstein GPT represents the latest evolution of Salesforce’s Einstein artificial intelligence technology. Unlike its predecessors, Einstein GPT integrates proprietary Einstein AI models with ChatGPT and other leading large language models. This integration enables users to interact with CRM data using natural language prompts, resulting in highly personalized, AI-generated content and triggering powerful automations that enhance workflows and productivity. By leveraging both Einstein AI and GPT technology, businesses can achieve unparalleled efficiency and deliver exceptional customer experiences. Features of Einstein GPT in Salesforce CRM Key features and capabilities of Salesforce Einstein chatbot GPT include: Utilizing Einstein GPT for Business Improvement Einstein GPT can be leveraged across various domains to enhance business operations: Integration with Salesforce Data Cloud Salesforce Data Cloud, a cloud-based data management system, enables real-time data aggregation from diverse sources. Einstein GPT utilizes unified customer data profiles from the Salesforce Data Cloud to personalize interactions throughout the customer journey. OpenAI on ChatGPT Methods We trained this model using Reinforcement Learning from Human Feedback (RLHF), using the same methods as InstructGPT, but with slight differences in the data collection setup. We trained an initial model using supervised fine-tuning: human AI trainers provided conversations in which they played both sides—the user and an AI assistant. We gave the trainers access to model-written suggestions to help them compose their responses. We mixed this new dialogue dataset with the InstructGPT dataset, which we transformed into a dialogue format. To create a reward model for reinforcement learning, we needed to collect comparison data, which consisted of two or more model responses ranked by quality. To collect this data, we took conversations that AI trainers had with the chatbot. We randomly selected a model-written message, sampled several alternative completions, and had AI trainers rank them. Using these reward models, we can fine-tune the model using Proximal Policy Optimization. We performed several iterations of this process. ChatGPT is fine-tuned from a model in the GPT-3.5 series, which finished training in early 2022. You can learn more about the 3.5 series here. ChatGPT and GPT-3.5 were trained on an Azure AI supercomputing infrastructure. Limitations ChatGPT and Einstein GPT Salesforce Einstein GPT signifies a significant advancement in AI technology, empowering businesses to deliver tailored customer experiences and streamline operations. With its integration into Salesforce CRM and other platforms, Einstein GPT offers unprecedented capabilities for personalized engagement and automated insights, ensuring organizations remain competitive in today’s dynamic market landscape. When OpenAI quietly launched ChatGPT in late November 2022, the San Francisco-based AI company didn’t anticipate the viral sensation it would become. Initially viewed as a “research preview,” it was meant to showcase a refined version of existing technology while gathering feedback from the public to address its flaws. However, the overwhelming success of ChatGPT caught OpenAI off guard, leading to a scramble to capitalize on its newfound popularity. ChatGPT, based on the GPT-3.5 language model, was fine-tuned to be more conversational and accessible, setting it apart from previous iterations. Its release marked a significant milestone, attracting millions of users eager to test its capabilities. OpenAI quickly realized the need to address potential misuse and improve the model’s safety features. Since its launch, ChatGPT has undergone several updates, including the implementation of adversarial training to prevent users from exploiting it (known as “jailbreaking”). This technique involves pitting multiple chatbots against each other to identify and neutralize malicious behavior. Additionally,

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November 8, 2022in Generative AI, Mulesoft, Salesforce, Salesforce Data Cloud, Salesforce Einstein

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