Generative AI - gettectonic.com - Page 16
Consumer Chatbot Technology
07Jun

Consumer Chatbot Technology

The Reality Behind AI Chatbots and the Path to Autonomous AI In the rush to adopt the latest Consumer Chatbot Technology, it’s easy to overlook a fundamental reality: consumer chatbot technology isn’t ready for enterprise use—and it likely never will be. The reason is simple: AI assistants are only as effective as the data that powers them. Most large language models (LLMs) are trained on data from public websites, which lack the specific business and customer data that enterprises need. This means consumer bots can’t adequately assist employees in selling products, marketing merchandise, or improving productivity, as they lack the necessary personalization and business context. To achieve the vision of AI that goes beyond simple chatbots performing basic tasks—like drafting emails, essays, blogs, or graphics—to a more advanced role where AI acts autonomously and addresses business-critical needs, a different approach is needed. This vision involves AI taking action with minimal human intervention, using digital agents to identify and respond to these needs. At Salesforce, we are pursuing a clear path to AI that not only takes action but also automates routine tasks, all while adhering to established business rules, permissions, and context. Instead of relying solely on LLMs, which primarily focus on generating human-like text, future AI assistants will depend on large action models (LAMs) that integrate decision-making and action-taking capabilities. The Journey Toward AI Autonomy Our journey towards this vision began with the Salesforce Data Cloud, a robust data engine built on the Einstein 1 Platform. This platform integrates data from across the enterprise and third-party repositories, enabling companies to activate their data, automate workflows, personalize customer interactions, and develop smarter AI solutions. Recognizing the shift from generative AI to autonomous AI, Salesforce introduced Einstein Copilot, the industry’s first conversational, enterprise-class AI assistant. Integrated across the Salesforce ecosystem, Einstein Copilot utilizes an organization’s data, whether it’s behind a firewall or in an external data lake, to act as a reasoning engine. It interprets user intents, interacts with the most suitable AI model, solves problems, generates relevant content, and provides decision-making support. Expanding the Role of AI in Business Since its launch in February 2024, Salesforce has been expanding Einstein Copilot’s library of actions to meet specific business needs in sales, service, marketing, data analysis, and industries like ecommerce, financial services, healthcare, and education. These “actions” are akin to LEGO blocks—discrete tasks that can be assembled to achieve desired project outcomes. For example, a sales representative might use Einstein Copilot to generate a personalized close plan, gain insights into why a deal may not close, or review whether pricing was discussed in a recent call. Einstein Copilot then orchestrates these tasks, provides recommendations, and compiles everything into a detailed report. The ultimate goal is for AI not only to gather and organize information but also to take proactive action. Imagine a sales representative instructing their digital agent to set up meetings with top prospects in a specific territory. The AI could not only identify suitable contacts but also suggest meeting times, plan travel schedules, draft emails, and even create talking points—all of which it could execute autonomously with the representative’s approval. Tectonic dreams of the day AI is smart enough to interpret our search engine typos and produce the results for what we were actually looking for! The Future of AI Autonomy The possibilities for semi-autonomous or fully autonomous AI are vast. As we continue to develop and refine these technologies, the potential for AI to transform business processes and decision-making becomes increasingly tangible. At Salesforce, they are committed to leading this charge, ensuring that our AI solutions not only meet but exceed the expectations of enterprises worldwide. Salesforce is in a strong position to deliver on all of them because of the volume and breadth of data housed in Data Cloud, the heavy workflow traffic in our Customer 360 CRM, and the fact we’ve delivered an enterprise-class copilot that is rapidly expanding its library of actions. It will not happen overnight. The technology needs to advance, organizations and people have to be able to trust AI and be trained to use it in the right ways, and more work will need to be done to ensure the right balance between human involvement and AI autonomy. But with our continued investment in CRM, data, and trusted AI, we will achieve that vision before too long. Salesforce is in a strong position to deliver on all of them because of the volume and breadth of data housed in Data Cloud, the heavy workflow traffic in our Customer 360 CRM, and the fact we’ve delivered an enterprise-class copilot that is rapidly expanding its library of actions. Jayesh Govindarajan, Senior Vice President, Salesforce 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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June 7, 2024in Einstein 1 Platform, Generative AI, Salesforce Data Cloud
Salesforce Announces Slack Lists for Project Management
06Jun

Salesforce Announces Slack Lists for Project Management

Salesforce Transforms Slack into Project Management Platform with New Slack Lists Feature Salesforce Inc. has introduced Slack Lists, a new feature designed to transform Slack from a simple collaboration tool into a comprehensive project management platform. Initially rolling out in select regions, Slack Lists will soon be available to all paid Slack users, integrating project and task management directly into the Slack interface. Salesforce Announces Slack Lists for Project Management. And this answers the age old question, can Salesforce be used as a project management tool? Key Benefits and Features Collaborative Tools Slack Lists excels in fostering collaboration by allowing team members to bring relevant coworkers into conversations and create message threads for specific list items. These threads consolidate discussions about particular items, requests, or issues into single, organized conversations. This ensures tasks are not forgotten and keeps all team members informed about project progress. Executive Insights Denise Dresser, CEO of Slack, stated that Slack Lists transforms conversations into actionable tasks that drive work forward. “Now those loose steps shared in a project channel can be tracked across teams,” she explained. “With project management in Slack, teams across organizations will have everything they need to complete projects faster and increase productivity.” Practical Applications Salesforce emphasized that Slack Lists can benefit any organization within an enterprise. For example: Holger Mueller of Constellation Research Inc. noted that Salesforce has long aimed to extend Slack’s capabilities beyond core messaging and collaboration. “Salesforce has opted to go generic with Slack Lists, allowing users to create list entries for projects, service requests, and more,” he said. “This ideally makes collaboration on those projects easier, making Slack more goal-oriented and changing the future of work.” Early Adoption Marriott International Inc. participated in the pilot phase of Slack Lists before its general release. Lori Drake, Director of System Strategy at Marriott Digital Services, expressed enthusiasm about the feature, saying, “We’re excited to use lists to manage projects and track requests, and think it can unlock significant time savings for our teams.” Availability 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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June 6, 2024in Generative AI, Salesforce, Salesforce Slack
03Jun

An Eye on AI

Humans often cast uneasy glances over their shoulders as artificial intelligence (AI) rapidly advances, achieving feats once exclusive to human intellect. An Eye on AI should ease their troubled minds. AI-driven chatbots can now pass rigorous exams like the bar and medical licensing tests, generate tailored images and summaries from complex texts, and simulate human-like interactions. Yet, amidst these advancements, concerns loom large — fears of widespread job loss, existential threats to humanity, and the specter of machines surpassing human control to safeguard their own existence. Skeptics of these doomsday scenarios argue that today’s AI lacks true cognition. They assert that AI, including sophisticated chatbots, operates on predictive algorithms that generate responses based on patterns in data inputs rather than genuine understanding. Even as AI capabilities evolve, it remains tethered to processing inputs into outputs without cognitive reasoning akin to human thought processes. So, are we venturing into perilous territory or merely witnessing incremental advancements in technology? Perhaps both. While the prospect of creating a malevolent AI akin to HAL 9000 from “2001: A Space Odyssey” seems far-fetched, there is a prudent assumption that human ingenuity, prioritizing survival, would prevent engineering our own demise through AI. Yet, the existential question remains — are we sufficiently safeguarded against ourselves? Doubts about AI’s true cognitive abilities persist despite its impressive functionalities. While AI models like large language models (LLMs) operate on vast amounts of data to simulate human reasoning and context awareness, they fundamentally lack consciousness. AI’s creativity, exemplified by its ability to invent new ideas or solve complex problems, remains a simulated mimicry rather than authentic intelligence. Moreover, AI’s domain-specific capabilities are constrained by its training data and programming limitations, unlike human cognition which adapts dynamically to diverse and novel situations. AI excels in pattern recognition tasks, from diagnosing diseases to classifying images, yet it does so without comprehending the underlying concepts or contexts. For instance, in medical diagnostics or art authentication, AI can achieve remarkable accuracy in identifying patterns but lacks the interpretative skills and contextual understanding that humans possess. This limitation underscores the necessity for human oversight and critical judgment in areas where AI’s decisions impact significant outcomes. The evolution of AI, rooted in neural network technologies and deep learning paradigms, marks a profound shift in how we approach complex tasks traditionally performed by human experts. However, AI’s reliance on data patterns and algorithms highlights its inherent limitations in achieving genuine cognitive understanding or autonomous decision-making. In conclusion, while AI continues to transform industries and enhance productivity, its capabilities are rooted in computational algorithms rather than conscious reasoning. As we navigate the future of AI integration, maintaining a balance between leveraging its efficiencies and preserving human expertise and oversight remains paramount. Ultimately, the intersection of AI and human intelligence will define the boundaries of technological advancement and ethical responsibility in the years 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 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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June 3, 2024in Generative AI
Einstein Personalization and Copilots
30May

Einstein Personalization and Copilots

Salesforce launched a suite of new generative AI products at Connections in Chicago, including new Einstein Copilots for marketers and merchants, and Einstein Personalization. Einstein Personalization and Copilots To gain insights into these products and Salesforce’s evolving architecture, Bobby Jania, CMO of Marketing Cloud was interviewed. Salesforce’s Evolving Architecture Salesforce has a knack for introducing new names for its platforms and products, sometimes causing confusion about whether something is entirely new or simply rebranded. Reporters sought clarification on the Einstein 1 platform and its relationship to Salesforce Data Cloud. “Data Cloud is built on the Einstein 1 platform,” Jania explained. “Einstein 1 encompasses the entire Salesforce platform, including products like Sales Cloud and Service Cloud, continuing the original multi-tenant cloud concept.” Data Cloud, developed natively on Einstein 1, was the first product built on Hyperforce, Salesforce’s new cloud infrastructure. “From the start, Data Cloud has been able to connect to and read anything within Sales Cloud, Service Cloud, etc. Additionally, it can now handle both structured and unstructured data.” This marks significant progress from a few years ago when Salesforce’s platform comprised various acquisitions (like ExactTarget) that didn’t seamlessly integrate. Previously, data had to be moved between products, often resulting in duplicates. Now, Data Cloud serves as the central repository, with applications like Tableau, Commerce Cloud, Service Cloud, and Marketing Cloud all accessing the same operational customer profile without duplicating data. Salesforce customers can also import their own datasets into Data Cloud. “We wanted a federated data model,” Jania said. “If you’re using Snowflake, for example, we virtually sit on your data lake, providing value by forming comprehensive operational customer profiles.” Understanding Einstein Copilot “Copilot means having an assistant within the tool you’re using, contextually aware of your tasks and assisting you at every step,” Jania said. For marketers, this could start with a campaign brief created with Copilot’s help, identifying an audience, and developing content. “Einstein Studio is exciting because customers can create actions for Copilot that we hadn’t even envisioned.” Contrary to previous reports, there is only one Copilot, Einstein Copilot, with various use cases like marketing, merchants, and shoppers. “We use these names for clarity, but there’s just one Copilot. You can build your own use cases in addition to the ones we provide.” Marketers will need time to adapt to Copilot. “Adoption takes time,” Jania acknowledged. “This Connections event offers extensive hands-on training to help people use Data Cloud and these tools, beyond just demonstrations.” What’s New with Einstein Personalization Einstein Personalization is a real-time decision engine designed to choose the next best action or offer for customers. “What’s new is that it now runs natively on Data Cloud,” Jania explained. While many decision engines require a separate dataset, Einstein Personalization evaluates a customer holistically and recommends actions directly within Service Cloud, Sales Cloud, or Marketing Cloud. Ensuring Trust Connections presentations emphasized that while public LLMs like ChatGPT can be applied to customer data, none of this data is retained by the LLMs. This isn’t just a matter of agreements; it involves the Einstein Trust Layer. “All data passing through an LLM runs through our gateway. Personally identifiable information, such as credit card numbers or email addresses, is stripped out. The LLMs do not store the output; Salesforce retains it for auditing. Any output that returns through our gateway is logged, checked for toxicity, and only then is PII reinserted into the response. These measures ensure data safety beyond mere handshakes,” Jania said. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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May 30, 2024in Einstein 1 Platform, Generative AI, Hyperforce, Salesforce, Salesforce Data Cloud, Salesforce Einstein, Snowflake
Salesforce Unlimited+ Edition Explained
30May

Salesforce Unlimited+ Edition Explained

Salesforce Unlimited Plus (UE+) is designed as an advanced offering that incorporates several specialized features tailored for different industries, making it particularly suitable for larger organizations and enterprises that require robust, integrated solutions for complex business processes and customer relationship management. Salesforce Unlimited+ Edition Explained. Target Audience UE+ is targeted toward large enterprises that need extensive CRM functionalities combined with AI and data analytics capabilities. This solution is ideal for organizations that: • Manage complex customer relationships across multiple channels. • Require deep integration of data and processes across departments. • Are looking to leverage advanced AI capabilities for predictive insights and automation. • Need industry-specific solutions that can be customized for unique business requirements. The integration of various Salesforce clouds (e.g., Sales Cloud, Service Cloud, Data Cloud) with enhanced features like AI and specific industry capabilities makes UE+ a comprehensive solution for organizations aiming to streamline their operations and gain a competitive edge through advanced technology adoption. Here are the five Salesforce editions for every purpose: ·Starter/Essentials: Ideal for small businesses, offering basic contact, lead, and opportunity management. ·Professional: Tailored for mid-sized companies with enhanced sales forecasting and automation capabilities. ·Enterprise: Geared towards larger organizations, providing advanced customization, reporting, and integration options. ·Unlimited: Offers comprehensive functionality, customizability, 24/7 support, and access to premium features like generative AI. ·Unlimited Plus: Most robust solution for businesses of all sizes, featuring additional functionalities and enhanced capabilities. Key Considerations: ·Business Size: Consider the number of users and overall business scale when choosing an edition. ·Features Needed: Identify the specific features crucial for your sales, service, or marketing processes. ·Scalability: Choose an edition that accommodates your projected growth and future needs. ·Budget: Evaluate the cost of each edition against its offered features and value proposition. Sales Cloud Unlimited Edition+ Features: Account and Contact Management: Complete visibility of customer profiles including activity history and communications. Opportunity Management: Tracking and details of every sales deal at each stage. Pipeline Inspection: A comprehensive tool that allows sales managers to monitor pipeline changes, offering AI-driven insights and recommendations to optimize sales strategies. Einstein AI Capabilities: Includes tools like Einstein Conversation Insights which transcribe and analyze sales calls, highlighting key parts for review and deeper analysis. Customizable Reports and Dashboards: Enhanced capabilities for building real-time reports and visualizations to track sales metrics and forecasts. Advanced Integration Features: Integration with external data and systems through various APIs including REST and SOAP. Automation and Customization: Extensive options for workflow automation and personalization of user interfaces and customer interactions using the Flow Builder and Lightning App Builder. Developer Tools: Access to tools like Developer Sandbox for safe testing and app development environments. Service Cloud Unlimited+ Features: Einstein Bots: AI-powered chatbots to handle customer inquiries automatically, available 24/7 across various communication channels. Enhanced Messaging: Integration with popular messaging platforms like WhatsApp, SMS, and Apple Messages to facilitate seamless customer interactions. Feedback Management: Tools to gather and analyze customer feedback directly within the CRM. Self-Service Capabilities: Including customizable help centers and service catalogs that allow customers to find information and resolve issues independently. Field Service Tools: Comprehensive management of field operations including work order and asset management. Real-Time Analytics: Advanced reporting features for creating in-depth analytics to monitor and improve customer service processes. Additional features include Data Cloud, Generative AI, Service Cloud Voice, Digital Engagement, Feedback Management, Self-Service, and Slack. Salesforce Unlimited+ for Industries UE+ for Industries: UE+ for Industries includes Unlimited+ for Sales and Service together with industry-specific data models and capabilities to help customers drive faster time to value within their sectors: •Financial Services Cloud UE+ for Sales and Financial Services Cloud UE+ for Service helps banks, asset management, and insurance agencies connect all of their customer data on one platform and embed AI to deliver personalized financial engagement, at scale. •An insurance carrier can use Financial Services Cloud UE+ to connect engagement data like emails, webinars, and educational content with third-party conference attendance, social media follows, and business performance data to understand what is motivating agents, helping drive more personalized relationships and grow revenue with Data Cloud and Einstein AI. •Health Cloud UE+ for Service helps healthcare, pharmaceutical, and other medical organizations improve response times at their contact centers and offer digital healthcare services with built-in intelligence, real-time collaboration, and a 360-degree view of every patient, provider, and partner. •A hospital can use the bundle to quickly create a personalized, AI-powered support center to triage and speed up time to care with self-service tools like scheduling and connecting patients and members with care teams on their preferred channels. •Manufacturing Cloud UE+ for Sales brings together tools for manufacturing organizations to build their data foundation, embed AI capabilities across the sales cycle, and maximize productivity, empowering them to scale their commercial operations and grow revenues. •A manufacturer can now look across the entire book of business to see how companies are performing against negotiated sales agreements and then use AI-generated summaries to determine where to prioritize their time and resources. By Tectonic’s AArchitecture Team Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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May 30, 2024in Generative AI, Salesforce
Did Google Dethrone ChatGPT
30May

Did Google Dethrone ChatGPT?

Google’s Bard has emerged as a contender in the realm of large language models (LLMs), sparking speculation about its potential to outshine OpenAI’s ChatGPT. This insight explores the validity of this claim and examines the tests and factors that could determine the ultimate victor in this ongoing AI rivalry. Did Google Dethrone ChatGPT? Google’s Gemini 1.5 Pro is a generational leap in terms of Multimodal Large Language Models, or MLLMs, much like GPT-4 was to LLMs back in March 2023. Did Google Dethrone ChatGPT? While initial rumors of Bard’s “dethronement” of ChatGPT surfaced from a single LinkedIn post in February 2024, substantial evidence is required to substantiate such claims. Let’s determine the potential battleground: The Testing Grounds: There’s no singular, universally recognized benchmark for evaluating LLMs. Here are some areas where Google and OpenAI may showcase their AI prowess: Generative Text Quality: Can the LLM generate various creative text formats—such as poems, code, scripts, and emails—while maintaining coherence and factual accuracy? Question Answering: How effectively can the LLM respond to open-ended, challenging, or unconventional questions, drawing on its knowledge base? Following Instructions: Can the LLM adhere to complex instructions and perform tasks requiring multi-step reasoning? Bias Mitigation: Does the LLM demonstrate impartiality in its responses, or does it exhibit traces of prejudice or social stereotypes? Beyond the Tests: While test results offer insights into LLM capabilities, other factors influence their overall impact: Accessibility: How easily can the LLM be accessed by the public? Is there a user-friendly interface or developer API? Real-World Applications: How seamlessly can the LLM be integrated into practical applications like chatbots, virtual assistants, or educational tools? Continuous Learning: How adeptly does the LLM adapt and enhance its performance over time, incorporating new data and user feedback? The Current Landscape: Declaring a definitive winner is challenging. Bard and ChatGPT excel in different domains. Here’s a speculative analysis: Generative Text Quality: Bard may have a slight advantage, leveraging Google’s extensive dataset. Question Answering: ChatGPT might excel in responding to open-ended queries with creativity, while Bard may prioritize factual accuracy. Following Instructions & Bias Mitigation: Both LLMs are continually refining their capabilities in these areas. The Future of LLMs: The landscape of LLMs is dynamic, with Google and OpenAI poised to make significant advancements. Anticipated developments include: Focus on Explainability: Efforts to understand the reasoning behind LLM responses to foster transparency and trust. Bias Mitigation: Strategies to address bias in LLMs for fairer and more inclusive interactions. Specialized LLMs: Development of domain-specific LLMs tailored to fields like medicine or law. Is Google AI better than ChatGPT? Gemini offers a better user experience, with more imagery and website links. Gemini Advanced generates better AI images than ChatGPT Plus. Gemini responses were often set out in a more readable format than ChatGPT’s responses. Gemini was better at generating spreadsheet formulas than ChatGPT. How is Bard better than ChatGPT? Bard has real-time access to the internet through Google Search, allowing it to incorporate the latest information and news into its responses. Trained on a static dataset not updated since 2021, however, ChatGPT can only access external information through plugins, and this functionality is limited. Is Google nervous about ChatGPT? It’s that the technology represents everything Google was afraid artificial intelligence would become. If ChatGPT runs rampant, the search giant fears it could ruin AI adoption for everyone. Since going viral, ChatGPT has demonstrated how generative AI can be user-friendly, practical, and productive. The narrative of ChatGPT’s dethronement may be premature. Bard and ChatGPT are evolving entities, and the ultimate victor will be determined by their ability to navigate future challenges and opportunities. As these LLMs progress, users stand to benefit from access to increasingly sophisticated and beneficial AI tools. 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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May 30, 2024in Generative AI, Salesforce Einstein
AI in Marketing
29May

AI in Marketing

John Dutton recently posted in his blog about AI “representatives” who talk to you. It’s an interesting look into the “creep” factor potentially in artificial intelligence and certainly provides plenty of food for thought on robots and AI in marketing. Read it here. Summarized below. When the media or the internet shares a look at this wierd generated image talking, its easy to spot. When not flagged, it is getting a little harder to know for sure-is it real or is it Memorex. Unveiling AI in Ukraine Last week, Ukraine’s Ministry of Foreign Affairs introduced Victoriya Shi, a “digital representative” and AI-produced avatar. Shi delivers official statements in videos shared on the Ministry’s online social channels. According to Ukrainian Foreign Minister Dmytro Kuleba, Shi was created to “save time and resources” for diplomats. Given the ongoing conflict in Ukraine, this rationale seems reasonable. However, the introduction of such an AI avatar raises questions about the future and the potential for dystopian developments. A key concern is the ease of deepfaking an already artificial persona. This challenge has been addressed by the MFA through a smart yet simple solution: a QR code in the corner of each video that directs viewers to the official text version of the announcement on the Ministry’s website. It’s worth noting that the official statements themselves are not AI-generated, which could set a worrying precedent. While the Ukrainian version’s reception is unknown, the English version of Victoriya Shi struggles to escape the “uncanny valley” of artificial humans. Her sign-off, “I look forward to our fruitful cooperation,” has an eerie, robotic undertone. This unsettling impression might not be entirely negative. Navigating the Age of AI We are deeply entrenched in the Age of AI, where trust has become a scarce commodity. The concept of “fake news” emerged well before generative AI, gaining prominence in late 2016 with the rise of certain political figures. A search on Google Trends reveals the sudden spike in terms like “fake news” and “post-truth” during that period. With AI’s potential to create convincing deepfakes, the challenge of distinguishing real from fake is intensifying. A recent incident in Hong Kong saw an employee deceived by an AI-generated video, leading to a $25 million fraud. This highlights the need for secure credentialing, especially in large organizations and potential metaverse meetings. However, in-person meetings remain immune to such digital deceptions. AI’s Role in Authenticity Ironically, AI might help combat its own deceptions. OpenAI’s recent collaboration with the Coalition for Content Provenance and Authenticity (C2PA) aims to develop tools for identifying AI-generated content. As deepfakes become more sophisticated, the absence of C2PA authentication could become a red flag. If this leads to a heightened skepticism towards digital media, it might not be entirely negative. AI could bolster our defenses against scams, encouraging a healthy suspicion of the digital content we consume. The Balance of Authenticity and Truth The distinction between authenticity and truth is crucial. A government-created AI avatar can be fake in its artificiality but still deliver authentic, official statements. As generative AI advances, we must fine-tune our skepticism. Victoriya Shi’s name reflects Ukraine’s hope for “victory” and the integration of AI (“Shi” in Ukrainian). The war may ultimately hinge on intelligent tech use rather than sheer military might. Update and Reflections Following the newsletter’s release, it was revealed that WPP, the world’s largest ad agency network, nearly fell victim to a deepfake scam, with the CEO’s voice being replicated by AI. The Dystopia/Utopia Dichotomy The generative AI revolution has begun, and its trajectory could lead to either a utopian or dystopian future. My novel, “2084,” explores a world where life appears superficially perfect, masking underlying issues. Artistic AI Innovations One of my book’s main characters is a sculptor, a profession I initially believed immune to AI. However, Monumental Labs, founded in 2022, uses “sensors and AI” to produce sculptures at a fraction of traditional costs. This reality mirrors the AI-driven world imagined in “2084.” Genetic Modifications and Luxury Fresh Del Monte’s Rubyglow® pineapple, an ultra-premium, genetically modified fruit, exemplifies the future of designer foods. My novel envisions similar advancements with patented food items and drone-pollinated plants. The Challenger Mindset Adam Morgan, an expert in the challenger brand mindset, emphasizes the importance of maintaining a challenger attitude regardless of market position. Companies like Netflix exemplify this, adapting and thriving in a competitive landscape by retaining a challenger’s drive. The Right to Repair and Brand Identity The US Government Accountability Office highlights the “softwareification” of cars, making independent repairs difficult. Similarly, Apple’s restrictive policies on iPhone repairs underline the broader trend of manufacturers controlling repair markets. Cult of Brand Identity The Gray Area podcast discusses how modern consumers interact with brands, focusing on identity over product quality. This shift underscores the evolving landscape of commercial competition and consumer behavior. 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 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 Guide to Creating a Working Sales Plan Creating a sales plan is a pivotal step in reaching your revenue objectives. To ensure its longevity and adaptability to 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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May 29, 2024in Generative AI, Salesforce Einstein
Patterns for AI Security
27May

Patterns for AI Security

Supporting the development of AI design patterns that demonstrate trustworthiness not only enhances user experiences but also serves as an enabling tool for informing more effective policy and compliance measures. Patterns for AI Security. By prototyping patterns, teams can effectively communicate complex policies, illustrating how they could function within industries and for users. This approach also facilitates the testing of patterns, enabling teams to swiftly identify trade-offs and challenge assumptions, thereby accelerating the establishment of industry standards for best practices. Ultimately, this iterative process leads to the creation of better policies and services that yield superior outcomes for both individuals and organizations. Patterns for AI Security For instance, consider the pattern of “watermarking,” mandated by China’s Cyberspace Administration and poised to be adopted by the USA and EU. Through exploration of this pattern, the team at IF highlighted the inherent challenges associated with implementing watermarking for users and businesses. Another design pattern is the AI query router. A user inputs a query, that query is sent to a router, which is a classifier that categorizes the input. A recognized query routes to small language model, which tends to be more accurate, more responsive, & less expensive to operate. If the query is not recognized, a large language model handles it. LLMs much more expensive to operate, but successfully returns answers to a larger variety of queries. In this way, an AI product can balance cost, performance, & user experience. Moreover, investing in trustworthy solutions not only addresses immediate challenges but also positions businesses for long-term success. As reliance on AI systems becomes ubiquitous, the complexities of trust, collaboration, and robustness will only intensify. Stakeholders, both in the private and public sectors, increasingly expect organizations to deliver responsible solutions that prioritize user value without compromising on privacy. This is particularly evident among Gen Z individuals, who demand technology that understands and anticipates their needs while upholding privacy standards. Gen Alpha will be even moreso inclined. Organizations that recognize the significance of trustworthiness and proactively invest in differentiating their products and services accordingly stand to gain a competitive advantage in the evolving landscape. By prioritizing trustworthiness, businesses can not only meet the expectations of their stakeholders but also foster lasting relationships built on transparency, reliability, and integrity. We all anchor to some tried and tested methods, approaches and patterns when building something new. This statement is very true for those in software engineering, however for generative AI and artificial intelligence itself this may not be the case. With emerging technologies such as generative AI we lack well documented patterns to ground our solution’s. Here are a handful of approaches and patterns for generative AI, based on evaluation of countless production implementations of LLM’s in production. The goal of these patterns is to help mitigate and overcome some of the challenges with generative AI implementations such as cost, latency and hallucinations. List of Patterns Like Related Posts 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 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

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May 27, 2024in Generative AI
IBM Salesforce AI Partnership
25May

IBM Salesforce AI Partnership

IBM and Salesforce Expand Partnership to Advance Open, Trusted AI and Data Ecosystems PR Newswire-IBM Salesforce AI Partnership ARMONK, N.Y. and SAN FRANCISCO, May 21, 2024 – IBM (NYSE: IBM) and Salesforce (NYSE: CRM) today announced an expanded strategic partnership designed to bring together IBM’s watsonx AI and Data Platform with the Salesforce Einstein 1 Platform. This collaboration aims to provide greater customer choice and flexibility in AI and data deployment, empowering teams to make data-driven decisions seamlessly. Key Highlights of the Expanded Partnership: 1. Enhanced Large Language Models for Generative AI: The IBM watsonx platform and IBM Granite series models will introduce more large language models (LLMs) to support generative AI use cases across the Salesforce Einstein 1 Platform. This integration will provide pre-built actions and prompts, enhancing AI capabilities for CRM solutions. 2. Zero Copy Data Integration: IBM joins the Salesforce Zero Copy Partner Network to enable secure, zero-copy data integration between IBM watsonx and Salesforce Data Cloud. This integration offers customers a cost-effective way to connect and leverage their data without the need to move it, ensuring seamless data access and actionability. 3. Commitment to Responsible AI: Salesforce has joined the AI Alliance, reinforcing its commitment to developing open, safe, and responsible AI. This collaboration aims to foster transparency and ethics in AI development, aligning with the AI Alliance’s mission to advance beneficial AI innovation. Integration Details: Large Language Model Flexibility: Organizations can deploy generative AI for CRM use cases with LLMs tailored to their business needs. Salesforce’s “Bring Your Own Large Language Model” strategy allows customers to integrate their LLMs with the watsonx.ai platform and Salesforce Einstein 1 Studio. This will provide access to IBM’s Granite series AI models and custom LLMs built on watsonx, enhancing AI-driven customer interactions. Industry-Specific Solutions: Together with IBM Consulting, Salesforce will offer industry-specific prompt templates and copilot actions in Einstein 1 Studio, starting with public sector use cases. These templates are designed to optimize both foundation and Granite models for various industries, including automotive, energy, financial services, and public sector. Bidirectional Data Integration: By joining the Salesforce Zero Copy Partner Network, IBM enables bidirectional data integration with Salesforce Data Cloud. This integration allows customers to access IBM data via watsonx.data within Salesforce Data Cloud, maintaining data security and minimizing risks. This approach simplifies data management, maximizing technology investments and ensuring comprehensive data accessibility for analytics and AI. Industry Impact: Ritika Gunnar, General Manager of Product Management, Data and AI at IBM, emphasized the importance of choosing the right foundation models for AI strategies, stating, “IBM and Salesforce are making it easier for clients to navigate the complex generative AI landscape by helping them select the right LLM for their business needs.” Rahul Auradkar, EVP & GM of United Data Services & Einstein at Salesforce, highlighted the benefits of the partnership, saying, “With bidirectional data integration, companies can harmonize all their data faster, fueling actionable insights that empower teams to make data-driven decisions and deliver integrated experiences across all customer touchpoints.” About IBM: IBM is a global leader in hybrid cloud and AI, providing consulting expertise to clients in over 175 countries. IBM’s innovations in AI, quantum computing, and industry-specific cloud solutions support digital transformations with a commitment to trust, transparency, and inclusivity. For more information, visit www.ibm.com. About Salesforce: Salesforce is the #1 AI CRM, helping companies connect with customers through CRM + AI + Data + Trust on one unified platform, Einstein 1. For more information, visit www.salesforce.com. For additional details on IBM Granite and the partnership with Salesforce, visit IBM Granite. Statements regarding IBM’s and Salesforce’s future directions are subject to change and represent goals and objectives only. 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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May 25, 2024in Einstein 1 Platform, Generative AI, Salesforce Data Cloud
Copilot A Step Up For Merchandising
23May

Copilot A Step Up For Merchandising

A Leap Forward in Merchandising Merchants face the ongoing challenge of enhancing loyalty, conversion rates, and shopper lifetime value. Enter Einstein Copilot, ushering in automation to boost productivity and inject intelligence for an elevated customer experience in unprecedented ways. Salesforce asserts that early AI adopters are saving an average of 6.4 hours per week. Copilot A Step Up For Merchandising. Einstein Copilot empowers merchants to swiftly craft personalized product promotions to attract new customers and target slow-moving stock based on inventory insights. Additionally, it optimizes site traffic with search engine optimization (SEO) content, generates product descriptions, and enhances checkout conversion with AI recommendations tailored to specific objectives. Copilot A Step Up For Merchandising Prior to Einstein Copilot, other generative AI copilot solutions operated as separate applications, disconnected from the workflow, and lacked the ability to securely leverage trusted company data for generating relevant or consistent results from large language models. Einstein Copilot integrates seamlessly within the world’s leading AI CRM and harnesses data from any Salesforce application to deliver more precise AI-powered recommendations and content. Through natural language prompts, Einstein Copilot facilitates a range of tasks, including: Sales: Conducting account research, preparing for meetings, and automatically updating account information in Salesforce. Summarizing highlights, gauging customer sentiment, and extracting next steps from video calls. Searching for specific details in customer calls and auto-generating sales emails to match tone and style while aligning with customer context. Drafting clauses and embedding them directly within customer contracts. Service: Automatically responding to customers with personalized, relevant answers sourced from trusted company knowledge across various channels like email, SMS, live chat, or social media. Empowering service teams to resolve customer issues swiftly using generative answers integrated seamlessly into their workflow. Automating tasks like summarizing support cases and field work orders. Marketing: Generating email copy for marketing campaigns, refining campaign segmentation with Data Cloud, creating website landing pages based on personalized consumer preferences, and auto-populating contact forms with each customer’s unified profile in Salesforce. Generating surveys following online actions to enhance long-term engagement and purchasing. Commerce: Assisting in building high-converting digital storefronts, automating complex tasks like managing multi-product catalog data, crafting product descriptions in multiple languages, generating personalized product promotions, and optimizing SEO metadata for conversion. Customizing and designing storefront components using natural language prompts. Developers: Converting natural language prompts into Apex code, offering suggestions for more effective and accurate code, and proactively scanning for code vulnerabilities within the developer environment. Tableau: Transitioning swiftly from raw data to actionable insights through a conversational interface, enhancing data analyst productivity with a natural language assistant for faster data exploration, building relevant visualizations, automating repetitive tasks, and facilitating efficient data curation. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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May 23, 2024in Einstein 1 Platform, Generative AI, Salesforce
Salesforce Commerce Cloud and Einstein Copilot Capabilities
23May

Salesforce Commerce Cloud and Einstein Copilot Capabilities

Salesforce Enhances Commerce Cloud and Einstein Copilot Salesforce has announced a double whammy of upgrades to its Commerce Cloud and Einstein Copilot solutions, aiming to supercharge customer service and experience offerings for merchants. And yes, they’re pulling out all the stops – think of it as giving your online store a superhero cape and a sidekick with a PhD in customer satisfaction. Salesforce Commerce Cloud and Einstein Copilot Capabilities. Enhancements to Commerce Cloud Commerce Cloud is getting three major innovations designed to help businesses create more sophisticated commerce sites, boost personalization, and drive revenue growth. Salesforce promises to tackle rising customer expectations by providing a seamless, integrated experience across all channels. In other words, they’re turning your website into a mind-reading wizard, minus the beard and wand. But probably wearing a cool purple cape with stars. According to Michael Affronti, GM and SVP of Commerce Cloud, these new features will enable Salesforce’s customers to deliver superior shopping experiences: “Commerce companies are looking to architect high-caliber ecommerce sites that can swiftly adapt to changing customer expectations and continue to foster strong customer relationships. With the combined power of data, AI, and CRM, Commerce Cloud gives brands the choice of the right tool so they can build superior shopping experiences their way.” New Commerce Cloud Capabilities Einstein Copilot Advancements Salesforce is pulling out the big AI guns, leveraging generative AI (GenAI) to enhance Einstein Copilot with new marketing and merchandising capabilities alongside its traditional sales and service functions. It’s like your old assistant got a brain transplant and now has the IQ of Einstein, the charm of James Bond, and the work ethic of a coffee-fueled startup founder. Ariel Kelman, President and CMO of Salesforce, emphasized the importance of these advancements: “Marketing and commerce leaders need a trusted advisor to help them tap into the promise of generative AI. With the Einstein 1 Platform we’re giving organizations the power to unify all of their data on one trusted platform. This is the key to getting results from generative AI that are actually useful in driving your business forward.” Key Features of Einstein 1 for Marketing and Commerce Expanding Partnerships and Enhancing AI and Data Offerings In addition to these product enhancements, Salesforce has expanded its partnership with IBM to improve AI and data offerings. The collaboration aims to merge IBM’s watsonx.ai platform with Salesforce’s Einstein 1 software, providing customers with the ability to make data-driven decisions and access actions directly within their workflows. It’s like pairing up Batman and Superman to fight the evil forces of inefficiency and bad data. The partnership includes bidirectional data integration, flexible large language models (LLMs), prebuilt CRM solutions, and a focus on responsible AI development. IBM will also join Salesforce’s Zero Copy Partner Network, ensuring that data moves as smoothly as butter on hot toast. Salesforce Commerce Cloud and Einstein Copilot Capabilities These enhancements and partnerships underline Salesforce’s commitment to providing innovative solutions that enhance customer experiences and drive business growth, all while making sure your digital commerce experience is smoother than a jazz saxophone solo. 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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May 23, 2024in Einstein 1 Platform, Generative AI, Salesforce, Salesforce Commerce Cloud
Generative AI and Service Cloud
23May

Generative AI and Service Cloud

Salesforce Service Cloud users are set to receive more Einstein 1 generative AI tools in June and October. A key development is the expansion of automated customer conversations across more sales and marketing platforms. Generative AI and Service Cloud family of tools is growing. This insight aims to uncover the numerous use cases of generative AI in the modern contact center. We’ll help you understand how generative AI can fast track your contact center’s efficiency, improve data analysis capabilities, streamline QA and coaching processes, and make customers’ experiences better.  Today, Salesforce launched Unified Conversations for WhatsApp, which automates bot responses to customer inquiries related to targeted marketing messages on the popular messaging app. Additionally, Salesforce plans to extend support to Line, a messaging app popular in Japan, later this year. These services are built on Salesforce’s Einstein 1 generative AI platform. The platform’s bots aggregate structured and unstructured CRM, product, service, and other data through Salesforce Data Cloud to generate personalized responses. These new features enable conversations to be routed to the digital channels where a Salesforce user’s customers are the most active. And to move omnichannel as customers needs change. Salesforce is also introducing a “bring your own channel” connector to support digital channels not natively covered by the platform. Current examples might include TikTok, Discord, and South Korea’s KakaoTalk, according to Ryan Nichols, Chief Product Officer for Salesforce Service Cloud. Generative AI and Service Cloud “It’s about getting data from all your conversations with customers from Service Cloud into Data Cloud and using that to not just deliver excellent customer service, but also grow your business,” Nichols said. Salesforce Einstein Conversation Mining, a Service Cloud feature currently in beta, aggregates conversations across customer channels to surface insights on the topics customers need help with. This aims to turn inbound customer service from a cost center into a revenue center, a goal long pursued at conferences like Dreamforce and ICMI. This massive change drives more than revenue, it drives ROI. Performance metrics such as time-to-answer and hold-time reduction have traditionally pressured agents to minimize call duration to retain their jobs. Now Salesforce is going to help them. While some skeptics question if generative AI can achieve this ambitious goal, Constellation Research analyst Liz Miller suggests it might be possible. Having previously managed a contact center herself, Miller recognizes the transformative potential of generative AI. With the aid of data, bots, and copilot counterparts assisting humans, agents could save time and access the right information to upsell customers during service engagements. Here are some of the ways Generative AI will change customer service forever. 1. Monitor and Ensure Compliance Maintaining compliance is crucial for fostering customer trust, preserving a positive brand image, and avoiding hefty privacy and compliance fines. In a contact center, compliance mistakes can quickly escalate into costly lawsuits and revenue losses. Generative AI allows your compliance team to proactively manage compliance by quickly identifying trends and addressing issues in real time. Instead of waiting for a compliance issue to escalate, you can fine-tune your AI model to provide compliance insights whenever necessary. For instance, you can ask: This approach offers more comprehensive insights than scorecards, which often lack context and accuracy. Generative AI’s analytical capabilities provide actionable insights to improve compliance across your contact center. 2. Get Insights About Your Call Center Performance at a Glance Generative AI language models make it easier than ever to gain insights into your contact center’s performance. Simply ask the model for the information you need. For example, you can inquire about the real-time average handling time (AHT) by asking, “What is the average handling time today?” But that’s just the beginning. With an advanced language model, you can compare metrics across different quarters or generate ideas for coaching plans by asking for each agent‘s strengths and weaknesses and suggestions for improvement. 3. Automate Post-Call Work Generative AI assistants can act as real-time notetakers, summarizing 100% of calls and freeing agents from manual note-taking. This automation makes after-call work effortless, generating comprehensive and compliant notes with a single click. 4. Capture Coachable Moments Easily Incorporating real-world coachable moments into your sessions is essential for tangible performance improvements. Generative AI can identify areas where agents typically struggle without requiring hours of call listening and note-checking. Traditional methods mean compromising on the specificity of coaching due to time constraints, especially when managing large teams. Generative AI solutions, however, enable call center managers to obtain detailed insights about each agent’s performance quickly. This allows for personalized coaching plans that address individual shortcomings efficiently. You can ask: 5. Improve Decision Making With Efficient Root-Cause Analysis Effective decision-making can transform your contact center. However, many managers struggle to identify the root causes of performance issues. Generative AI algorithms can analyze vast amounts of data and customer interactions, uncovering patterns and trends in customer and agent behavior. These insights help pinpoint the issues most impacting performance and customer satisfaction, allowing you to make informed decisions. The process is nearly fully automated, freeing your team from time-consuming data collection tasks. 6. Reduce Manual Work and Focus on Improvement Improving contact center performance requires extensive data, which is resource-intensive to collect manually. Generative AI simplifies this by analyzing customer interactions and providing actionable insights on demand. This saves time and money, allowing you to focus on improvements that deliver a higher ROI. 7. Scale What Works Discovering and scaling best practices is essential for team-wide success. Generative AI and Natural Language Processing (NLP) models can analyze customer interactions to identify effective strategies and coaching opportunities. For example, if a representative handles challenging situations well, AI can generate tips for other team members based on these successful interactions. Generative AI can identify top-performing agents and analyze their calls to extract best practices, providing a more comprehensive approach than focusing on a single agent. Queries you might use include: 8. Generate Agent Scripts Generative AI enables you to draft and fine-tune agent scripts for various customer interactions. Instead of relying

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May 23, 2024in Generative AI, Salesforce Data Cloud, Salesforce Einstein, Salesforce Service Cloud
Salesforce and Google LLMs
22May

Salesforce and Google LLMs

In recent weeks, AI professionals had the privilege of attending groundbreaking hands-on workshops at the headquarters of two Silicon Valley giants, Salesforce and Google. These experiences offered a firsthand look at the contrasting approaches these tech titans are taking to bring enterprise-grade, large language model (LLM) applications to scale. As they immersed themselves in the cutting-edge world of AI development, a sense of excitement and awe washed over them at the unfolding history. Salesforce and Google LLMs. The workshops provided a fascinating glimpse into the future of enterprise software, where AI is not just a buzzword but a transformative force reshaping how businesses operate. Salesforce and Google, each with their unique strengths and philosophies, are at the forefront of this revolution, pushing the boundaries of what’s possible with LLMs and retrieval-augmented generation (RAG). As they navigated through the hands-on exercises and engaged with the brilliant minds behind these innovations, they realized they were witnessing a pivotal moment in Silicon Valley and computer history. Salesforce LLM Salesforce: Low-Code, Business User-Friendly At the “Build the Future with AI and Data Workshop” held at Salesforce Tower in downtown San Francisco, the focus was on empowering business users with a low-code, clicks-not-code approach. The workshop, attended by around 100 people, took place in a ballroom-sized auditorium. Each attendee received a free instance of the Generative AI-enabled org, pre-populated with a luxury travel destination application, which expired in 5 days. Data Cloud: Lots of Clicks The workshop began with setting up data ingestion and objects for linking AWS S3 buckets to Salesforce’s Data Cloud. The process was intricate, involving a new nomenclature reminiscent of SQL Views within Views, requiring a considerable number of setup steps before accessing Prompt Builder. It should be noted that when using Einstein Studio for the first time, users don’t normally need to do Data Cloud setup. This was done in this workshop so they could later include Data Cloud embeddings in a Prompt Builder retrieval. Prompt Builder: Easy to Use Prompt Builder was the highlight of the workshop. It allows for template variables and various prompt types, including the intriguing Field Prompt, which enables users to attach a prompt to a field. When editing a record, clicking the wizard button in that field executes the prompt, filling out the field automatically. This feature has the potential to greatly enhance data richness, with numerous use cases across industries. Integrating Flow and Apex with Prompt Builder demonstrated the platform’s flexibility. They created an Apex Class using Code Builder, which returned a list that could be used by Prompt Builder to formulate a reply. The seamless integration of these components showcased Salesforce’s commitment to providing a cohesive, user-friendly experience. Einstein Copilot, Salesforce’s AI assistant, exhibited out-of-the-box capabilities when integrated with custom actions. By creating a Flow and integrating it into a custom action, users could invoke Einstein Copilot to assist with various tasks. A Warmly Received Roadmap Salesforce managers, including SVP of Product Management John Kucera, provided insights into the Generative AI roadmap during a briefing session. They emphasized upcoming features such as Recommended Actions, which package prompts into buttons, and improved context understanding for Einstein Copilot. The atmosphere in the room was warm, with genuine excitement and a sense of collaboration between Salesforce staff and attendees. The workshop positioned Salesforce’s AI solution as an alternative to hiring an AI programmer and building AI orchestration using tools like those used in the Google workshop. Salesforce’s approach focuses on a user-friendly interface for setting up data sources and custom actions, enabling users to leverage AI without relying on code. This low-code philosophy aims to democratize AI, making it accessible to a broader range of business users. For organizations already invested in the Salesforce ecosystem, the platform’s embedded AI capabilities offer a compelling way to build expertise and leverage the power of Data Cloud. Salesforce’s commitment to rapidly rolling out embedded AI enhancements, all building on the familiar Admin user experience, makes it an attractive option for businesses seeking to adopt AI without the steep learning curve associated with coding. While there was palpable enthusiasm among attendees, the workshop also highlighted the complexity of setting up data sources and the challenges of working with a new nomenclature. As Salesforce continues to refine its AI offerings, striking the right balance between flexibility and ease of use will be crucial to widespread adoption. Google LLM Google: Engineering-Centric, Code-Intensive The “Build LLM-Powered Apps with Google” workshop, held on the Google campus in Mountain View, attracted around 150 attendees, primarily developers and engineers. They met in a large meeting room with circular tables. The event kicked off with a keynote presentation and detailed descriptions of Google’s efforts in creating retrieval-augmented generation (RAG) pipelines. They participated in a hands-on workshop, building a RAG database for an “SFO Assistant” chatbot designed to assist passengers at San Francisco airport. Running Postgres and pgvector with BigQuery Using Google Cloud Platform, they created a new VM running Postgres with the pgvector extension. They executed a series of commands to load the SFO database and establish a connection between Gemini and the database. The workshop provided step-by-step guidance, with Google staff helping when needed. Ultimately, they successfully ran a chatbot utilizing the RAG database. The workshop also showcased the power of BigQuery in generating prompts at scale through SQL statements. By crafting SQL queries that combined prompt engineering with retrieved data, they learned how to create personalized content, such as emails, for a group of customers in a single step. This demonstration highlighted the potential for efficient, large-scale content generation using Google’s tools. Gemini Assistant One of the most exciting discoveries for them during the workshop was the Gemini Assistant for BigQuery, a standout IT Companion Chatbot tailored for the GCP ecosystem. Comparable to GitHub Copilot Chat or ChatGPT-Plus, Gemini Assistant demonstrated a deep understanding of GCP and the ability to generate code snippets in various programming languages. What distinguishes Gemini Assistant is its strong grounding in GCP knowledge, enabling it to provide contextually

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May 22, 2024in Einstein 1 Platform, Generative AI, Salesforce
Patterns for Trust in AI
20May

Patterns for Trust in AI

The advent of AI has introduced a layer of complexity to the concept of trust, intertwining technology with societal and individual concerns in various ways. Patterns for Trust in AI address some of these concerns. Ultimately, this erosion of trust poses formidable obstacles to innovation and responsible development within teams. To foster trust and mitigate these challenges, the adoption of design patterns emerges as a viable solution: Design patterns encapsulate solutions to common problems in a manner that’s easily replicable by others, offering a framework for creating trustworthy services. While design patterns streamline user experiences, they should also introduce a judicious level of friction to: Like Related Posts 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 Guide to Creating a Working Sales Plan Creating a sales plan is a pivotal step in reaching your revenue objectives. To ensure its longevity and adaptability to Read more 50 Advantages of Salesforce Sales Cloud According to the Salesforce 2017 State of Service report, 85% of executives with service oversight identify customer service as a Read more Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more

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May 20, 2024in Generative AI, Salesforce Einstein, Salesforce Optimization
Gen AI and Test Automation
19May

Gen AI and Test Automation

Generative AI has brought transformative advancements across industries, and test automation is no exception. By generating code, test scenarios, and even entire suites, Generative AI enables Software Development Engineers in Test (SDETs) to boost efficiency, expand test coverage, and improve reliability. 1. Enhanced Test Case Generation One of the biggest hurdles in test automation is generating diverse, comprehensive test cases. Traditional methods often miss edge cases or diverse scenarios. Generative AI, however, can analyze existing data and automatically generate extensive test cases, including potential edge cases that may not be apparent to human testers. Example: An SDET can use Generative AI to create test cases for a web application by feeding it requirements and user data. This enables the AI to produce hundreds of test cases, capturing diverse user behaviors and interactions that manual testers may overlook. pythonCopy codeimport openai openai.api_key = ‘YOUR_API_KEY’ def generate_test_cases(application_description): response = openai.Completion.create( engine=”text-davinci-003″, prompt=f”Generate comprehensive test cases for the following application: {application_description}”, max_tokens=500 ) return response.choices[0].text app_description = “An e-commerce platform for browsing products, adding to cart, and checking out.” test_cases = generate_test_cases(app_description) print(test_cases) Sample Output: 2. Intelligent Test Script Creation Writing test scripts manually can be labor-intensive and error-prone. Generative AI can simplify this by generating test scripts based on an application’s flow, ensuring consistency and precision. Example: If an SDET needs to automate tests for a mobile app, they can use Generative AI to generate scripts for various scenarios, significantly reducing manual work. pythonCopy codeimport hypothetical_ai_test_tool ui_description = “”” Login Page: – Username field – Password field – Login button Home Page: – Search bar – Product listings – Add to cart buttons “”” test_scripts = hypothetical_ai_test_tool.generate_selenium_scripts(ui_description) Sample Output for test_login.py: pythonCopy codefrom selenium import webdriver from selenium.webdriver.common.keys import Keys def test_login(): driver = webdriver.Chrome() driver.get(“http://example.com/login”) username_field = driver.find_element_by_name(“username”) password_field = driver.find_element_by_name(“password”) login_button = driver.find_element_by_name(“login”) username_field.send_keys(“testuser”) password_field.send_keys(“password”) login_button.click() assert “Home” in driver.title driver.quit() 3. Automated Maintenance of Test Suites As applications evolve, maintaining test suites is critical. Generative AI can monitor app changes and update test cases automatically, keeping test suites accurate and relevant. Example: In a CI/CD pipeline, an SDET can deploy Generative AI to track code changes and update affected test scripts. This minimizes downtime and ensures tests stay aligned with application updates. pythonCopy codeimport hypothetical_ai_maintenance_tool def maintain_test_suite(): changes = hypothetical_ai_maintenance_tool.analyze_code_changes() updated_scripts = hypothetical_ai_maintenance_tool.update_test_scripts(changes) for script_name, script_content in updated_scripts.items(): with open(script_name, ‘w’) as file: file.write(script_content) maintain_test_suite() Sample Output:“Updating test_login.py with new login flow changes… Test scripts updated successfully.” 4. Natural Language Processing for Test Case Design Generative AI with NLP can interpret human language, enabling SDETs to create test cases from plain-language descriptions, enhancing collaboration across technical and non-technical teams. Example: An SDET can use an NLP-powered tool to translate a feature description from a product manager into test cases. This speeds up the process and ensures that test cases reflect intended functionality. pythonCopy codeimport openai openai.api_key = ‘YOUR_API_KEY’ def create_test_cases(description): response = openai.Completion.create( engine=”text-davinci-003″, prompt=f”Create test cases based on this feature description: {description}”, max_tokens=500 ) return response.choices[0].text feature_description = “Allow users to reset passwords via email to regain account access.” test_cases = create_test_cases(feature_description) print(test_cases) Sample Output: 5. Predictive Analytics for Test Prioritization Generative AI can analyze historical data to prioritize high-risk areas, allowing SDETs to focus testing on critical functionalities. Example: An SDET can use predictive analytics to identify areas with frequent bugs, allocating resources more effectively and ensuring robust testing of high-risk components. pythonCopy codeimport hypothetical_ai_predictive_tool def prioritize_tests(): risk_areas = hypothetical_ai_predictive_tool.predict_risk_areas() prioritized_tests = hypothetical_ai_predictive_tool.prioritize_test_cases(risk_areas) return prioritized_tests prioritized_test_cases = prioritize_tests() print(“Prioritized Test Cases:”) for test in prioritized_test_cases: print(test) Sample Output: Gen AI and Test Automation Generative AI has the potential to revolutionize test automation, offering SDETs tools to enhance efficiency, coverage, and reliability. By embracing Generative AI for tasks like test case generation, script creation, suite maintenance, NLP-based design, and predictive prioritization, SDETs can reduce manual effort and focus on strategic tasks, accelerating testing processes and ensuring robust, reliable software systems. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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May 19, 2024in Data, Generative AI

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