Internal Data Archives - gettectonic.com
13Mar

Why Its Good to be Data-Driven

The Power of Data-Driven Decision Making Success in business hinges on the ability to make informed decisions. Every operational aspect, from minor choices like office furniture selection to critical investments such as multi-million-dollar marketing campaigns, is shaped by a series of interrelated decisions. While instinct and intuition may play a role, most business choices rely on relevant data—covering aspects such as objectives, pricing, technology, and potential risks. However, excess irrelevant data can be just as detrimental as insufficient accurate data. Why Its Good to be Data-Driven organization… The Evolution of Data-Driven Decision Making Organizations that prioritize data-driven strategies rely on accurate, relevant, complete, and timely data. Simply amassing large volumes of information does not equate to better decision-making; companies must democratize data access, ensuring it is available to all employees rather than limited to data analysts. The practice of using data to inform business decisions gained traction in the mid-20th century when researchers identified decision-making as dynamic, complex, and often ambiguous. Early techniques like decision trees and prospect theory emerged in the 1970s alongside computer-aided decision-making models. The 1980s saw the rise of commercial decision support systems, and by the early 21st century, data warehousing and data mining revolutionized analytics. However, without clear governance and organizational policies, these vast data stores often fell short of their potential. Today, the goal of data-driven decision-making is to combine automated decision models with human expertise, creativity, and critical thinking. This approach requires integrating data science with business operations, equipping managers and employees with powerful decision-support tools. Characteristics of a Data-Driven Organization A truly data-driven organization understands the value of its data and maximizes its potential through structured alignment with business objectives. To safeguard and leverage data assets effectively, businesses must implement governance frameworks ensuring compliance with privacy, security, and integrity standards. Key challenges in establishing a data-driven infrastructure include: The Benefits of a Data-Driven Approach Businesses recognize that becoming data-driven requires more than just investing in technology; success depends on strategy and execution. According to KPMG, four critical factors contribute to the success of data-driven initiatives: A data-driven corporate culture accelerates decision-making, enhances employee engagement, and increases overall business value. Integrating ethical considerations into data usage is crucial for mitigating biases and maintaining data integrity. Transitioning to a Data-Driven Business With the rapid advancement of generative AI, data-driven organizations are poised to unlock trillions of dollars in economic value. McKinsey estimates that AI-driven decision-making could add between .6 trillion and .4 trillion annually across key sectors, including customer operations, marketing, software engineering, and R&D. To successfully transition into a data-driven organization, companies must: By embracing a data-driven model, organizations enhance their ability to make automated yet strategically sound decisions. With seamless data integration across CRM, ERP, and business applications, companies empower human decision-makers to apply their expertise to high-quality, actionable insights—driving innovation and competitive advantage in a rapidly evolving marketplace. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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March 13, 2025in Data, Generative AI, Technology0 Comments
Generative AI in Marketing
28Feb

Generative AI in Marketing

Generative Artificial Intelligence (GenAI) continues to reshape industries, providing product managers (PMs) across domains with opportunities to embrace AI-focused innovation and enhance their technical expertise. Over the past few years, GenAI has gained immense popularity. AI-enabled products have proliferated across industries like a rapidly expanding field of dandelions, fueled by abundant venture capital investment. From a product management perspective, AI offers numerous ways to improve productivity and deepen strategic domain knowledge. However, the fundamentals of product management remain paramount. This discussion underscores why foundational PM practices continue to be indispensable, even in the evolving landscape of GenAI, and how these core skills can elevate PMs navigating this dynamic field. Why PM Fundamentals Matter, AI or Not Three core reasons highlight the enduring importance of PM fundamentals and actionable methods for excelling in the rapidly expanding GenAI space. 1. Product Development is Inherently Complex While novice PMs might assume product development is straightforward, the reality reveals a web of interconnected and dynamic elements. These may include team dependencies, sales and marketing coordination, internal tooling managed by global teams, data telemetry updates, and countless other tasks influencing outcomes. A skilled product manager identifies and orchestrates these moving pieces, ensuring product growth and delivery. This ability is often more impactful than deep technical AI expertise (though having both is advantageous). The complexity of modern product development is further amplified by the rapid pace of technological change. Incorporating AI tools such as GitHub Copilot can accelerate workflows but demands a strong product culture to ensure smooth integration. PMs must focus on fundamentals like understanding user needs, defining clear problems, and delivering value to avoid chasing fleeting AI trends instead of solving customer problems. While AI can automate certain tasks, it is limited by costs, specificity, and nuance. A PM with strong foundational knowledge can effectively manage these limitations and identify areas for automation or improvement, such as: 2. Interpersonal Skills Are Irreplaceable As AI product development grows more complex, interpersonal skills become increasingly critical. PMs work with diverse teams, including developers, designers, data scientists, marketing professionals, and executives. While AI can assist in specific tasks, strong human connections are essential for success. Key interpersonal abilities for PMs include: Stakeholder management remains a cornerstone of effective product management. PMs must build trust and tailor their communication to various audiences—a skill AI cannot replicate. 3. Understanding Vertical Use Cases is Essential Vertical use cases focus on niche, specific tasks within a broader context. In the GenAI ecosystem, this specificity is exemplified by AI agents designed for narrow applications. For instance, Microsoft Copilot includes a summarization agent that excels at analyzing Word documents. The vertical AI market has experienced explosive growth, valued at .1 billion in 2024 and projected to reach .1 billion by 2030. PMs are crucial in identifying and validating these vertical use cases. For example, the team at Planview developed the AI Assistant “Planview Copilot” by hypothesizing specific use cases and iteratively validating them through customer feedback and data analysis. This approach required continuous application of fundamental PM practices, including discovery, prioritization, and feedback internalization. PMs must be adept at discovering vertical use cases and crafting strategies to deliver meaningful solutions. Key steps include: Conclusion Foundational product management practices remain critical, even as AI transforms industries. These core skills ensure that PMs can navigate the challenges of GenAI, enabling organizations to accelerate customer value in work efficiency, time savings, and quality of life. By maintaining strong fundamentals, PMs can lead their teams to thrive in an AI-driven future. AI Agents on Madison Avenue: The New Frontier in Advertising AI agents, hailed as the next big advancement in artificial intelligence, are making their presence felt in the world of advertising. Startups like Adaly and Anthrologic are introducing personalized AI tools designed to boost productivity for advertisers, offering automation for tasks that are often time-consuming and tedious. Retail brands such as Anthropologie are already adopting this technology to streamline their operations. How AI Agents WorkIn simple terms, AI agents operate like advanced AI chatbots. They can handle tasks such as generating reports, optimizing media budgets, or analyzing data. According to Tyler Pietz, CEO and founder of Anthrologic, “They can basically do anything that a human can do on a computer.” Big players like Salesforce, Microsoft, Anthropic, Google, and Perplexity are also championing AI agents. Perplexity’s CEO, Aravind Srinivas, recently suggested that businesses will soon compete for the attention of AI agents rather than human customers. “Brands need to get comfortable doing this,” he remarked to The Economic Times. AI Agents Tailored for Advertisers Both Adaly and Anthrologic have developed AI software specifically trained for advertising tasks. Built on large language models like ChatGPT, these platforms respond to voice and text prompts. Advertisers can train these AI systems on internal data to automate tasks like identifying data discrepancies or analyzing economic impacts on regional ad budgets. Pietz noted that an AI agent can be set up in about a month and take on grunt work like scouring spreadsheets for specific figures. “Marketers still log into 15 different platforms daily,” said Kyle Csik, co-founder of Adaly. “When brands in-house talent, they often hire people to manage systems rather than think strategically. AI agents can take on repetitive tasks, leaving room for higher-level work.” Both Pietz and Csik bring agency experience to their ventures, having crossed paths at MediaMonks. Industry Response: Collaboration, Not Replacement The targets for these tools differ: Adaly focuses on independent agencies and brands, while Anthrologic is honing in on larger brands. Meanwhile, major holding companies like Omnicom and Dentsu are building their own AI agents. Omnicom, on the verge of merging with IPG, has developed internal AI solutions, while Dentsu has partnered with Microsoft to create tools like Dentsu DALL-E and Dentsu-GPT. Havas is also developing its own AI agent, according to Chief Activation Officer Mike Bregman. Bregman believes AI tools won’t immediately threaten agency jobs. “Agencies have a lot of specialization that machines can’t replace today,” he said. “They can streamline processes, but

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February 28, 2025in AI Tools, Artificial Intelligence, Data, Generative AI, Google, Laws, Salesforce, Technology
ChatGPT Memory Announced
02Jan

OpenAI ChatGPT Prompt Guide

Mastering AI Prompting: OpenAI’s Guide to Optimal Model Performance The Art of Effective AI Communication OpenAI has unveiled essential guidelines for optimizing interactions with their reasoning models. As AI systems grow more sophisticated, the quality of user prompts becomes increasingly critical in determining output quality. This guide distills OpenAI’s latest recommendations into actionable strategies for developers, business leaders, and researchers seeking to maximize their AI results. Core Principles for Superior Prompting 1. Clarity Over Complexity Best Practice: Direct, uncomplicated prompts yield better results than convoluted instructions. Example Evolution: Why it works: Modern models possess sophisticated internal reasoning – trust their native capabilities rather than over-scripting the thought process. 2. Rethinking Step-by-Step Instructions New Insight: Explicit “think step by step” prompts often reduce effectiveness rather than enhance it. Example Pair: Pro Tip: For explanations, request the answer first then ask “Explain your calculation” as a follow-up. 3. Structured Inputs with Delimiters For Complex Queries: Use clear visual markers to separate instructions from content. Implementation: markdown Copy Compare these two product descriptions: — [Description A] — [Description B] — Benefit: Reduces misinterpretation by 37% in testing (OpenAI internal data). 4. Precision in Retrieval-Augmented Generation Critical Adjustment: More context ≠ better results. Be surgical with reference materials. Optimal Approach: 5. Constraint-Driven Prompting Formula: Action + Domain + Constraints = Optimal Output Example Progression: 6. Iterative Refinement Process Workflow Strategy: Case Study: Advanced Techniques for Professionals For Developers: python Copy # When implementing RAG systems: optimal_context = filter_documents( query=user_query, relevance_threshold=0.85, max_tokens=1500 ) For Business Analysts: Dashboard Prompt Template:“Identify [X] key trends in [dataset] focusing on [specific metrics]. Format as: 1) Trend 2) Business Impact 3) Recommended Action” For Researchers: “Critique this methodology [paste abstract] focusing on: 1) Sample size adequacy 2) Potential confounding variables 3) Statistical power considerations” Performance Benchmarks Prompt Style Accuracy Score Response Time Basic 72% 1.2s Optimized 89% 0.8s Over-engineered 65% 2.1s Implementation Checklist The Future of Prompt Engineering As models evolve, expect: Final Recommendation: Regularly revisit prompting strategies as model capabilities progress. What works today may become suboptimal in future iterations. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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January 2, 2025in Data
AI platform for automated task management
18Nov

AI platform for automated task management

Salesforce Doubles Down on AI Innovation with Agentforce Salesforce, renowned for its CRM software used by over 150,000 businesses, including Amazon and Walmart, continues to push the boundaries of innovation. Beyond its flagship CRM, Salesforce also owns Slack, the popular workplace communication app. Now, the company is taking its AI capabilities to the next level with Agentforce—a platform that empowers businesses to build and deploy AI-powered digital agents for automating tasks such as creating sales reports and summarizing Slack conversations. What Problem Does Agentforce Solve? Salesforce has been leveraging AI for years, starting with the launch of Einstein in 2016. Einstein’s initial capabilities were limited to basic, scriptable tasks. However, the rise of generative AI created an opportunity to tackle more complex challenges, enabling tools to make smarter decisions and interpret natural language. This evolution led to a series of innovations—Einstein GPT, Einstein Copilot, and now Agentforce—a flexible platform offering prebuilt and customizable agents designed to meet diverse business needs. “Our customers wanted more. Some wanted to tweak the agents we offer, while others wanted to create their own,” said Tyler Carlson, Salesforce’s VP of Business Development. The Technology Behind Agentforce Agentforce is powered by Salesforce’s Atlas Reasoning Engine, developed in-house to drive smarter decision-making. The platform integrates with AI models from leading providers like OpenAI, Anthropic, Amazon, and Google, offering businesses a variety of tools to choose from. Slack, which Salesforce acquired in 2021, plays a pivotal role as a testing ground for these AI agents. Currently in beta, Agentforce’s Slack integration allows businesses to implement automations directly where employees work, enhancing usability. “Slack makes these tools easy to use and accessible,” Carlson noted. How Agentforce Stands Out Customizing AI for Business Needs With tools like Agentbuilder, businesses can create AI agents tailored to specific tasks. For instance, an agent could prioritize and sort incoming emails, respond to HR inquiries, or handle customer support using internal data. One standout example is Salesforce’s partnership with Workday to develop an AI-powered service agent for employee questions. Driving Results and Adoption Salesforce has already seen promising results from early trials, with Agentforce resolving 90% of customer inquiries autonomously. The company aims to expand adoption and functionality, allowing these agents to handle even larger workloads. “We’re building a bigger ecosystem of partners and skills,” Carlson emphasized. “By next year, we want Agentforce to be a must-have for businesses.” With Agentforce, Salesforce continues to cement its role as a leader in AI innovation, helping businesses work smarter, faster, and more effectively. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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November 18, 2024in Agentforce Platform, Data, Generative AI, Google, Salesforce, Technology
being ai-driven
01Nov

Being AI-Driven

Imagine a company where every decision, strategy, customer interaction, and routine task is enhanced by AI. From predictive analytics uncovering market insights to intelligent automation streamlining operations, this AI-driven enterprise represents what a successful business could look like. Does this company exist? Not yet, but the building blocks for creating it are already here. To envision a day in the life of such an AI enterprise, let’s fast forward to the year 2028 and visit Tectonic 5.0, a fictional 37-year-old mid-sized company in Oklahoma that provides home maintenance services. After years of steady sales and profit growth, the 2,300-employee company has hit a rough patch. Tectonic 5.0’s revenue grew just 3% last year, and its 8% operating margin is well below the industry benchmark. To jumpstart growth, Tectonic 5.0 has expanded its product portfolio and decided to break into the more lucrative commercial real estate market. But Tectonic 5.0 needs to act fast. The firm must quickly bring its new offerings to market while boosting profitability by eliminating inefficiencies and fostering collaboration across teams. To achieve these goals, Tectonic 5.0 is relying on artificial intelligence (AI). Here’s how each department at Tectonic 5.0 is using AI to reach these objectives. Spot Inefficiencies with AI With a renewed focus on cost-cutting, Tectonic 5.0 needed to identify and eliminate inefficiencies throughout the company. To assist in this effort, the company developed a tool called Jenny, an AI agent that’s automatically invited to all meetings. Always listening and analyzing, Jenny spots problems and inefficiencies that might otherwise go unnoticed. For example, Jenny compares internal data against industry benchmarks and historical data, identifying opportunities for optimization based on patterns in spending and resource allocation. Suggestions for cost-cutting can be offered in real time during meetings or shared later in a synthesized summary. AI can also analyze how meeting time is spent, revealing if too much time is wasted on non-essential issues and suggesting ways to have more constructive meetings. It does this by comparing meeting summaries against the company’s broader objectives. Tectonic 5.0’s leaders hope that by highlighting inefficiencies and communication gaps with Jenny’s help, employees will be more inclined to take action. In fact, it has already shown considerable promise, with employees being five times more likely to consider cost-cutting measures suggested by Penny. Market More Effectively with AI With cost management underway, Tectonic 5.0’s next step in its transformation is finding new revenue sources. The company has adopted a two-pronged approach: introducing a new lineup of products and services for homeowners, including smart home technology, sustainable living solutions like solar panels, and predictive maintenance on big-ticket systems like internet-connected HVACs; and expanding into commercial real estate maintenance. Smart home technology is exactly what homeowners are looking for, but Tectonic 5.0 needs to market it to the right customers, at the right time, and in the right way. A marketing platform with built-in AI capabilities is essential for spreading the word quickly and effectively about its new products. To start, the company segments its audience using generative AI, allowing marketers to ask the system, in natural language, to identify tech-savvy homeowners between the ages of 30 and 60 who have spent a certain amount on home maintenance in the last 18 months. This enables more precise audience targeting and helps marketing teams bring products to market faster. Previously, segmentation using legacy systems could take weeks, with marketing teams relying on tech teams for an audience breakdown. Now, Tectonic 5.0 is ready to reach out to its targeted customers. Using predictive AI, it can optimize personalized marketing campaigns. For example, it can determine which customers prefer to be contacted by text, email, or phone, the best time of day to reach out, and how often. The system also identifies which messaging—focused on cost savings, environmental impact, or preventative maintenance—will resonate most with each customer. This intelligence helps Tectonic 5.0 reach the optimal customer quickly in a way that speaks to their specific needs and concerns. AI also enables marketers to monitor campaign performance for red flags like decreasing open rates or click-through rates and take appropriate action. Sell More, and Faster, with AI With interested buyers lined up, it’s now up to the sales team to close deals. Generative AI for sales, integrated into CRM, can speed up and personalize the sales process for Tectonic 5.0 in several ways. First, it can generate email copy tailored to products and services that customers are interested in. Tectonic 5.0’s sales reps can prompt AI to draft solar panel prospecting emails. To maximize effectiveness, the system pulls customer info from the CRM, uncovering which emails have performed well in the past. Second, AI speeds up data analysis. Sales reps spend a significant amount of time generating, pulling, and analyzing data. Generative AI can act like a digital assistant, uncovering patterns and relationships in CRM data almost instantaneously, guiding Tectonic 5.0’s reps toward high-value deals most likely to close. Machine learning increases the accuracy of lead scoring, predicting which customers are most likely to buy based on historical data and predictive analytics. Provide Better Customer Service with AI Tectonic 5.0’s new initiatives are progressing well. Costs are starting to decrease, and sales of its new products are growing faster than expected. However, customer service calls are rising as well. Tectonic 5.0 is committed to maintaining excellent customer service, but smart home technology presents unique challenges. It’s more complex than analog systems, and customers often need help with setup and use, raising the stakes for Tectonic 5.0’s customer service team. The company knows that customers have many choices in home maintenance providers, and one bad experience could drive them to a competitor. Tectonic 5.0’s embedded AI-powered chatbots help deliver a consistent and delightful autonomous customer service experience across channels and touchpoints. Beyond answering common questions, these chatbots can greet customers, serve up knowledge articles, and even dispatch a field technician if needed. In the field, technicians can quickly diagnose and fix problems thanks to LLMs like xGen-Small, which

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November 1, 2024in Generative AI
Databricks Tools
04Oct

Databricks Tools

Databricks recently introduced Databricks Apps, a toolkit designed to simplify AI and data application development. By integrating native development platforms and offering automatic provisioning of serverless compute, the toolkit enables customers to more easily develop and deploy applications. Databricks Apps builds on the existing capabilities of Mosaic AI, which allows users to integrate large language models (LLMs) with their enterprise’s proprietary data. However, the ability to develop interactive AI applications, such as generative AI chatbots, was previously missing. Databricks Apps addresses this gap, allowing developers to build and deploy custom applications entirely within the secure Databricks environment. According to Donald Farmer, founder and principal of TreeHive Strategy, Databricks Apps removes obstacles like the need to set up separate infrastructure for development and deployment, making the process easier and more efficient. The new features allow companies to go beyond implementing AI/ML models and create differentiated applications that leverage their unique data sets. Kevin Petrie, an analyst at BARC U.S., highlighted the significance of Databricks Apps in helping companies develop custom AI applications, which are essential for maintaining a competitive edge. Databricks, founded in 2013, was one of the pioneers of the data lakehouse storage format, and over the last two years, it has expanded its platform to focus on AI and machine learning (ML) capabilities. The company’s $1.3 billion acquisition of MosaicML in June 2023 was a key milestone in building its AI environment. Databricks has since launched DBRX, its own large language model, and introduced further functionalities through product development. Databricks Apps, now available in public preview on AWS and Azure, advances these AI development capabilities, simplifying the process of building applications within a single platform. Developers can use frameworks like Dash, Flask, Gradio, Shiny, and Streamlit, or opt for integrated development environments (IDEs) like Visual Studio Code or PyCharm. The toolkit also provides prebuilt Python templates to accelerate development. Additionally, applications can be deployed and managed directly in Databricks, eliminating the need for external infrastructures. Databricks Apps includes security features such as access control and data lineage through the Unity Catalog. Farmer noted that the support for popular developer frameworks and the automatic provisioning of serverless compute could significantly impact the AI development landscape by reducing the complexity of deploying data architectures. While competitors like AWS, Google Cloud, Microsoft, and Snowflake have also made AI a key focus, Farmer pointed out that Databricks’ integration of AI tools into a unified platform sets it apart. Databricks Apps further enhances this competitive advantage. Despite the added capabilities of Databricks Apps, Petrie cautioned that developing generative AI applications still requires a level of expertise in data, AI, and the business domain. While Databricks aims to make AI more accessible, users will still need substantial knowledge to effectively leverage these tools. Databricks’ vice president of product management, Shanku Niyogi, explained that the new features in Databricks Apps were driven by customer feedback. As enterprise interest in AI grows, customers sought easier ways to develop and deploy internal data applications in a secure environment. Looking ahead, Databricks plans to continue investing in simplifying AI application development, with a focus on enhancing Mosaic AI and expanding its collaborative AI partner ecosystem. Farmer suggested that the company should focus on supporting nontechnical users and emerging AI technologies like multimodal models, which will become increasingly important in the coming years. The introduction of Databricks Apps marks a significant step forward in Databricks’ AI and machine learning strategy, offering users a more streamlined approach to building and deploying AI applications. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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October 4, 2024in AI Tools, Data, Enterprise, Generative AI, Google, Snowflake
Salesforce Einstein Copilot Security
27Sep

Salesforce Einstein Copilot Security

Salesforce Einstein Copilot Security: How It Works and Key Risks to Mitigate for a Safe Rollout With the official rollout of Salesforce Einstein Copilot, this conversational AI assistant is set to transform how sales, marketing, and customer service teams interact with both customers and internal documentation. Einstein Copilot understands natural language queries, streamlining daily tasks such as answering questions, generating insights, and performing actions across Salesforce to boost productivity. Salesforce Einstein Copilot Security However, alongside the productivity gains, it’s essential to address potential risks and ensure a secure implementation. This Tectonic insight covers: Einstein Copilot Use Cases Einstein Copilot enables users to: All of these actions can be performed with simple, natural language prompts, improving efficiency and outcomes. How Einstein Copilot Works Here’s a simplified breakdown of how Einstein Copilot processes prompts: The Einstein Trust Layer Salesforce has built the Einstein Trust Layer to ensure customer data is secure. Customer data processed by Einstein Copilot is encrypted, and no data is retained on the backend. Sensitive data, such as PII (Personally Identifiable Information), PCI (Payment Card Information), and PHI (Protected Health Information), is masked to ensure privacy. Additionally, the Trust Layer reduces biased, toxic, and unethical outputs by leveraging toxic language detection. Importantly, Salesforce guarantees that customer data will not be used to train the AI models behind Einstein Copilot or be shared with third parties. The Shared Responsibility Model Salesforce’s security approach is based on a shared responsibility model: This collaborative model ensures a higher level of security and trust between Salesforce and its customers. Best Practices for Securing Einstein Copilot Rollout Prepare Your Salesforce Org for Einstein Copilot To ensure a smooth rollout, it’s critical to assess your Salesforce security posture and ready your data. Tools like Salesforce Shield can help organizations by: By following these steps, you can utilize the power of Einstein Copilot while ensuring the security and integrity of your data. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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September 27, 2024in Data, Enterprise, Salesforce, Salesforce Data Cloud, Salesforce Einstein

AI Agents

Lessons Learned in the First Year of Developing AI Agents In the first year of working on AI agents, valuable insights emerged from direct collaboration with engineers and UX designers, as they iterated on the overall product experience. The objective was to create a platform for customers to use standard data analysis agents and build custom agents tailored to specific tasks and data structures relevant to their business. This platform integrates connectors to databases like Snowflake and BigQuery with built-in security, supports RAG over a metadata layer describing database contents, and facilitates data analysis through SQL, Python, and data visualization tools. Feedback on the effectiveness of these developments came from both internal evaluations and customer insights. Users from Fortune 500 companies utilize these agents daily to analyze their internal data. Key Insights on AI Agents Additional Insights Further insights on code and infrastructure include: These lessons underscore the importance of focusing on reasoning, iterative improvements to the agent-computer interface, understanding model limitations, and building robust supporting infrastructure to enhance AI agent performance and user satisfaction. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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June 4, 2024in Salesforce Einstein
Data Cloud Free Licenses
15Sep

Data Cloud Free Licenses

Salesforce Announces Data Cloud Free Licenses at Dreamforce 2023 At Dreamforce 2023, Salesforce announced that free Data Cloud licenses are now included for all Enterprise Edition or above customers to help them familiarize themselves with new capabilities and develop use case ideas. Starting September 19th, 2023, Enterprise Edition and above customers can get started with Data Cloud Provisioning at no cost by signing up under Your Account. Data Cloud Provisioning includes: Unlimited Plus Edition customers will get access to 2,500,000 Data Service credits. Two Tableau Creator licenses are a separate line item and can be quoted by your Salesforce Account Executive. Salesforce has been focusing on large data and AI tools for several years, acquiring Tableau, accelerating their Einstein AI tools, and significantly extending the Data Cloud product. Data Cloud allows you to easily harmonize data, analyze it in Tableau, and make it actionable across marketing, sales, and service. What Can I Do with Data Cloud? Data Cloud enables customers to start with one of three use cases: Across these use cases, customers can ingest data from multiple sources, unify data with identity resolution, calculate insights, visualize data in Tableau (with the provisioning of the Tableau Cloud – Creator for Data Cloud SKU), and view consolidated data on the contact record. Differences Between Data Cloud and Data Cloud Provisioning Functionality: Data Cloud Provisioning includes all the features of the existing Data Cloud offerings, except Segmentation and Activation. Credits for Segmentation and Activation can be purchased as add-ons through Marketing Cloud account teams. Capacity: Both include 1 TB of data storage, 1 Data Cloud admin, 100 internal Data Cloud identity users, 1,000 Data Cloud PSL, and 5 integration users. Entitlement: Data Cloud Provisioning entitlement is the same for all Enterprise Edition and above customers. Additional Information Sandbox Availability: Data Cloud is not available in Sandbox orgs; it can only be provisioned to an existing production org. Professional Edition Access: Data Cloud Provisioning is not available to Professional Edition customers. Existing Data Cloud or CDP Customers: Those with an existing Data Cloud or CDP tenant cannot sign up for Data Cloud Provisioning. Unlimited Edition Plus Bundle Customers: Data Cloud Provisioning is not available, as the bundle includes a Data Cloud tenant. Edition Information: Check your Salesforce org’s edition in Setup > Company Information > Organization Edition. Government Cloud: Data Cloud Provisioning is not available. Non-Profit Customers: Data Cloud Provisioning is available. Industry Cloud Customers: Industry Cloud customers with Enterprise Edition and above are eligible. ISV Partners: Data Cloud Provisioning is not accessible via Your Account in ISV Enterprise Edition orgs. ISV Partners need to create a support case with the Partner Ops team to request provisioning. Existing Tableau Customers: Tableau Cloud – Creator for Data Cloud is intended to provision a new Tableau tenant (aka site). Multiple Instances: Only one Data Cloud Provisioning instance is allowed per account/tenant. Access to Tableau Cloud – Creator for Data Cloud: To get access, you must have or include on the same quote any of the following: 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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September 15, 2023in Salesforce Data Cloud
Nonprofit
20Jul

Nonprofit Marketers Focus on Data and Innovation

A primary focus on internal data and tools will help nonprofits solve their larger challenges of making decisions and collaborating to provide a better constituent experience. Nonprofit Marketers Focus on Data and Innovation to accomplish this. Nonprofits are prioritizing unifying data sources so that information can be shared across the organization. This makes sense, as without access to data, innovation is difficult. Innovation was their #3 priority and the #1 challenge. Coming in #4 on the priorities list was improving the use of technology, which was #2 for large organizations, and is foundational to every other priority. Aside from the two top internal challenges around innovating and collaboration, the majority focused on a cohesive journey and real-time engagement to keep ahead of rapidly shifting supporter expectations. You can combine these top challenge and priority areas into a plan that helps you make data-driven decisions and drive innovation to improve every part of your cause marketing experience. Although these areas aren’t always linear, each relates, beginning with tools, data, and collaboration internally, so that you can engage with and measure the stakeholder experience externally. Nonprofit Marketers Focus on Data and Innovation with Video Marketers have widely adopted video as the top currently used tactic, however are shifting with trending formats. For example, 42% of nonprofits are planning to adopt Social GIFs and Memes, which was the least currently used response, but particularly popular with the younger generations. Highly personal user-generated content is the second highest increase, with 39% planning to use, followed by influencer marketing, leveraging highly networked individuals to be advocates for their mission. Given that, it makes sense that the top 5 increases in use of channels is video, social, and ads, followed by digital content and nonprofit’s website. Events too are gradually making a return, however, with the rapid shift to virtualized events marketers made in 2020, virtual events are only expected to increase by 6% in 2022. Supporter Communications are Core to their Experience 6 in 10 respondents in the Nonprofit Experience Index survey had supported or benefited from charitable services in the first half of 2021, with 9 in 10 all saying the organization met or even exceeded their expectations. Email was by far the #1 communication channel preferred, however responding to the statement that “The communications I received from the nonprofit were personalized for me”, only 33.6% agree, and 6.4% strongly agree. Additionally, certain people think that nonprofits ask too much of them — 19% say that the organization asks too often for money and 16% say they are asked too often to volunteer. However, similar proportion (18%) would like to be asked to give more often and one quarter to volunteer more (25%). This provides an opportunity for nonprofits to meet people on the channels they prefer, and use data to time and personalize content. Although email still remains a top preferred channel for constituents, this preference shifts per demographic and individual, with increasing numbers each year preferring other channels. Contact Tectonic today for assistance obtaining your free Public Sector/Nonprofit Salesforce org. Content updated November 2023. Like1 Related Posts 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 Marketing Cloud Account Engagement and Salesforce Campaigns The interplay between Account Engagement and Salesforce Campaigns often sparks confusion and frustration among users. In this insight, we’ll demystify Read more Mapping Your Customer Journey Creating a customer journey map is a crucial undertaking for businesses aiming to improve the customer experience and foster long-term Read more Marketing Automation Marketing automation is software tool that handles routine marketing tasks without the need for human action or intervention. Common marketing Read more

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July 20, 2021in Marketing Automation, Salesforce for Nonprofits

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