Predictive AI Archives - gettectonic.com - Page 3
Acceptable AI Use Policies
19Apr

AI to Close Sales

AI-Powered Sales Innovations 1. Identifying the Right Prospects with Predictive AI 💡 “Which prospects should I focus on?” What AI does:Predictive AI analyzes CRM data to assess which deals are likely to close, assigning each lead a score. Example:A sales rep filters leads based on Einstein Opportunity Scoring in Sales Cloud. The system ranks opportunities from 1 (low likelihood) to 99 (high likelihood). The rep clicks on scores to see insights into why certain deals are stronger and prioritizes their outreach accordingly. 2. Automating Lead Qualification with Buyer Assistant 💡 Turn your website into a sales rep What AI does:AI replaces traditional web forms with live chat, engaging visitors in real-time and qualifying them based on set criteria. Example:A visitor starts a chat on your site. Buyer Assistant asks key questions, determines if they fit your ideal customer profile, and logs their contact details. AI cross-checks CRM data for existing records and offers an instant or scheduled meeting with a sales rep. 3. Generating Personalized Sales Emails with Einstein Copilot 💡 “Write an email introducing me to these prospects.” What AI does:Generative AI drafts tailored emails using CRM insights. Example:A rep asks Einstein Copilot to email Richard Reed about a new product launch. Einstein personalizes the message based on past interactions. The rep tweaks the tone, making it more concise and engaging before sending. 4. Summarizing Deals and Customers Instantly 💡 Get up to speed in seconds What AI does:AI consolidates deal data, recent activities, key contacts, and next steps in an easy-to-read summary. Example:A rep taking over an account pulls up Einstein Opportunity Summary to see deal value, stage, key contacts, past interactions, and pending actions—all in one place. 5. Capturing Key Sales Call Insights Automatically 💡 No more rewatching entire sales calls What AI does:AI transcribes and summarizes calls, flagging important moments, customer sentiment, objections, and follow-up actions. Example:A rep uses Einstein Conversation Insights (ECI) during a sales call. Months later, a customer asks about pricing options discussed previously. Instead of listening to the old call, the rep asks Einstein, which instantly retrieves the key details. 6. Eliminating Data Entry with Automated Activity Capture 💡 More selling, less admin work What AI does:AI logs calls, emails, and meetings automatically, ensuring CRM records are always up to date. Example:A rep emails a lead through Outlook. Einstein Activity Capture creates a deal record in Sales Cloud, tracks all interactions, and updates the pipeline stage as the conversation progresses. 7. Guided Selling with AI-Powered Close Plans 💡 “How do I close this deal?” What AI does:AI generates step-by-step close plans tailored to each deal. Example:A stalled deal faces pricing competition. The rep asks Einstein Copilot for a close plan. AI suggests a discounted product bundle to outperform the competitor. The rep then requests an email comparing pricing details, and AI drafts it instantly. The Future of AI in Sales AI is fundamentally reshaping how sales teams operate. It’s not just about efficiency—it’s about enabling reps to sell smarter and focus on high-value activities. By integrating AI across your sales workflow, you can: ✅ Prioritize the best opportunities✅ Engage prospects in real-time✅ Automate content creation✅ Streamline CRM updates✅ Close deals faster Want to unlock AI’s full potential in your sales strategy? Start by identifying the areas where AI can drive the most impact—and let the technology do the heavy lifting. Content updated March 2025. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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April 19, 2024in Data, Generative AI, Technology
What is Salesforce Einstein 1
09Apr

What is Salesforce Einstein 1?

The bold new future of enterprise AI requires a new type of platform. What is Salesforce Einstein 1? One platform that can handle terabytes of disconnected data, gives you the freedom to choose your AI models, and connects directly into the flow of work, all while maintaining customer trust. The Einstein 1 Platform unifies your data, AI, CRM, development, and security into a single, comprehensive platform. It empowers IT, admins, and developers with an extensible AI platform, facilitating fast development of generative apps and automation. What is Salesforce Einstein 1 Einstein 1 has a mixture of artificial intelligence tools on the platform, and it kind of mirrors the way the core Salesforce platform is built, standardized and custom. We have out of the box AI features such as sales email generation in Sales Cloud, and service replies in Service Cloud. What is Einstein One? Einstein One seamlessly integrates our robust data platform with Data Cloud, offering a comprehensive AI and data-driven solution within our CRM service. As the leading comprehensive AI for CRM, Salesforce Einstein transforms the Customer Success Platform by incorporating a suite of integrated AI technologies, making it more intelligent and accessible to innovators worldwide. Introducing the Einstein One Platform The future of enterprise AI demands a groundbreaking platform capable of managing terabytes of diverse data, providing the flexibility to select AI models, and seamlessly integrating into daily workflows while maintaining customer trust. The Einstein One Platform consolidates data, AI, CRM, development, and security into a singular, comprehensive platform. It empowers IT, administrators, and developers with an extensible AI platform, facilitating rapid development of generative apps and automation. Salesforce has made significant investments in enhancing the Einstein One platform by introducing two prompt engineering features – a testing center and prompt engineering suggestions, demonstrating the company’s commitment to advancing AI capabilities. Data-driven and AI-driven customer experiences are vital in constructing Data Cloud. Salesforce’s predictive AI, Einstein, has been operational since 2016, conducting over a trillion predictions per week. The company continuously evolves its capabilities, introducing generative AI products like Einstein GPT, discussed at Dreamforce [2023]. ‘Customer 360′ now represents Salesforce customers’ ability to transform into customer-centric entities, delivering integrated and cohesive experiences across sales, marketing, service, and commerce. Leveraging Data Cloud, structured and unstructured engagement data comes alive, offering a personalized perspective of customer interactions. Einstein is accessible through various Salesforce products, including Sales Cloud, Service Cloud, Marketing Cloud, Salesforce Platform, Analytics Cloud, and Community Cloud. What is the Einstein One platform? Salesforce’s Einstein One is a transformative platform that securely integrates data, connects multiple Salesforce products, and empowers customer-centric businesses to create AI-driven apps, revolutionizing CRM interactions. What applications are connected with the Einstein One platform? Einstein Trust Layer: Ensuring the security of generative AI applications within Salesforce, this application acts as a safeguard for data, preventing unauthorized access. Data Cloud: Unifying structured and unstructured data, Data Cloud completes the customer 360 profile, integrating seamlessly with the Einstein One platform and making data actionable. Einstein Copilot: A built-in conversational assistant, Einstein Copilot enables users to ask questions in Natural Language and receive intelligent answers, actions, and more. Copilot Studio: An essential component of Einstein One, Copilot Studio manages Einstein One and Einstein Copilot, allowing businesses to control user access and use generative AI in their workspace. Prompt Builder, Skill Builder, Model Builder: Tools within Copilot Studio, these components empower businesses to create trusted AI prompts, add skills to user access, and choose AI models. What is Data Cloud for Einstein One? Data Cloud, combined with the Einstein Trust Layer, provides secure access to a unified data repository, forming a reliable resource for complete customer profiles and supporting AI assistants. Data Cloud facilitates valuable visualizations through reports, dashboards, and tools like Tableau, offering insights to enhance understanding of customers and business operations. What will Einstein One do for businesses? Einstein One aims to: Increase productivity Reduce operational costs Enhance the delivery of exceptional customer experiences The debut of Einstein One marks a significant stride for Salesforce and businesses, offering a secure path to boost productivity through AI technologies, ensuring continuous access to cutting-edge technologies for businesses. Where can generative AI impact businesses today? Generative AI has the potential to impact businesses by enhancing repetitive processes, making them faster. Exploring text-based processes, such as repetitive emails or form filling, is a great starting point for businesses looking to enhance capabilities. What can generative AI do for sales, service, field service teams? Generative AI can positively impact teams by automating processes, offering intelligent insights, and improving overall efficiency. Specific use cases for generative AI in sales, service, and field service teams can be explored through Salesforce’s 101 video series. What does Einstein One Tableau do? Einstein One Data Cloud, formerly known as Genie, serves as a metadata management tool connecting data from various sources. It makes data usable for AI and analysis, integrating with Einstein One, Tableau, and Salesforce’s CRM applications. Is Salesforce Einstein the same as Tableau? Formerly known as Einstein Analytics, Tableau CRM is Salesforce’s premium analytic platform, offering a powerful way to explore data, especially for current Salesforce users. Salesforce has renamed Einstein Analytics to Tableau CRM, reflecting its commitment to empowering users to make better decisions faster. In essence, the Einstein One platform combines Salesforce Data Cloud, Einstein Copilot, and the Einstein Trust Layer, providing a comprehensive solution for secure and connected data, facilitating generative AI on a comprehensive platform. Salesforce’s Einstein One Platform & the Data-Driven Revolution This innovation has the potential to revolutionize the way businesses interact with their customers. In this insight, we delve deeper into this transformative development, offering valuable insights, considerations for implementation, and the indispensable expertise of Tectonic as a data-oriented technology partner. Salesforce Data Cloud and Einstein AI are now seamlessly integrated into the Einstein One Platform. This integration empowers companies to effortlessly connect their diverse data sources, enabling the creation of AI-driven applications with minimal coding effort. Moreover, it forges a cohesive view of your data across

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April 9, 2024in Artificial Intelligence, Data, Einstein 1 Platform, Einstein Insights, Enterprise, Generative AI, Google, Mulesoft, Salesforce, Salesforce Data Cloud, Salesforce Einstein, Salesforce Sales Cloud, Technology
Where Will AI Take Us?
26Mar

Where Will AI Take Us?

Author Jeremy Wagstaff wrote a very thought provoking article on the future of AI, and how much of it we could predict based on the past. This insight expands on that article. Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn. These machines can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Many people think of artificial intelligence in the vein of how they personally use it. Some people don’t even realize when they are using it. Artificial intelligence has long been a concept in human mythology and literature. Our imaginations have been grabbed by the thought of sentient machines constructed by humans, from Talos, the enormous bronze automaton (self-operating machine) that safeguarded the island of Crete in Greek mythology, to the spacecraft-controlling HAL in 2001: A Space Odyssey. Artificial Intelligence comes in a variety of flavors, if you will. Artificial intelligence can be categorized in several ways, including by capability and functionality: You likely weren’t even aware of all of the above categorizations of artificial intelligence. Most of us still would sub set into generative ai, a subset of narrow AI, predictive ai, and reactive ai. Reflect on the AI journey through the Three C’s – Computation, Cognition, and Communication – as the guiding pillars for understanding the transformative potential of AI. Gain insights into how these concepts converge to shape the future of technology. Beyond a definition, what really is artificial intelligence, who makes it, who uses it, what does it do and how. Artificial Intelligence Companies – A Sampling AI and Its Challenges Artificial intelligence (AI) presents a novel and significant challenge to the fundamental ideas underpinning the modern state, affecting governance, social and mental health, the balance between capitalism and individual protection, and international cooperation and commerce. Addressing this amorphous technology, which lacks a clear definition yet pervades increasing facets of life, is complex and daunting. It is essential to recognize what should not be done, drawing lessons from past mistakes that may not be reversible this time. In the 1920s, the concept of a street was fluid. People viewed city streets as public spaces open to anyone not endangering or obstructing others. However, conflicts between ‘joy riders’ and ‘jay walkers’ began to emerge, with judges often siding with pedestrians in lawsuits. Motorist associations and the car industry lobbied to prioritize vehicles, leading to the construction of vehicle-only thoroughfares. The dominance of cars prevailed for a century, but recent efforts have sought to reverse this trend with ‘complete streets,’ bicycle and pedestrian infrastructure, and traffic calming measures. Technology, such as electric micro-mobility and improved VR/AR for street design, plays a role in this transformation. The guy digging out a road bed for chariots and Roman armies likely considered none of this. Addressing new technology is not easy to do, and it’s taken changes to our planet’s climate, a pandemic, and the deaths of tens of millions of people in traffic accidents (3.6 million in the U.S. since 1899). If we had better understood the implications of the first automobile technology, perhaps we could have made better decisions. Similarly, society should avoid repeating past mistakes with AI. The market has driven AI’s development, often prioritizing those who stand to profit over consumers. You know, capitalism. The rapid adoption and expansion of AI, driven by commercial and nationalist competition, have created significant distortions. Companies like Nvidia have soared in value due to AI chip sales, and governments are heavily investing in AI technology to gain competitive advantages. Listening to AI experts highlights the enormity of the commitment being made and reveals that these experts, despite their knowledge, may not be the best sources for AI guidance. The size and impact of AI are already redirecting massive resources and creating new challenges. For example, AI’s demand for energy, chips, memory, and talent is immense, and the future of AI-driven applications depends on the availability of computing resources. The rise in demand for AI has already led to significant industry changes. Data centers are transforming into ‘AI data centers,’ and the demand for specialized AI chips and memory is skyrocketing. The U.S. government is investing billions to boost its position in AI, and countries like China are rapidly advancing in AI expertise. China may be behind in physical assets, but it is moving fast on expertise, generating almost half of the world’s top AI researchers (Source: New York Times). The U.S. has just announced it will provide chip maker Intel with $20 billion in grants and loans to boost the country’s position in AI. Nvidia is now the third largest company in the world, entirely because its specialized chips account for more than 70 percent of AI chip sales. Memory-maker Micro has mostly run out of high-bandwidth memory (HBM) stocks because of the chips’ usage in AI—one customer paid $600 million up-front to lock in supply, according to a story by Stack. Back in January, the International Energy Agency forecast that data centers may more than double their electrical consumption by 2026 (Source: Sandra MacGregor, Data Center Knowledge). AI is sucking up all the payroll: Those tech workers who don’t have AI skills are finding fewer roles and lower salaries—or their jobs disappearing entirely to automation and AI (Source: Belle Lin at WSJ). Sam Altman of OpenAI sees a future where demand for AI-driven apps is limited only by the amount of computing available at a price the consumer is willing o pay. “Compute is going to be the currency of the future. I think it will be maybe the most precious commodity in the world, and I think we should be investing heavily to make a lot more compute.” Sam Altman, OpenAI CEO This AI buildup is reminiscent of past technological transformations, where powerful interests shaped outcomes, often at the expense of broader societal considerations. Consider early car manufacturers. They focused on a need for factories, components, and roads.

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March 26, 2024in Generative AI
Acceptable AI Use Policies
19Mar

AI Data Readiness

Data Readiness: The Key to AI Success As organizations rapidly integrate AI into their CRM systems, data readiness has become a critical factor in successful AI implementation. A study by Forrester found that companies prioritizing data readiness achieve better outcomes when deploying AI-powered CRM features. Additionally, organizations can leverage vendors to enhance their data readiness and AI capabilities, though trust remains a serious consideration. The Impact of Data Readiness The Growing Importance of AI-Ready Data AI adoption has surged across industries, with a McKinsey survey revealing that AI adoption has more than doubled in recent years. Furthermore, IBM’s 2023 CEO study found that 75% of CEOs believe competitive advantage hinges on advanced generative AI capabilities. However, AI is only as effective as the data supporting it. Chad Sanderson, CEO of Gable, likens data readiness to a healthy diet: “If garbage goes in, garbage comes out.” AI-ready data must be well-governed, secure, bias-free, enriched, accurate, and high quality. What is Data Readiness for AI? Data readiness for AI involves preparing data to ensure its effectiveness in AI-driven applications. Key characteristics of AI-ready data include: Defining AI-Ready Data: 4 Key Factors Different industry experts provide varied definitions of AI-ready data: In our interpretation, AI-ready data exists in a unified platform, not in silos. It is enriched with metadata, ensuring transparency in data lineage, enabling AI systems to deliver meaningful insights. Additionally, AI-ready data requires a governance framework to maintain security, compliance, and ethical use. 1. Metadata Management Metadata management provides essential context for AI systems. Key elements include: 2. Data and Metadata Quality Management High-quality AI output depends on high-quality data. Organizations should continuously evaluate data based on relevance, reliability, and accuracy. Equally important is metadata quality, which ensures that AI models interpret and apply data correctly. 3. Data Lineage Management Understanding data origins and transformations is crucial for AI readiness. Harvard Business Review emphasizes the importance of ontology, a structured representation of data relationships, in AI system development. 4. Data Governance Data governance establishes AI standards, ensuring ethical and compliant AI practices. Steve Lohr of The New York Times highlights data provenance as a key governance element, akin to food safety regulations ensuring transparency in data origins and processing. Is Your Data AI-Ready? According to IBM’s 2023 CEO survey, the top barriers to AI adoption include concerns around data lineage, security, and compliance. Harvard Business Review also cites siloed data as a major challenge, emphasizing that AI success requires well-prepared data systems. Gartner underscores the necessity of lighthouse principles—guiding frameworks for ethical AI usage. Organizations lacking structured governance, transparency, and documentation are not yet AI-ready. How to Ensure Data Readiness for AI A Gartner survey found that only 9% of CIOs and data leaders have an AI vision statement, with over a third lacking plans to create one. The first step to AI readiness is defining an AI vision statement and setting lighthouse principles to guide AI initiatives. Key strategies for AI readiness include: Wrapping Up Achieving data readiness for AI involves ensuring data is secure, accurate, bias-free, enriched, and accessible. Key components include metadata management, quality assurance, data lineage tracking, and governance frameworks. Ultimately, AI readiness extends beyond technology—it requires a cultural shift within organizations. Aligning strategy, preparing data, and fostering AI-driven decision-making will enable organizations to harness AI’s full potential. By following these best practices, businesses can ensure their AI investments deliver meaningful, data-driven insights. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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March 19, 2024in Data, Generative AI, Salesforce, Technology
What is Einstein Used for in Salesforce?
14Mar

What is Einstein Used for in Salesforce?

Salesforce Einstein is an AI-powered platform that can be used in various ways to enhance customer experiences and streamline business operations: SalesSalesforce Einstein can help sales teams better understand customers, improve conversion rates, and close deals more quickly. For instance, it can generate sales call summaries, draft emails using customer data, and provide real-time predictions. Customer ServiceEinstein helps customer service agents resolve cases faster and provide customers with relevant information during interactions. MarketingSalesforce Einstein enables marketers to create personalized experiences and send the right content to the right customer at the right time. ITSalesforce empowers IT teams to embed intelligence across the business and create smarter apps for customers and employees. CommerceSalesforce assists retailers by recommending the best products to each customer. Salesforce also includes features to protect data privacy and security, such as the Tectonic GPT Trust Layer, which provides AI bias detection, data security, and regulatory compliance. Salesforce Einstein is the first all-inclusive AI for CRM. It’s an integrated set of AI technologies that makes the Customer Success Platform smarter and brings AI to Salesforce users everywhere. Salesforce is the only comprehensive AI for CRM. It is: Tectonic and Salesforce allow businesses to become AI-first, providing the ability to anticipate customer needs, improve service efficiency, and enable smarter, data-driven decision-making. Sales teams can anticipate next opportunities and exceed customer needs,Service teams can proactively resolve issues before they occur,Marketing teams can create predictive journeys and personalize experiences like never before,IT teams can embed intelligence everywhere and create smarter apps. AI that works for your business.Drive business productivity and personalization with predictive AI, generative AI, and agents across the Customer 360 platform. Create and deploy assistive AI experiences natively in Salesforce, allowing your customers and employees to converse directly with Agentforce to solve issues faster and work smarter. Empower service reps, agents, marketers, and others with AI tools safely grounded in your customer data to make every customer experience more impactful. What is Salesforce Einstein?As of 2024, this groundbreaking AI-based product remains a leader in the CRM industry since its release in 2016. It combines a range of AI technologies, including advanced machine learning, natural language processing (NLP), predictive analytics, and image recognition, enabling businesses to improve productivity and sustain growth. Salesforce AI BenefitsThe most significant benefits of AI are the time and efficiency gains it offers to business processes. By automating tasks, employees can focus on more strategic work. Additionally, automating repetitive tasks reduces errors and enhances operational efficiency. Saleesforce provides robust reporting features that generate valuable insights to support decision-making, helping businesses understand customer needs and identify opportunities. From a customer perspective, Salesforce ensures more meaningful and personalized experiences through advanced NLP capabilities and machine learning to better understand customer behavior. Salesforce AI FeaturesSalesforce is a feature-rich platform that leverages AI’s capabilities in Natural Language Processing, Machine Learning, and image processing. Some of the key features include: Salesforce PricingCosts depend on the required features and the size of the business. Pricing starts at $50 per user per month, with potential increases based on the specific capabilities needed. Salesforce Tectonic ChallengesAlthough Salesforce Tectonic offers numerous benefits, companies may face challenges during integration, such as aligning it with existing systems and ensuring proper training for employees to maximize its use. How to Prepare for Salesforce Tectonic IntegrationUsing an implementation partner like Tectonic can help ensure seamless integration. A partner will assess your current Salesforce setup, recommend the right features, and guide you through the integration process. ConclusionSalesforce is a cutting-edge platform that empowers businesses to transform operations with comprehensive AI capabilities. It provides tailored solutions for sales, service, marketing, and commerce teams, enabling better customer interactions, data-driven decision-making, and increased productivity. With the right implementation partner like Tectonic, businesses can seamlessly integrate and leverage Tectonic to stay ahead in a competitive landscape. Content updated November 2024. Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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March 14, 2024in AI Tools, Data, Generative AI, Salesforce, Salesforce Einstein
Customized Conversational AI Assistant
11Mar

Customized Conversational AI Assistant

Create and Customize a Conversational AI Assistant for CRM Einstein Copilot is your all-in-one CRM AI assistant, seamlessly integrated into every Salesforce application. It empowers teams to accelerate tasks with intelligent actions, deploy conversational AI with built-in trust, and easily scale a unified copilot across your organization. Customized Conversational AI Assistant. Einstein 1 Studio Customize and Enhance AI for CRM:Einstein 1 Studio allows you to tailor Einstein Copilot to your specific business needs. Configure actions, prompts, and models to create a personalized AI experience. Users can interact with the AI using natural language, making task execution more intuitive and efficient. Copilot Builder Expand Einstein Copilot with Advanced Features:Enhance Einstein Copilot by integrating actions with familiar Salesforce platform features like Flows, Apex code, and Mulesoft APIs. Convert workflows into copilot actions and test these interactions within a user-friendly interface, enabling you to monitor and refine your copilot’s performance. Prompt Builder Accelerate Employee Task Completion:Design prompt templates that quickly summarize and generate content, helping employees complete tasks faster. Create prompts that draw from CRM data, Data Cloud, and external sources to make every business task more relevant. Develop prompts once and deploy them across Einstein Copilot, Lightning pages, and flows. Model Builder Integrate and Manage AI Models:Incorporate your predictive AI models and large language models (LLMs) within Salesforce through the Einstein Trust Layer. Utilize no-code ML models in Data Cloud, and manage all your AI models from a centralized control platform, ensuring seamless operation and integration. Deploy Trustworthy AI Leverage Generative AI with Built-In Safeguards:Einstein Copilot is designed to ensure the privacy and security of your data, while improving result accuracy and promoting responsible AI use across your organization. Built directly into the Salesforce Platform, the Einstein Trust Layer offers top-tier features and safeguards to ensure your AI deployments are trustworthy. “The combination of AI, data, and CRM allows us to help busy parents solve the ‘what’s for dinner’ dilemma with personalized recipe recommendations their family will love.”— Heather Conneran, Director, Brand Experience Platforms, General Mills Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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March 11, 2024in Data, Generative AI, Mulesoft, Salesforce
Generative AI Glossary
27Jan

Key Questions to Ask About Generative AI Before Diving into the Gene Pool

As generative AI plays an increasingly significant role in shaping business decisions and reshaping customer relationships, leaders must grasp the potential.  This means use cases, and risks associated with AI. The good, the bad, and the ugly.  Questions to Ask About Generative AI gene pool. The journey begins with asking pertinent questions. Are you feeling overwhelmed by generative AI yet? The multitude of questions that businesses need to address regarding AI—covering technology, skills, privacy, data, and organizational requirements, among others—can be seemingly endless. Knowing where to start and identifying the most crucial AI-related questions before jumping into implementation can be challenging.  But it is totally worth the time. “Many organizations are venturing into AI for the first time. They are transitioning from predictive AI, machine learning, or deep learning to explore the next generation of AI for elevating productivity.” Marc Benioff, CEO of Salesforce While the demand and potential of AI are substantial, so are the associated risks. To assist in navigating this landscape, here’s a snapshot: Employee View: Exec Summary: Your Next Move: By Tectonic’s Marketing Consultant, Shannan Hearne Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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January 27, 2024in Generative AI, Salesforce Einstein
Marketing Cloud Growth and Advanced Editions
22Dec

Marketing Cloud Growth and Advanced Editions

While Growth Edition is tailored to small businesses looking to get started with robust marketing automation, Advanced Edition caters to companies that need more sophisticated tools to scale personalization efforts, improve customer engagement, and streamline workflows. It offers additional features, including real-time journey testing, predictive AI for customer scoring, and advanced SMS capabilities, allowing businesses to enhance every touchpoint with their customers.

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December 22, 2023in Data, Enterprise, Marketing Automation, Salesforce, Salesforce Data Cloud, Salesforce Implementation Services, Salesforce Marketing Automation, Salesforce Marketing Cloud, Technology
Salesforce Einstein and Einstein Automate
09Aug

Einstein Trust

Generative AI, Salesforce, and the Commitment to Trust The excitement surrounding generative AI is palpable as it unlocks new dimensions of creativity for individuals and promises significant productivity gains for businesses. Engaging with generative AI can be a great experience, whether creating superhero versions of your pets with Midjourney or crafting pirate-themed poems using ChatGPT. According to Salesforce research, employees anticipate saving an average of 5 hours per week through the adoption of generative AI, translating to a substantial monthly time gain for full-time workers. Whether designing content for sales and marketing or creating a cute version of a beloved story, generative AI is a tool that helps users create content faster. However, amidst the enthusiasm, questions arise, including concerns about the security and privacy of data. Users ponder how to leverage generative AI tools while safeguarding their own and their customers’ data. Questions also revolve around the transparency of data collection practices by different generative AI providers and ensuring that personal or company data is not inadvertently used to train AI models. Additionally, there’s a need for assurance regarding the accuracy, impartiality, and reliability of AI-generated responses. Salesforce has been at the forefront of addressing these concerns, having embraced artificial intelligence for nearly a decade. The Einstein platform, introduced in 2016, marked Salesforce’s foray into predictive AI, followed by investments in large language models (LLMs) in 2018. The company has diligently worked on generative AI solutions to enhance data utilization and productivity for their customers. The Einstein Trust Layer is designed with private, zero-retention architecture. Emphasizing the value of Trust, Salesforce aims to deliver not just technological capabilities but also a responsible, accountable, transparent, empowering, and inclusive approach. The Einstein Trust Layer represents a pivotal development in ensuring the security of generative AI within Salesforce’s offerings. The Einstein Trust Layer is designed to enhance the security of generative AI by seamlessly integrating data and privacy controls into the end-user experience. These controls, forming gateways and retrieval mechanisms, enable the delivery of AI securely grounded in customer and company data, mitigating potential security risks. The Trust Layer incorporates features such as secure data retrieval, dynamic grounding, data masking, zero data retention, toxic language detection, and an audit trail, all aimed at protecting data and ensuring the appropriateness and accuracy of AI-generated content. Salesforce proactively provided the ability for any admin to control how prompt inputs and outputs are generated, including reassurance over data privacy and reducing toxicity. This innovative approach allows customers to leverage the benefits of generative AI without compromising data security and privacy controls. The Trust Layer acts as a safeguard, facilitating secure access to various LLMs, both within and outside Salesforce, for diverse business use cases, including sales emails, work summaries, and service replies in contact centers. Through these measures, Salesforce underscores its commitment to building the most secure generative AI in the industry. Generating content within Salesforce can be achieved through three methods: CRM Solutions: Einstein Copilot Studio: Einstein LLM Generations API: An overarching feature of these AI capabilities is that every Language Model (LLM) generation is meticulously crafted through the Trust Layer, ensuring reliability and security. At Tectonic, we look forward to helping you embrace and utilize generative AI with Einstein save time. Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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August 9, 2023in Artificial Intelligence, Data, Generative AI, Salesforce, Salesforce Einstein
Cloud First
19Apr

Advances in Generative AI

What is generative AI?  Generative AI focuses on creating new and original content, chat responses, designs, synthetic data or even deepfakes.  While predictive AI worked on predefined, human supplied rules, generative AI functions somewhat autonomously. Advances in Generative AI have been groundbreaking. Advances in generative AI represent a significant advancement beyond established technologies like predictive AI, and business leaders are eagerly embracing its potential. A remarkable 91% recognize generative AI as a major advantage, driven by its diverse applications, from content creation to software development. Despite its novelty, generative AI is rapidly progressing, causing over three-quarters of business leaders to express concerns about potentially missing out on its benefits. In particular, marketing leaders are apprehensive about not fully leveraging generative AI in their workflows, with 88% worried that their companies are lagging behind. Insight Generation and Decision-Making: Going beyond traditional data analysis, generative AI excels by not only analyzing existing data but also generating potential scenarios. This predictive modeling empowers businesses to anticipate market shifts, understand consumer preferences, and identify potential risks, fostering proactive strategies over reactive ones. Generative AI’s Global Impact: Generative AI has captivated global attention, with ChatGPT becoming the fastest-growing software program in history, reaching a hundred million users within two months of its public debut. This surge has sparked an arms race among tech giants like Microsoft and Google, and AI chip maker Nvidia has witnessed increased business. Unlike previous AI programs that provided numeric scores, generative AI, including programs like Stability AI’s Stable Diffusion and OpenAI’s DALL-E, reproduces elements of the real world. Amazon announced in 2023 that its voice assistant Alexa now comes with generative AI capabilities. Apple is developing a large array of features that use generative AI, including a new version of Siri expected to launch in 2024. Mixed Modality in AI: The concept of mixed modality or “multi-modality” is taking center stage, enabling programs to fuse text, images, physical space representations, sounds, video, and entire computer functions as smart applications. This approach enhances program capabilities and contributes to continuous learning, potentially advancing the goal of “embodied AI” and robotics. Evolution of Generative AI: Generative AI will continue evolving, contributing to advancements in translation, drug discovery, anomaly detection, and the generation of new content, spanning text, video, fashion design, and music. A generative AI chatbot, for example, is a type of conversational AI system that uses deep learning and natural language processing techniques to generate human-like text responses in real-time. These chatbots can hold text-based conversations with users, understand user input, and generate contextually relevant responses. Transformative Trends in Marketing and Sales Operations: Generative AI is reshaping marketing and sales operations with key trends, including hyper-quick sales and marketing content creation, automation of repetitive tasks (e.g., keyword research, administrative work, content formatting, and data analysis), and the facilitation of sales enablement and custom materials. What is the Main Goal of Generative AI? The answer likely would vary depending on who you ask, but commonly we expect generative AI tools to change the calculus of knowledge work automation. Generative AI isn’t going to eliminate the need for human workers, but it will assist them with the ability to produce human-like writing, images, audio, or video in response to plain-English text prompts. The potential to collaborate with human partners to generate contact that represents practical work is exciting. Curious how generative AI could help your business? Contact Tectonic today to learn more. Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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April 19, 2023in Salesforce Einstein
AI All Grown Up
09Feb

Understanding Generative AI and Predictive AI

Understanding Generative AI and Predictive AI: A Synergistic Approach Artificial Intelligence (AI) is broadly categorized into two key branches: Generative AI and Predictive AI. Both play a crucial role across various industries, from healthcare and fintech to logistics and education. Their impact is undeniable, driving efficiency, accuracy, and innovation. However, this is not a debate about Generative AI versus Predictive AI. Instead, it is an exploration of both branches and how they contribute to technological advancement. Let’s dive in. Generative AI vs. Predictive AI: An Overview Generative AI has been around for decades, with early iterations like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). While these earlier models saw limited enterprise adoption, the success of ChatGPT demonstrated the vast potential of Generative AI in producing articulate, human-like content. Conversely, Predictive AI is widely used across industries to correlate data and support decision-making. It is particularly prevalent in applications like cybersecurity, inventory management, and digital twin technology. Businesses increasingly recognize the benefits of both AI branches. From automating processes to creating digital replicas for scenario testing, AI applications continue to evolve. The goal now is not to compare Generative AI and Predictive AI, but to understand their mechanisms and potential for seamless integration. Are you fully leveraging AI in your enterprise? If not, or if you have questions, feel free to reach out. Now, let’s delve into how these AI branches work. What is Generative AI? Generative AI is transforming industries by producing text, code, music, and even videos. Companies use it to analyze vast datasets and generate content instantaneously. Key Applications of Generative AI: By 2026, over 80% of businesses are expected to incorporate Generative AI into their workflows. While implementation can be complex, expert guidance can help streamline the process. How Does Generative AI Work? Generative AI leverages machine learning (ML) and big data to analyze input forms—such as text, images, or sound—and learn their structures. Once trained, it generates new content without merely replicating existing data, making it a powerful tool for innovation. Generative AI in Action: If you’re uncertain about how to implement Generative AI in your business, consulting with experts can provide clarity. What is Predictive AI? Predictive AI, or predictive analytics, forecasts future outcomes based on historical data. It empowers businesses to make informed decisions by identifying patterns and trends. Key Applications of Predictive AI: Predictive AI improves decision-making capabilities by analyzing large datasets and refining machine learning algorithms. Integrating it with other analytics tools enhances its effectiveness and mitigates implementation challenges. Predictive AI in Action: Predictive AI’s ability to anticipate market trends and consumer behavior makes it a valuable tool for businesses looking to stay ahead. Generative AI vs. Predictive AI: Key Differences While Generative AI focuses on creating new content based on learned data patterns, Predictive AI forecasts future outcomes using historical data. These two models are not competing forces; rather, they complement each other in building comprehensive business strategies. Both models require a strong foundation in data governance and cybersecurity to ensure ethical and effective AI implementation. The Future of AI: Generative vs. Predictive According to McKinsey, the combined impact of Generative and Predictive AI could contribute up to $4.4 trillion annually to the global economy. What’s Next for AI? Generative AI: Predictive AI: Both Generative and Predictive AI are poised to shape the future of AI-driven industries. Businesses that embrace both models will gain a competitive edge in innovation and strategic decision-making. Conclusion Generative and Predictive AI are not opposing technologies; they are complementary forces that drive efficiency, accuracy, and creativity. Their applications span numerous industries, proving their immense value in today’s tech-driven world. Navigating AI implementation can be complex, but expert guidance can simplify the process. If you have questions about integrating AI into your business, consulting with professionals can help you harness its full potential. The future of business is deeply intertwined with AI—taking the right steps today will ensure success in the years ahead. Let Tectonic take you to the AI world. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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February 9, 2023in Artificial Intelligence, Data, Enterprise, Generative AI, Salesforce, Technology
gettectonic.com