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Training Your Own AI Model
19Nov

Training Your Own AI Model May Not Win Customers – But Leveraging Data Will

While AI plays a crucial role in delivering personalized customer experiences, the real game-changer lies in data connectivity. It’s not about having the flashiest AI model but about how data is managed across every interaction. Imagine a global retail brand with a network of physical stores, an e-commerce site, and active social media. An in-store shopper “likes” a product on Instagram, triggering an autonomous agent to send a personalized discount code. This leads them to a tailored shopping page based on their preferences, with items in their size, style, and budget. After the purchase, the agent follows up to ensure satisfaction and offers discounts on complementary items, creating a VIP-like experience. While AI can automate these touchpoints, the real enabler is a robust data platform that brings each data point into a cohesive, connected system. Without this, AI’s effectiveness is limited; Gartner predicts that 30% of generative AI projects will be abandoned by 2025 due to poor data quality. Why Data – Not a Custom AI Model – Drives Results Many companies think they need a custom-trained language model (LLM) to achieve superior customer insights. However, training an LLM is costly, requires specialized expertise, and lacks built-in permission controls. And because LLMs require constant updates to stay relevant, it’s a significant commitment with diminishing returns. Instead, a more practical approach is to integrate a pre-trained LLM with a data platform that aggregates data from diverse systems in real time. By providing AI with current, connected data, companies can enhance accuracy, identify trends, and respond to customer needs quickly. The Power of Integrated Data Platforms Consider the shopper who “liked” a social media post and then interacted with an autonomous agent. Alone, these interactions may not signify much, but together they reveal a clear buying intent. Scaling this approach allows brands to spot valuable opportunities faster. Autonomous agents can help sales teams prioritize prospects, resolve service cases, and drive sales – all in real time. This isn’t limited to retail. In healthcare, agents can streamline patient benefits verification, summarize insurance details, and assist with pharmacy coverage. From finance to manufacturing, data-enabled AI agents have endless applications. Metadata: The Secret to Seamless AI For AI to deliver tailored, high-value interactions, it needs organized data – and metadata is key. By categorizing and tagging data, metadata enables AI to find the right information, whether drafting a customer message or identifying top sales prospects. A unified data platform that harmonizes data and metadata yields sharper predictions and more personalized outputs, outperforming siloed data systems. Data-Driven Precision in Customer Experience An effective data platform is the backbone of an agile, customer-centric business, making AI far more than guesswork. By using data to understand customer needs in real time, businesses can deliver targeted, timely experiences that build loyalty and growth. A comprehensive data strategy is essential to unlocking AI’s potential. This data strategy guide provides best practices for aligning data with business goals, unearthing insights, and enhancing customer engagement. A unified data platform connects your organization around the customer – transforming a simple “like” into a sale, and a sale into loyalty. Without it, your AI is just guessing. 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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November 19, 2024in Data, Generative AI
ChatGPT and Politics?
02Nov

ChatGPT and Politics?

ChatGPT has also appeared in influence operations, with groups using it to generate political content for social media. OpenAI observed an Iranian-led operation, Storm-2035, using ChatGPT to publish politically charged content about U.S. elections and global conflicts. Yet, OpenAI noted that these AI-driven influence efforts often lack audience engagement.

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November 2, 2024in AI Tools, Generative AI
Cool and New AI
31Oct

Cool and New AI Cool and New AI

AI is revolutionizing the way we work, offering a wide range of tools beyond ChatGPT that can enhance efficiency, creativity, and productivity. Whether you’re working with data, code, marketing, videos, images, AI bots, or research, here are the top AI tools that can transform your workflow. Cool and New AI. Don’t get spooked. There will be a cornucopia more in November. 🌟 Code 1️⃣ GlideTurn spreadsheets into powerful mobile apps without writing a single line of code. Glide makes it easy for non-developers to create professional apps with minimal effort. 2️⃣ BubbleA visual programming platform that allows users to build web applications without any coding knowledge. Ideal for entrepreneurs and startups looking to launch digital products quickly. 3️⃣ AskCodiThis AI coding assistant speeds up coding tasks, offers helpful suggestions, and simplifies debugging for developers, making it a must-have tool for coding professionals. 🌟 Data 1️⃣ BasedLabsA robust data analytics platform designed for scientists and engineers. BasedLabs offers complex data processing and model building with exceptional precision. 2️⃣ Coral AIPerfect for data-driven professionals, Coral AI provides efficient edge AI tools for processing large datasets and delivering insights with on-device intelligence, speeding up tasks. 3️⃣ JuliusAn AI-powered tool for market researchers and data analysts, Julius streamlines data processes and offers powerful insights into market trends. 🌟 Marketing 1️⃣ Sprout SocialThis all-in-one social media management platform leverages AI to help marketers optimize their social presence, engage with audiences, and track detailed analytics. 2️⃣ AdCreative AIEnhance your marketing campaigns with AI-generated ads that convert. AdCreative AI allows marketers to design high-impact, creative ads effortlessly. 3️⃣ Jasper AIA top tool for content creators, Jasper AI assists in crafting high-conversion marketing copy, blogs, and ad texts at scale, making it indispensable for digital marketing. 🌟 Video 1️⃣ SynthesiaCreate professional videos without the need for cameras or actors. Synthesia’s AI avatars enable you to produce multilingual videos, making it ideal for corporate and educational content. 2️⃣ HeygenThis AI tool simplifies video production by allowing users to create AI-generated videos, perfect for marketing campaigns or training materials. 3️⃣ Opus ClipOpus Clip transforms long-form video content into short, engaging clips optimized for social media, helping creators repurpose content easily. 🌟 Image 1️⃣ Getimg.AIAutomate image creation with Getimg.AI, which enhances your visual content by generating high-quality images in minutes, speeding up the design process. 2️⃣ PicsartA versatile image editing and design platform with AI tools that make creating stunning visuals effortless, making it ideal for social media, advertising, and creative projects. 3️⃣ Leonardo AIA powerful AI-driven tool for creators, Leonardo AI helps generate high-quality images, illustrations, and graphics, making it an essential tool for designers and artists. 🌟 AI Bot 1️⃣ LiveChatAn AI-powered live chat solution that integrates seamlessly into websites to provide real-time customer support, enhancing business communication. 2️⃣ LandbotThis tool helps create conversational experiences with AI-powered chatbots for customer support, sales, and lead generation, automating client interactions. 3️⃣ CustomGPTA customizable GPT-powered AI chatbot tailored for specific industries and businesses, perfect for providing personalized customer support. 🌟 Research 1️⃣ ChatPDFTurn PDFs into interactive documents with ChatPDF, allowing users to easily navigate and extract information using an AI-based assistant. 2️⃣ VidIQVidIQ provides AI-powered tools to optimize YouTube content for better engagement and visibility, making it invaluable for content creators. 3️⃣ SemrushAn advanced SEO platform powered by AI, Semrush gives marketers and researchers deep insights into online visibility, helping boost content performance. AI extends far beyond ChatGPT. This diverse range of tools is designed to make your work more efficient and productive, whether you’re coding, marketing, creating content, or conducting research. Embrace these AI tools to unlock new levels of creativity and efficiency. 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 31, 2024in AI Tools, Data
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
Transform Customer Experience
25Oct

Transform Customer Experience

In today’s AI-driven business environment, customer experience (CX) has evolved from being a buzzword to a critical factor in determining success. It’s no longer enough for businesses to offer high-quality products or excellent service alone—today’s customers are always online, engaged, and seeking the most convenient, relevant, and enjoyable experiences. This is where Salesforce Data Cloud becomes a game-changer, providing the tools needed to meet modern customer expectations. Transforming Customer Experience with Salesforce Data Cloud Salesforce enables businesses to collect, integrate, and leverage critical customer information within its ecosystem, offering an all-encompassing view of each customer. This unified customer data allows organizations to forecast visitor trends, assess marketing impact, and predict customer behavior. As data-driven decision-making becomes increasingly central to business strategy, Salesforce Data Cloud and its Customer Data Platform (CDP) features provide a significant competitive edge—whether in e-commerce, fintech, or B2B industries. Data Cloud is more than just your traditional CDP. It’s the only data platform native to the world‘s #1 AI CRM. This means that marketers can quickly access and easily action on unified data – from across the entire business – to drive growth and increase customer lifetime value. Data Cloud’s Role in Enhancing CX By unifying data in one place, Salesforce Data Cloud enables organizations to access real-time customer insights. This empowers them to track customer activity across channels like email, social media, and online sales, facilitating targeted marketing strategies. Businesses can analyze customer behavior and deliver personalized messaging, aligning marketing, sales, and customer service efforts to ensure consistency. With these capabilities, Salesforce customers can elevate the CX by delivering the right content, at the right time, to the right audience, ultimately driving customer satisfaction and growth. New Features of Salesforce Data Cloud Salesforce continues to evolve, introducing cutting-edge features that reshape customer interaction: To fully maximize these features, partnering with a Salesforce Data Cloud consultant can help businesses unlock the platform’s full potential and refine their customer engagement strategies. Agentic AI Set to Supercharge Business Processes Salesforce’s vision extends beyond customer relationship management with the integration of Agentic AI through its Customer 360 platform. According to theCUBE Research analysts, this signals a shift toward using AI agents to automate complex business processes. These AI agents, built on Salesforce’s vast data resources, promise to revolutionize how companies operate, offering customized, AI-driven business tools. “If they can pull this off, where it becomes a more dynamic app platform, more personalized, really focused on those processes all the way back to the data, it’s going to be a clear win for them,” said Strechay. “They’re sitting on cloud; they’re sitting on IaaS. That’s a huge win from that perspective.” AI agents create a network of microservices that think and act independently, involving human intervention only when necessary. This division of labor allows businesses to capture expertise in routine tasks while freeing human workers to focus on more complex decision-making. However, the success of these AI agents depends on access to accurate and reliable data. As Gilbert explained, “Agents can call on other agents, and when they’re not confident of a step in a process or an outcome, they can then bounce up to an inbox for a human to supervise.” The goal isn’t to eliminate humans but to capture their expertise for simpler processes. Empowering Developers and Citizen Creators At the core of this AI-driven transformation is Salesforce’s focus on developers. The platform’s low-code tools allow businesses to easily customize AI agents and automate business processes, empowering both experienced developers and citizen creators. With simple language commands or goal-setting, companies can build and train these AI agents, streamlining operations. “It’s always going to be about good data—that’s the constant,” Bertrand said. “The second challenge is how to train agents and humans to work together effectively. While some entry-level jobs may be replaced, AI will continue to evolve, creating new opportunities in the future.” Is Salesforce Data Cloud the Right Fit for Your Business? Salesforce Data Cloud offers comprehensive capabilities for businesses of all sizes, but it’s essential to assess whether it aligns with your specific needs. The platform is particularly valuable for: For businesses that fit these scenarios, working with Salesforce’s partner ecosystem or a Data Cloud consultant can help ensure successful integration and optimization. What’s New in Salesforce’s Latest Release? The latest Salesforce Spring Release introduced several exciting features, further enhancing Salesforce Data Cloud: These updates reflect Salesforce’s commitment to providing innovative, data-driven solutions that enhance customer experiences and drive business success. 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 25, 2024in AI Tools, Data, Generative AI, Salesforce, Salesforce Data Cloud
Fully Formatted Facts
20Oct

Fully Formatted Facts

A recent discovery by programmer and inventor Michael Calvin Wood is addressing a persistent challenge in AI: hallucinations. These false or misleading outputs, long considered an inherent flaw in large language models (LLMs), have posed a significant issue for developers. However, Wood’s breakthrough is challenging this assumption, offering a solution that could transform how AI-powered applications are built and used. The Importance of Wood’s Discovery for Developers Wood’s findings have substantial implications for developers working with AI. By eliminating hallucinations, developers can ensure that AI-generated content is accurate and reliable, particularly in applications where precision is critical. Understanding the Root Cause of Hallucinations Contrary to popular belief, hallucinations are not primarily caused by insufficient training data or biased algorithms. Wood’s research reveals that the issue stems from how LLMs process and generate information based on “noun-phrase routes.” LLMs organize information around noun phrases, and when they encounter semantically similar phrases, they may conflate or misinterpret them, leading to incorrect outputs. How LLMs Organize Information For example: The Noun-Phrase Dominance Model Wood’s research led to the development of the Noun-Phrase Dominance Model, which posits that neural networks in LLMs self-organize around noun phrases. This model is key to understanding and eliminating hallucinations by addressing how AI processes noun-phrase conflicts. Fully-Formatted Facts (FFF): A Solution Wood’s solution involves transforming input data into Fully-Formatted Facts (FFF)—statements that are literally true, devoid of noun-phrase conflicts, and structured as simple, complete sentences. Presenting information in this format has led to significant improvements in AI accuracy, particularly in question-answering tasks. How FFF Processing Works While Wood has not provided a step-by-step guide for FFF processing, he hints that the process began with named-entity recognition using the Python SpaCy library and evolved into using an LLM to reduce ambiguity while retaining the original writing style. His company’s REST API offers a wrapper around GPT-4o and GPT-4o-mini models, transforming input text to remove ambiguity before processing it. Current Methods vs. Wood’s Approach Current approaches, like Retrieval Augmented Generation (RAG), attempt to reduce hallucinations by adding more context. However, these methods often introduce additional noun-phrase conflicts. For instance, even with RAG, ChatGPT-3.5 Turbo experienced a 23% hallucination rate when answering questions about Wikipedia articles. In contrast, Wood’s method focuses on eliminating noun-phrase conflicts entirely. Results: RAG FF (Retrieval Augmented Generation with Formatted Facts) Wood’s method has shown remarkable results, eliminating hallucinations in GPT-4 and GPT-3.5 Turbo during question-answering tasks using third-party datasets. Real-World Example: Translation Error Elimination Consider a simple translation example: This transformation eliminates hallucinations by removing the potential noun-phrase conflict. Implications for the Future of AI The Noun-Phrase Dominance Model and the use of Fully-Formatted Facts have far-reaching implications: Roadmap for Future Development Wood and his team plan to expand their approach by: Conclusion: A New Era of Reliable AI Wood’s discovery represents a significant leap forward in the pursuit of reliable AI. By aligning input data with how LLMs process information, he has unlocked the potential for accurate, trustworthy AI systems. As this technology continues to evolve, it could have profound implications for industries ranging from healthcare to legal services, where AI could become a consistent and reliable tool. While there is still work to be done in expanding this method across all AI tasks, the foundation has been laid for a revolution in AI accuracy. Future developments will likely focus on refining and expanding these capabilities, enabling AI to serve as a trusted resource across a range of applications. Experience RAGFix For those looking to explore this technology, RAGFix offers an implementation of these groundbreaking concepts. Visit their official website to access demos, explore REST API integration options, and stay updated on the latest advancements in hallucination-free AI: Visit RAGFix.ai Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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 20, 2024in Salesforce
LLMs and AI
19Oct

LLMs and AI

Large Language Models (LLMs): Revolutionizing AI and Custom Solutions Large Language Models (LLMs) are transforming artificial intelligence by enabling machines to generate and comprehend human-like text, making them indispensable across numerous industries. The global LLM market is experiencing explosive growth, projected to rise from $1.59 billion in 2023 to $259.8 billion by 2030. This surge is driven by the increasing demand for automated content creation, advances in AI technology, and the need for improved human-machine communication. Several factors are propelling this growth, including advancements in AI and Natural Language Processing (NLP), large datasets, and the rising importance of seamless human-machine interaction. Additionally, private LLMs are gaining traction as businesses seek more control over their data and customization. These private models provide tailored solutions, reduce dependency on third-party providers, and enhance data privacy. This guide will walk you through building your own private LLM, offering valuable insights for both newcomers and seasoned professionals. What are Large Language Models? Large Language Models (LLMs) are advanced AI systems that generate human-like text by processing vast amounts of data using sophisticated neural networks, such as transformers. These models excel in tasks such as content creation, language translation, question answering, and conversation, making them valuable across industries, from customer service to data analysis. LLMs are generally classified into three types: LLMs learn language rules by analyzing vast text datasets, similar to how reading numerous books helps someone understand a language. Once trained, these models can generate content, answer questions, and engage in meaningful conversations. For example, an LLM can write a story about a space mission based on knowledge gained from reading space adventure stories, or it can explain photosynthesis using information drawn from biology texts. Building a Private LLM Data Curation for LLMs Recent LLMs, such as Llama 3 and GPT-4, are trained on massive datasets—Llama 3 on 15 trillion tokens and GPT-4 on 6.5 trillion tokens. These datasets are drawn from diverse sources, including social media (140 trillion tokens), academic texts, and private data, with sizes ranging from hundreds of terabytes to multiple petabytes. This breadth of training enables LLMs to develop a deep understanding of language, covering diverse patterns, vocabularies, and contexts. Common data sources for LLMs include: Data Preprocessing After data collection, the data must be cleaned and structured. Key steps include: LLM Training Loop Key training stages include: Evaluating Your LLM After training, it is crucial to assess the LLM’s performance using industry-standard benchmarks: When fine-tuning LLMs for specific applications, tailor your evaluation metrics to the task. For instance, in healthcare, matching disease descriptions with appropriate codes may be a top priority. Conclusion Building a private LLM provides unmatched customization, enhanced data privacy, and optimized performance. From data curation to model evaluation, this guide has outlined the essential steps to create an LLM tailored to your specific needs. Whether you’re just starting or seeking to refine your skills, building a private LLM can empower your organization with state-of-the-art AI capabilities. For expert guidance or to kickstart your LLM journey, feel free to contact us for a free consultation. 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 19, 2024in Artificial Intelligence, Data, Google, Salesforce, Technology
AI Agents, Tech's Next Big Bet
13Oct

AI Agents, Tech’s Next Big Bet

What Marketers Need to Know About AI Agents, Tech’s Next Big Bet Companies like Salesforce and OpenAI are making significant investments in AI agents, which are digital assistants poised to represent the next evolution of artificial intelligence. These agents promise to autonomously handle a variety of tasks, from making reservations to negotiating business deals. During OpenAI’s DevDay event in San Francisco last week, the company showcased a voice bot that successfully ordered 400 chocolate-covered strawberries from a local delivery service, specifying delivery and payment terms with minimal issues. OpenAI CEO Sam Altman stated, “2025 is when agents will work,” highlighting the potential for these technologies to revolutionize workflows. While this may seem futuristic, companies like Salesforce, HubSpot, and Pactum AI are already implementing their own AI agents, though examples from brands like Qantas Airways remain relatively scarce—a point of discussion at Advertising Week New York. What Are AI Agents? AI agents extend beyond mere chatbots. According to Parasvil Patel, a partner at Radical Ventures, they lack a single unifying definition and encompass a wide range of functionalities, from automating workflows to scheduling meetings. The overarching goal, however, is clear: “The ultimate aim is to execute work autonomously,” Patel explained. Currently, AI agents are in the “co-pilot” phase, handling specific tasks such as summarizing meetings. The true breakthrough will occur when they transition to “autopilot,” managing more complex tasks without human intervention. According to Patel, this shift could take up to 24 months. When Did They Emerge? AI agents first gained attention on social media in early 2023, with various startups, including AutoGPT—an open-source application built on OpenAI’s models—promising autonomous capabilities. However, Patel notes that many of these early experiments were not robust enough to be deployed effectively in production environments. How Are Companies Using AI Agents? The appeal of AI agents lies in their ability to save time, enhance efficiency, and free employees from repetitive tasks. For instance, a large distribution company struggling to manage 100,000 suppliers utilized Pactum’s AI, which deploys autonomous agents for negotiations. Instead of seeing negotiations as a dead end, these AI agents continuously customized payment deals based on the speed of suppliers’ goods. This approach led to price discounts, rebates, and allowances. Salesforce has also seen positive results with its AI agents. Its pilot program, AgentForce, launched with five clients—including OpenTable and global publisher Wiley—and achieved a 40% increase in case resolution compared to its previous chatbot for Wiley. At the firm’s Dreamforce event, Salesforce demonstrated AgentForce with Ask Astro, assisting attendees in planning their schedules by suggesting sessions and making reservations. Salesforce’s chief marketing officer, Ariel Kelman, stated that the company has heavily invested in developing its AI agent platform in response to client demand. “What companies are figuring out with generative AI is how to deliver productivity improvements for employees and provide meaningful interactions with customers,” he noted. What About Roadblocks? The journey to fully functional AI agents is not without challenges. Managing different data formats—text, images, and videos—can be complex, as highlighted by William Chen, director of product management for AI & emerging tech at Agora. “Your system is only as good as your data source,” he said. For Salesforce, the challenge lies in the nascent customer adoption of AI agents, with companies just beginning to explore how to leverage them for productivity, according to Kelman. The key challenge is determining what solutions work best for employees and customers across various use cases. Are Jobs at Risk? Not necessarily. AI agents are unlikely to replace jobs in the immediate future. Instead, they allow employees to focus on more strategic and meaningful tasks. Rand explained, “The role of people will shift to configuring the autopilot, rather than flying the plane, which is a positive change.” For example, a major logistics client of Pactum, which previously relied on human negotiators for managing deals with freight providers, can now use AI agents to negotiate more efficiently. This adaptability allows companies to dynamically shift their business strategies based on market conditions. What’s Next? While early adopters of AI agents are seeing initial successes, there’s much more to discover. Salesforce plans to launch its next AI agent later this month: a Sales Development Representative (SDR) designed to manage early-stage sales interactions. Typically, human SDRs follow up on marketing leads through emails and calls, but this AI agent will qualify leads, providing human salespeople with a targeted list of 50 to 100 prospects eager to engage. “Instead of receiving a list of 500 leads, they’ll get a refined list of those who actually want to talk,” Kelman concluded. 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 13, 2024in Artificial Intelligence, Data, Generative AI, Salesforce
What Makes a True AI Agent
03Oct

What Makes a True AI Agent

What Makes a True AI Agent? Rethinking the Pursuit of Autonomy Unpacking the Core Traits of AI Agents — And Why Foundations Matter More Than Buzzwords The tech industry is enamored with AI agents. From sales bots to autonomous systems, companies like Salesforce and HubSpot claim to offer groundbreaking AI agents. Yet, I’ve yet to encounter a truly autonomous, agentic experience built from LLMs. The market is awash with what I call “botshit,” and if the best Salesforce can do is improve slightly over a mediocre chatbot, that’s underwhelming. What Makes a True AI Agent? But here’s the critical question everyone is missing: even if we could build fully autonomous AI agents, how often would they be the best solution for users? To explore this, let’s consider travel planning through the lens of agents and assistants. This use case helps clarify what each trait of agentic behavior brings to the table and offers a framework for evaluating AI products beyond the hype. By the end of this piece, you’ll be able to decide whether AI autonomy is a worthwhile investment or a costly distraction. The Spectrum of Agentic Behavior: A Practical Framework There’s no consensus on what truly defines an AI “agent.” Instead of relying on a binary classification, I suggest adopting a spectrum framework with six key attributes from AI research. This approach is more useful in today’s landscape because: Using the example of a travel “agent,” we’ll explore how different implementations fall on this spectrum. Most real-world applications land somewhere between “basic” and “advanced” tiers across the six traits. This framework will help you make informed decisions about AI integration and communicate more effectively with both technical teams and end users. By the end, you’ll be equipped to: What Makes a True AI Agent The Building Blocks of Agentic Behavior 1. Perception The ability to sense and interpret its environment or relevant data streams. An agent with advanced perception could, for instance, notice your preference for destinations with excellent public transit and factor that into future recommendations. 2. Interactivity The ability to engage with its environment, users, and external systems. LLMs like ChatGPT have set a high bar for interactivity. However, most customer support bots struggle because they need to integrate company-specific data and backend systems, prioritizing accuracy over creativity. 3. Persistence The ability to store, maintain, and update long-term memories about users and interactions. True persistence requires systems that not only store data but also evolve with each interaction, much like how a human travel agent remembers your favorite seat on a plane. 4. Reactivity The ability to respond to changes in its environment in real time. For example, a reactive system could suggest alternative travel dates if hotel prices surge due to a local event. 5. Proactivity The ability to anticipate needs and offer relevant suggestions unprompted. True proactivity requires robust perception, persistence, and reactivity to offer timely, context-aware suggestions. 6. Autonomy The ability to operate independently and make decisions within defined parameters. Autonomy varies by the level of resource control, impact scope, and operational boundaries. For example: The more complex the task and the greater the impact of a mistake, the more safeguards and precision the system needs. Proactive Autonomy: A Future Frontier The next step is proactive autonomy — the ability to modify goals or parameters to achieve overarching objectives. While theoretically possible, this introduces new risks and complexities, bringing us closer to the scenarios seen in sci-fi, where AI systems operate beyond human control. Most companies are nowhere near this level, and prioritizing foundation work like perception and persistence is far more practical for today’s needs. Agents vs. Assistants: A Useful Distinction An AI agent demonstrates at least five of the six attributes and exhibits autonomy within its domain. An AI assistant excels in perception, interactivity, and persistence but lacks autonomy or proactivity. It primarily responds to human requests and relies on human oversight for decisions. While many AI systems today are labeled “agents,” most function more like assistants. A Roomba, for example, is closer to an agent, autonomously navigating and adapting within a predefined space. On the other hand, tools like GitHub Copilot serve as powerful assistants, enhancing user capabilities without making independent decisions. Foundations Before Flash: The Role of Data Despite all the AI buzz, few companies today have the data foundations to support meaningful agentic behavior. For instance, most customer interactions rely on nuanced, unwritten information that is hard to automate. Missing perception foundations and inadequate testing lead to the “botshit” plaguing the industry. The key is to focus on building strong foundations in perception, interactivity, and persistence before tackling full autonomy. Start with the Problem: Why User-Centric AI Wins Before chasing the dream of autonomous agents, companies should start by asking what users actually need. Many organizations would benefit more from developing reliable assistants rather than fully autonomous systems. Real user problems, like those solved by Waymo and Roomba, offer clear paths to valuable AI solutions. The Path Forward: Align Data, Systems, and User Needs When deciding where to invest in AI: By focusing on foundational pillars, companies can build AI systems that solve immediate problems, laying the groundwork for more advanced capabilities in the future. Whether you’re developing agents, assistants, or indispensable tools, aligning solutions with real user needs is the key to meaningful progress. Contact Tectonic for assistance answering the question What Makes a True AI Agent work for my business? 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

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October 3, 2024in Data, Salesforce
Salesforce Data Cloud Innovations Announced
01Oct

Salesforce Data Cloud Innovations Announced

Salesforce Data Cloud Drives Customer Experience Transformation with New Data and AI Innovations Salesforce has unveiled new innovations within its Data Cloud, enhancing the way organizations transform customer experiences by leveraging data and AI. Key advancements include expanded support for unstructured data, enabling native processing of audio and video content—such as webinars and calls—to unlock deeper insights into customer behavior. A standardized semantic data model ensures both Agentforce Agents and humans can consistently interpret and use data, while improved search capabilities enhance information discovery by factoring in customer context. Real-time data activations allow organizations to respond instantly to evolving customer needs, and new data security and governance features safeguard operations and prevent data exposure. “In this new era of AI and Agents, customer data and metadata are the new gold for the enterprise,” said Rahul Auradkar, EVP and GM for Data Cloud at Salesforce. “More companies are using Data Cloud to unify all their data—from customer interactions and product usage to IoT and social media data—gaining deeper insights across all touchpoints. Because Data Cloud is foundational to Salesforce, companies can act on this data to deliver personalized, meaningful customer experiences.” Data Cloud integrates with Salesforce Customer 360, Agentforce, Flow, and analytics, providing a trusted data foundation for personalized experiences and real-time analytics. It powers data-driven actions and workflows while securely enabling AI across all Salesforce applications. Leveraging zero-copy technology and MuleSoft connectors, Data Cloud can ingest data from hundreds of sources, including data lakes and warehouses. Integrated into the Salesforce Platform, this metadata seamlessly flows into Salesforce applications, governed by set policies. This gives every team a 360-degree customer view, driving AI, automation, and analytics without compromising security. Data Cloud also enhances Agentforce, providing contextual insights and guiding next-best steps, like automating follow-up emails or sharing detailed chat summaries with human agents. Tableau Semantics bridges the gap between AI and employees, ensuring they operate with consistent data definitions. The following new Data Cloud features will be available across various release stages: For more information, visit www.salesforce.com. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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 1, 2024in Data, Enterprise, Mulesoft, Salesforce, Salesforce Data Cloud, Technology
Bye Klarna
20Sep

Bye Klarna

Fintech firm Klarna is cutting ties with two major enterprise software providers, opting to automate its services using AI, and hints that more cuts could follow. Klarna co-founder and CEO Sebastian Siemiatkowski discussed the move during a recent conference call, as reported by Seeking Alpha. The company has already stopped using Salesforce, a platform that helps businesses manage sales and marketing data, and has also removed Workday, an HR and hiring platform, from its tech stack, according to a spokesperson from Klarna. This shift towards AI-driven automation is part of a larger strategy at Klarna. “We have multiple large-scale initiatives combining AI, standardization, and simplification that will allow us to eliminate several SaaS providers,” a company spokesperson said, though they did not specify which providers or services might be next. Founded in 2005, Klarna provides payment processing for e-commerce and reports over 150 million active users worldwide. Despite posting a net loss of $241 million last year—down from nearly $1 billion in 2022—the company reported a reduced loss of $32 million for the first half of 2024. With reports suggesting that Goldman Sachs has been tapped to underwrite Klarna’s potential IPO, the company’s focus on AI could strengthen its profitability prospects. This isn’t Klarna’s first AI initiative. Earlier in 2024, the company introduced an AI-powered customer service assistant in collaboration with OpenAI, which reportedly handled 2.3 million interactions in its first month and replaced the work of 700 agents. Klarna was among the early adopters of OpenAI’s enterprise ChatGPT package, and the company claims that 90% of its employees use the tool daily for process automation. Klarna’s decision to drop Salesforce and Workday is part of a broader effort to replace third-party SaaS solutions with internally developed applications, likely built on OpenAI’s infrastructure. Siemiatkowski stated in the August call, “We are shutting down a lot of our SaaS providers as we are able to consolidate.” However, not everyone is convinced. HR technology analyst Josh Bersin questioned whether Klarna could successfully replace a robust platform like Workday. “Workday systems have decades of workflows and complex data structures, including payroll and attendance,” he told Inc.. Bersin warned that developing an in-house system could lead Klarna into a “black hole of features,” with a poor user experience as a result. Many in the tech world share Bersin’s skepticism, with some suggesting the move is more of a PR tactic as Klarna gears up for its IPO. Investors and executives voiced doubts on social media, with financial insights account BuccoCapital posting on X, “Is it actually the best use of capital to rebuild in-house? Feels like a massive distraction,” while Ryan Jones, CEO of Flighty, called the move “free marketing.” Critics also point out that Klarna has downsized its workforce significantly, reducing its headcount by 1,200 over the past year, and Siemiatkowski has hinted at further reductions, suggesting the company could benefit from cutting staff from 3,800 to 2,000 employees. Siemiatkowski remains adamant that AI will allow Klarna to maintain growth despite these cuts. Bersin also noted that many tech giants have struggled to build their own HR platforms, citing examples like Google, which recently abandoned its internally developed HR software, and Amazon, which undergoes similar cycles regularly. “Microsoft,” Bersin added, “spends money on their own products but partners with SAP for HR software.” If Klarna does succeed in developing an in-house HR platform, it would be an achievement where even some of the biggest tech companies have fallen short. 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 20, 2024in Salesforce
Collaborative Business Intelligence
20Sep

Collaborative Business Intelligence

Collaborative Business Intelligence: Connecting Data and Teams In today’s data-driven world, the ability to interact with business intelligence (BI) tools is essential for making informed decisions. Collaborative business intelligence (BI), also known as social BI, allows users to engage with their organization’s data and communicate with data experts through the same platforms where they already collaborate. While self-service BI empowers users to generate insights, understanding the data’s context is critical to avoid misunderstandings that can derail decision-making. Collaborative BI integrates BI tools with collaboration platforms to bridge the gap between data analysis and communication, reducing the risks of misinterpretation. Traditional Business Intelligence Traditional BI involves the use of technology to analyze data and present insights clearly. Before BI platforms became widespread, data scientists and statisticians handled data analysis, making it challenging for non-technical professionals to digest the insights. BI evolved to automate visualizations, such as charts and dashboards, making data more accessible to business users. Previously, BI reports were typically available only to high-level executives. However, modern self-service BI tools democratize access, enabling more users—regardless of technical expertise—to create reports and visualize data, fostering better decision-making across the organization. The Emergence of Collaborative BI Collaborative BI is a growing trend, combining BI applications with collaboration tools. This approach allows users to work together synchronously or asynchronously within a shared platform, making it easier to discuss data reports in real time or leave comments for others to review. Whether it’s through Slack, Microsoft Teams, or social media apps, users can receive and discuss BI insights within their usual communication channels. This seamless integration of BI and collaboration tools offers a competitive edge, simplifying the process of sharing knowledge and clarifying data without switching between applications. Key Benefits of Collaborative Business Intelligence Leading Collaborative BI Platforms Here’s a look at some of the top collaborative BI platforms driving innovation in the market: Conclusion Collaborative BI empowers organizations by improving decision-making, democratizing data access, optimizing data quality, and ensuring data security. By integrating BI tools with collaboration platforms, businesses can streamline their operations, foster a culture of data-driven decision-making, and enhance overall efficiency. Choosing the right platform is key to maximizing the benefits of collaborative BI. 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 20, 2024in Data, Google, Salesforce, Technology
Enhancing Customer Experiences with Social Insights
16Sep

Enhancing Customer Experiences with Social Insights

Sprout Social Announces Integration with Salesforce Agentforce for Service Cloud Sprout Social (SPT) has unveiled a new integration with Salesforce’s (CRM) Agentforce assistant for Service Cloud. This partnership enables joint customers of Salesforce Service Cloud and Sprout Social to gather customer insights from social media and take proactive action to enhance customer experiences. These updates will be highlighted at Dreamforce 2024, where Sprout Social will showcase how AI, social data, and Salesforce can drive revenue, foster customer loyalty, and strengthen brand equity. As a leading provider of cloud-based social media management software, Sprout Social’s integration with Salesforce’s Agentforce assistant empowers businesses to capture social media insights and accelerate decision-making in customer service. These advancements will be a focal point at Dreamforce 2024, where Sprout Social will present on utilizing AI and social data to enhance customer care, build brand loyalty, and grow revenue. Enhancing Customer Experiences with Social Insights Delivering exceptional customer care requires meeting customers where they are—often on social media. This new integration equips businesses with a full view of their customers by combining social data with Agentforce’s AI-powered assistance. By leveraging this combined solution, companies can resolve cases more efficiently and anticipate customer needs, shifting from reactive to proactive customer service strategies. “This extension demonstrates our commitment to AI-driven innovation and our strong partnership with Salesforce,” said Scott Morris, Chief Marketing Officer of Sprout Social. “In today’s landscape, where customer care defines brand success, integrating social insights is crucial for supporting holistic, long-term care strategies and empowering brands to deliver personalized interactions at scale.” The integration expands Agentforce’s capabilities to include social data from major networks and review sites, alongside traditional communication channels like phone and email. This comprehensive view enables service teams to provide a seamless and personalized customer experience. A New Era of AI-Driven Customer Service “Social customer care has become integral to providing quality, end-to-end customer experiences,” said Ryan Nichols, Chief Customer Officer at Salesforce Service Cloud. “Collaborating with Sprout to integrate their platform with Agentforce assistant is a key step in enabling service teams to gain comprehensive customer insights and leverage conversational AI to elevate customer care.” At Dreamforce 2024, Sprout Social will present key sessions, including: The integration, currently in invite-only beta, enhances Agentforce with social data-driven insights, making customer service faster and more proactive. To learn more, contact your Sprout Social representative or visit Sprout Social’s Service Cloud page. Key Features of the Integration: Sprout Social will highlight these features at Dreamforce 2024, with insights on leveraging AI, social data, and Salesforce for enhanced customer care and brand growth. 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 16, 2024in Salesforce Service Cloud
Data Cloud and Genpact
16Sep

6 Work Easing Google AI Extensions

Time is often equated with money, and that couldn’t be more accurate. These Work Easing Google AI Extensions will give you back some time. Time is an invaluable asset, essential for managing both work and life. Many find themselves overwhelmed with tasks, constantly battling stress and exhaustion due to a lack of time. This was the case until AI-powered Chrome extensions came along, revolutionizing the way work is approached. Time concious workers have discovered the transformative power of these digital tools. They not only improved their careers but also completely changed how each manages there workload. Now, Tectonic’s sharing these invaluable resources, which have significantly boosted her productivity and time management. Here are some of the AI Chrome extensions we recommend: 1. Glasp AI Extension Glasp AI Chrome Extension Glasp AI is a robust tool that aids in capturing, organizing, and sharing knowledge from the web. Features: Benefits: 2. Merlin AI Merlin AI Chrome Extension Merlin AI uses advanced technology to assist with various tasks, helping users work faster and more efficiently. What it does: Why it’s helpful: 3. Just Done AI Extension Just Done AI Just Done AI is a great tool for detecting AI content. Features: Benefits: 4. Perplexity AI Perplexity AI Chrome Extension Perplexity AI is designed to elevate the browsing experience by providing quick access to AI-driven information and assistance. Why it’s useful: Benefits: 5. SciSpace SciSpace Chrome Extension SciSpace simplifies the understanding of scientific papers or reports by acting as a virtual assistant within the web browser. Just imagine having a coach explaining the technical concepts. Features: 6. WebChatGPT WebChatGPT Chrome Extension WebChatGPT enhances ChatGPT by allowing it to access current information from the internet, making the tool even more effective, by connecting it to up-to-date data. What it does: Why it’s beneficial: Work Easing Google AI Extensions These AI Chrome extensions have dramatically improved time management and productivity. They offer similar potential for anyone looking to optimize their workflow and make the most of their work time. 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 16, 2024in Salesforce

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