Predictive AI Archives - gettectonic.com
AI evolves with tools like Agentforce and Atlas
01Jun

How the Atlas Reasoning Engine Powers Agentforce

Autonomous, proactive AI agents form the core of Agentforce. But how do they operate? A closer look reveals the sophisticated mechanisms driving their functionality. The rapid pace of AI innovation—particularly in generative AI—continues unabated. With today’s technical advancements, the industry is swiftly transitioning from assistive conversational automation to role-based automation that enhances workforce capabilities. For artificial intelligence (AI) to achieve human-level performance, it must replicate what makes humans effective: agency. Humans process data, evaluate potential actions, and execute decisions. Equipping AI with similar agency demands exceptional intelligence and decision-making capabilities. Salesforce has leveraged cutting-edge developments in large language models (LLMs) and reasoning techniques to introduce Agentforce—a suite of ready-to-use AI agents designed for specialized tasks, along with tools for customization. These autonomous agents can think, reason, plan, and orchestrate with remarkable sophistication, marking a significant leap in AI automation for customer service, sales, marketing, commerce, and beyond. Agentforce: A Breakthrough in AI Reasoning Agentforce represents the first enterprise-grade conversational automation solution capable of proactive, intelligent decision-making at scale with minimal human intervention. Several key innovations enable this capability: Additional Differentiators of Agentforce Beyond the Atlas Reasoning Engine, Agentforce boasts several distinguishing features: The Future of Agentforce Though still in its early stages, Agentforce is already transforming businesses for customers like Wiley and Saks Fifth Avenue. Upcoming innovations include: The Third Wave of AI Agentforce heralds the third wave of AI, surpassing predictive AI and copilots. These agents don’t just react—they anticipate, plan, and reason autonomously, automating entire workflows while ensuring seamless human collaboration. Powered by the Atlas Reasoning Engine, they can be deployed in clicks to revolutionize any business function. The era of autonomous AI agents is here. Are you ready? 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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June 1, 2025in Agentforce Platform, Artificial Intelligence, Data, Enterprise, Generative AI, Salesforce
Real-World AI
29Apr

AI in the Travel Industry

AI in Travel: How the Industry is Transforming with Intelligent Technology The travel sector has long been at the forefront of AI adoption, with airlines, hotels, and cruise lines leveraging advanced analytics for decades to optimize pricing and operations. Now, as artificial intelligence evolves—particularly with the rise of generative AI—the industry is entering a new era of smarter automation, hyper-personalization, and seamless customer experiences. “AI and generative AI have emerged as truly disruptive forces,” says Kartikey Kaushal, Senior Analyst at Everest Group. “They’re reshaping how travel businesses operate, compete, and serve customers.” According to Everest Group, AI adoption in travel is growing at 14-16% annually, driven by demand for efficiency and enhanced customer engagement. But as adoption accelerates, the industry must balance automation with the human touch that travelers still value. 10 Key AI Use Cases in Travel & Tourism 1. Dynamic Pricing Optimization Travel companies pioneered AI-driven dynamic pricing, adjusting fares based on demand, competitor rates, weather, and events. Now, AI takes it further with hyper-personalized pricing—tracking user behavior (like repeated searches) to offer tailored deals. 2. Customer Sentiment Analysis AI evaluates traveler emotions through voice tone, reviews, and social media, enabling real-time adjustments. Hotels and airlines use sentiment tracking to improve service before complaints escalate. 3. Automated Office Tasks Travel agencies use generative AI (like ChatGPT) to draft emails, marketing content, and customer onboarding materials, freeing staff for high-value interactions. 4. Self-Service & Customer Empowerment AI-powered chatbots, itinerary builders, and booking tools let travelers plan trips independently. Some even bring AI-generated plans to agents for refinement—blending automation with human expertise. 5. Operational Efficiency & Asset Management Airlines and cruise lines deploy AI for:✔ Predictive maintenance (reducing downtime)✔ Route optimization (cutting fuel costs)✔ Staff scheduling (improving productivity) 6. AI-Powered Summarization Booking platforms use generative AI to summarize hotel reviews, local attractions, and FAQs—delivering concise, personalized travel insights. 7. Frictionless Travel Experiences From contactless hotel check-ins to AI-driven real-time recommendations (restaurants, shows, transport), AI minimizes hassles and enhances convenience. 8. AI Agents for Problem-Solving Agentic AI autonomously resolves disruptions—like rebooking flights, rerouting luggage, and updating hotels—without human intervention. 9. Enhanced Personalization Without “Creepiness” AI tailors recommendations based on past behavior but must avoid overstepping. The challenge? “A customer segment of one”—balancing customization with privacy. 10. Risk & Compliance Management AI helps navigate data privacy laws (GDPR, CCPA) and detects fraud, but companies must assign clear accountability for AI-driven decisions. Challenges in AI Adoption for Travel The Future: AI + Human Collaboration The most successful travel companies will blend AI efficiency with human empathy, ensuring technology enhances—not replaces—the art of travel. “The goal isn’t full automation,” says McKinsey’s Alex Cosmas. “It’s using AI to make every journey smoother, smarter, and more personal.” As AI evolves, so will its role in travel—ushering in an era where smarter algorithms and human expertise work together to create unforgettable experiences. What’s Next? The journey has just begun. 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 29, 2025in Agentic AI, Artificial Intelligence, Data, Generative AI, Laws, Technology
Commerce Cloud and Agentic AI
08Apr

Generative AI in Marketing

Generative AI in Marketing: Balancing Innovation and Risk Generative AI (gen AI) has become a disruptive force in the marketplace, particularly in marketing, where its ability to create content—from product descriptions to personalized ads—has reshaped strategies. According to Salesforce’s State of Marketing report, which surveyed 5,000 marketers worldwide, implementing AI is now their top priority. Some companies, like Vanguard and Unilever, have already seen measurable benefits, with Vanguard increasing LinkedIn ad conversions by 15% and Unilever cutting customer service response times by 90%. Yet, despite 96% of marketers planning to adopt gen AI within 18 months, only 32% have fully integrated it into their operations. This gap highlights the challenges of implementation—balancing efficiency with risks like inauthenticity or errors. For instance, Coca-Cola’s AI-generated holiday ad initially drew praise but later faced backlash for its perceived lack of emotional depth. The Strategic Dilemma: How, Not If, to Use Gen AI Many Chief Data and Analytics Officers (CDAOs) have yet to formalize gen AI strategies, leading to fragmented experimentation across teams. Based on discussions with over 20 industry leaders, successful adoption hinges on three key decisions: To answer these, companies must assess: Gen AI vs. Analytical AI: Choosing the Right Tool Analytical AI excels at predictions—forecasting customer behavior, pricing sensitivity, or ad performance. For example, Kia once used IBM Watson to identify brand-aligned influencers, a strategy still relevant today. Generative AI, on the other hand, creates new content—ads, product descriptions, or customer service responses. While analytical AI predicts what a customer might buy, gen AI crafts the persuasive message around it. The most effective strategies combine both: using analytical AI to identify the “next best offer” and gen AI to personalize the pitch. Custom vs. General Inputs: Striking the Balance Gen AI models can be trained on: For broad applications like customer service chatbots, general models (e.g., ChatGPT) work well. But for brand-specific needs—like ad copy or legal disclaimers—custom-trained models (e.g., BloombergGPT for finance or Jasper for marketing) reduce errors and intellectual property risks. Human Oversight: How Much Is Enough? The level of human review depends on risk tolerance: Air Canada learned this the hard way when its AI chatbot mistakenly promised a bereavement discount—a pledge a court later enforced. While human review slows output, it mitigates costly errors. A Framework for Implementation To navigate these trade-offs, marketers can use a quadrant-based approach: Input Type No Human Review Human Review Required General Data Fast, low cost, high risk Higher accuracy, slower output (e.g., review summaries) (e.g., social media posts) Custom Data Lower privacy risk, higher cost Highest accuracy, highest cost (e.g., in-store product locator) (e.g., SEC filings) The Path Forward Gen AI is not a one-size-fits-all solution. Marketers must weigh speed, cost, accuracy, and risk for each use case. While technology will evolve, today’s landscape demands careful strategy—blending gen AI’s creativity with analytical AI’s precision and human judgment’s reliability. The question is no longer whether to adopt gen AI, but how to harness its potential without falling prey to its pitfalls. Companies that strike this balance will lead the next wave of marketing innovation. 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 8, 2025in Data, Generative AI, Salesforce, Technology
AI Agents Are About to Disrupt Your Marketing Channels
01Apr

AI Agents Are About to Disrupt Your Marketing Channels

AI Agents Are About to Disrupt Your Marketing Channels—Here’s How to Adapt The Future of Marketing Isn’t Human-Centric—It’s Agent-Driven AI agents are poised to revolutionize how brands and consumers interact. These autonomous systems don’t just assist—they research, decide, and transact on behalf of users, fundamentally altering the role of traditional marketing channels. Google knows this. That’s why it’s replacing traditional search with Gemini, an AI agent that delivers answers, not just links. Meta is integrating AI across WhatsApp and Messenger, enabling two-way, large-scale brand interactions. Soon, every channel—email, social, loyalty programs, even your website—will become an AI-powered research and transaction hub. The question isn’t if this will impact your marketing strategy—it’s how soon. What Are AI Agents—And Why Should Marketers Care? AI agents are the next evolution of autonomous AI, combining:✅ Generative AI (content creation, personalization)✅ Predictive AI (data-driven decision-making)✅ Complex task execution (end-to-end customer journeys) Today’s challenge? Most companies struggle to move from AI experimentation to real-world impact. Agents change that—they bridge the gap between hype and execution, turning AI potential into measurable business results. 3 Ways to Future-Proof Your Channel Strategy 1. Build a Bulletproof Data Foundation (Now) AI agents won’t just use data—they’ll demand it to make decisions for customers. 🔹 Example: A customer asks an agent, “Find me the best CRM for small businesses.”🔹 Without structured data: The agent may overlook your product.🔹 With optimized data: Your CRM appears as a top recommendation, complete with pricing, features, and a seamless sign-up link. Action Step: Audit your product data, pricing, and USPs. Ensure they’re machine-readable and easily accessible to AI-driven platforms. 2. Rethink “Channels” as AI Conversation Hubs Traditional marketing funnels (search → browse → convert) will collapse. Instead: Action Step: Optimize for AI-native experiences—structured FAQs, API-accessible pricing, and instant conversion paths. 3. Prepare for AI-to-AI Negotiation B2B and high-consideration purchases (e.g., SaaS, automotive, real estate) will see AI agents negotiating deals on behalf of users. 🔹 Example: A corporate procurement AI evaluates your software against competitors, automatically requesting discounts or custom terms.🔹 Winners will be brands that enable AI-friendly decision-making (clear pricing, comparison data, instant approvals). Action Step: Develop agent-friendly sales collateral—dynamic pricing tables, competitor comparisons, and API-driven contract automation. The Bottom Line: Adapt or Get Displaced The shift to agent-driven marketing isn’t gradual—it’s exponential. Companies that wait will find themselves invisible to AI intermediaries shaping customer decisions. Your roadmap: The future belongs to marketers who design for AI-first experiences. The time to act is now. “AI agents won’t just change marketing—they’ll redefine it. The brands that win will be those that engineer their systems for machines, not just people.”—Salesforce AI Research, 2024 Ready to future-proof your strategy? Contact Tectonic. 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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April 1, 2025in Agentforce Platform, Data, Generative AI, Google, Salesforce, Salesforce Implementation Services, Salesforce Marketing Automation

Data Cloud Billable Usage

Data Cloud Billable Usage Overview Usage of certain Data Cloud features impacts credit consumption. To track usage, access your Digital Wallet within your Salesforce org. For specific billing details, refer to your contract or contact your Account Executive. Important Notes ⚠️ Customer Data Platform (CDP) Licensing – If your Data Cloud org operates under a CDP license, refer to Customer Data Platform Billable Usage Calculations instead.⚠️ Sandbox Usage – Data Cloud sandbox consumption affects credits, with usage tracked separately on Data Cloud sandbox cards. Understanding Usage Calculations Credit consumption is based on the number of units used multiplied by the multiplier on the rate card for that usage type. Consumption is categorized as follows: 1. Data Service Usage Service usage is measured by records processed, queried, or analyzed. Billing Category Description Batch Data Pipeline Based on the volume of batch data processed via Data Cloud data streams. Batch Data Transforms Measured by the higher of rows read vs. rows written. Incremental transforms only count changed rows after the first run. Batch Profile Unification Based on source profiles processed by an identity resolution ruleset. After the first run, only new/modified profiles are counted. Batch Calculated Insights Based on the number of records in underlying objects used to generate Calculated Insights. Data Queries Based on records processed, which depends on query structure and total records in the queried objects. Unstructured Data Processed Measured by the amount of unstructured data (PDFs, audio/video files) processed. Streaming Data Pipeline Based on records ingested through real-time data streams (web, mobile, streaming ingestion API). Streaming Data Transforms Measured by the number of records processed in real-time transformations. Streaming Calculated Insights Usage is based on the number of records processed in streaming insights calculations. Streaming Actions (including lookups) Measured by the number of records processed in data lookups and enrichments. Inferences Based on predictive AI model usage, including one prediction, prescriptions, and top predictors. Applies to internal (Einstein AI) and external (BYOM) models. Data Share Rows Shared (Data Out) Based on the new/changed records processed for data sharing. Data Federation or Sharing Rows Accessed Based on records returned from external data sources. Only cross-region/cross-cloud queries consume credits. Sub-second Real-Time Events & API Based on profile events, engagement events, and API calls in real-time processing. Private Connect Data Processed Measured by GB of data transferred via private network routes. 🔹 Retired Billing Categories: Accelerated Data Queries and Real-Time Profile API (no longer billed after August 16, 2024). 2. Data Storage Allocation Storage usage applies to Data Cloud, Data Cloud for Marketing, and Data Cloud for Tableau. Billing Category Description Storage Beyond Allocation Measured by data storage exceeding your allocated limit. 3. Data Spaces Billing Category Description Data Spaces Usage is based on the number of data spaces beyond the default allocation. 4. Segmentation & Activation Usage applies to Data Cloud for Marketing customers and is based on records processed, queried, or activated. Billing Category Description Segmentation Based on the number of records processed for segmentation. Batch Activations Measured by records processed for batch activations. Activate DMO – Streaming Based on new/updated records in the Data Model Object (DMO) during an activation. If a data graph is used, the count is doubled. 5. Ad Audiences Service Usage Usage is calculated based on the number of ad audience targets created. Billing Category Description Ad Audiences Measured by the number of ad audience targets generated. 6. Data Cloud Real-Time Profile Real-time service usage is based on the number of records associated with real-time data graphs. Billing Category Description Sub-second Real-Time Profiles & Entities Based on the unique real-time data graph records appearing in the cache during the billing month. Each unique record is counted only once, even if it appears multiple times. 📌 Example: If a real-time data graph contains 10M cached records on day one, and 1M new records are added daily for 30 days, the total count would be 40M records. 7. Customer Data Platform (CDP) Billing Previously named Customer Data Platform orgs are billed based on contracted entitlements. Understanding these calculations can help optimize data management and cost efficiency. Track & Manage Your Usage 🔹 Digital Wallet – Monitor Data Cloud consumption across all categories.🔹 Feature & Usage Documentation – Review guidelines before activating features to optimize cost.🔹 Account Executive Consultation – Contact your AE to understand credit consumption and scalability options. 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 26, 2025in Data, Salesforce, Salesforce Data Cloud
copilots and agentic ai
03Feb

Transforming Industries and Redefining Workflows

The Rise of Agentic AI: Transforming Industries and Redefining Workflows Artificial Intelligence (AI) is evolving faster than we anticipated. No longer limited to predicting outcomes or generating content, AI systems are now capable of handling complex tasks and making autonomous decisions. This new era—driven by Agentic AI—is set to redefine the workplace and transform industries. From Prediction to Autonomy: The Three Waves of AI To understand where we’re headed, it’s important to see how far AI has come. Arun Parameswaran, SVP & MD of Salesforce India, describes it as a fundamental shift: “What has changed with agents is their ability to handle complex reasoning… and, most importantly, to take action.” Unlike previous AI models that recommend or predict, Agentic AI executes tasks, reshaping customer experiences and operational workflows. Agentic AI in Action: Industry Applications At a recent Mint x Salesforce India deep-dive event on AI, industry leaders explored how Agentic AI is driving transformation across sectors. The panel featured: Here’s how Agentic AI is already making an impact: 1. Revolutionizing Customer Support Traditional chatbots have limited capabilities. Agentic AI, however, understands urgency and context. 2. Accelerating Business Decisions In finance and supply chain management, AI agents analyze vast amounts of data and execute decisions autonomously. 3. Transforming Travel & Aviation Airlines are leveraging AI to optimize booking systems, reduce costs, and enhance efficiency. 4. Automating Wealth Management AI agents in financial services monitor markets, adjust strategies, and offer personalized investment recommendations in real time. The Risks & Responsibilities of Agentic AI With great autonomy comes great responsibility. The potential of Agentic AI is vast—but so are the challenges: The Future of Work: AI as a Partner, Not a Replacement Despite concerns about job displacement, AI is more likely to reshape rather than replace roles. What Are AI Agents? AI agents go beyond traditional models like ChatGPT or Gemini. They are proactive, self-learning systems that: They fall into two categories: “AI agents don’t just wait for commands; they anticipate needs and act,” says Dr. Tomer Simon, Chief Scientist at Microsoft Research Israel. AI Agents in the Workplace: A Shift in Roles AI agents streamline processes, but they don’t eliminate the need for human oversight. Salesforce’s Agentforce is a prime example: “Companies need to integrate AI, not fear it. Those who fail to adopt AI tools risk drowning in tasks AI can handle,” warns Dr. Omri Allouche, Chief Scientist at Gong. The Road Ahead: AI-Driven Business Growth Agentic AI is not about replacing people—it’s about empowering them. As organizations re-evaluate workflows and embrace AI collaboration, the companies that act early will gain a competitive edge in efficiency and innovation. Final Thought The AI revolution is here, and Agentic AI is at its forefront. The key question isn’t whether AI will transform industries—it’s how organizations will adapt and thrive in this new era. 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 3, 2025in Agentic AI, AI Tools, Artificial Intelligence, Data, Generative AI, Google, Laws, Salesforce, Technology
ai in marketing
27Jan

Guide to AI in Marketing

The Ultimate Guide to AI in Marketing AI-powered algorithms and machine learning are revolutionizing the marketing landscape by enabling swift processing and analysis of vast datasets. Unlike traditional methods, AI efficiently organizes large volumes of data in real time, redefining how marketing strategies are created and executed. Marketing success hinges on effective data utilization, precise targeting, engaging content, and seamless workflows. AI simplifies these complexities, making them more accessible, scalable, and impactful. Here’s how AI transforms modern marketing. Unleashing AI’s Potential in Marketing AI has become a cornerstone for enhancing customer experiences and boosting marketing productivity. However, to fully leverage AI, it’s essential to understand its capabilities and implementation strategies. Think of AI as your vehicle for uncovering actionable customer insights, optimizing campaigns, and creating tailored customer experiences. While the pace of AI’s evolution may seem overwhelming, this guide will help you take control and confidently drive your AI-powered marketing efforts. Future Trends in Generative AI and Marketing Generative AI is unlocking new possibilities in customer engagement. This guide explores the challenges, advantages, and emerging trends in AI-driven marketing. From attracting customers to maximizing ROI, you’ll discover best practices and real-world examples of successful AI adoption. How AI Works in Marketing AI uses advanced algorithms and pattern recognition to simulate human intelligence in processing data. Through machine learning and deep learning, it identifies trends, predicts outcomes, and automates tasks typically requiring human intervention. Like humans learning from experience, AI improves with practice. It rapidly identifies consumer preferences, behaviors, and purchasing patterns. Two primary types of AI stand out in marketing: These AI types work together—predictive AI extracts insights from data, while generative AI uses those insights to create personalized content and solutions. This synergy enables marketers to automate tasks, segment audiences, and deliver tailored messaging based on individual preferences. AI in Action: Enhancing Customer Engagement AI enables marketers to engage with customers more effectively by: The Power of AI-Driven Marketing Analytics AI-powered analytics revolutionize decision-making by identifying patterns and offering actionable insights. Marketers can use AI tools to: Maximizing ROI with AI AI enables businesses to expand audience reach, improve conversion rates, and enhance customer relationships through personalized content and product recommendations. Its real-time analytics empower marketers to make informed decisions, while automation frees up time for strategic innovation. Navigating Challenges in AI Marketing AI’s potential comes with challenges, including: By prioritizing ethical practices, transparent data policies, and robust compliance measures, marketers can overcome these obstacles and leverage AI responsibly. Best Practices for AI-Driven Marketing To maximize the benefits of AI, marketers should: The Future: AI Copilots in Marketing AI copilots—conversational AI integrated into platforms—are transforming marketing workflows. These tools draft content, provide recommendations, and offer guidance based on CRM data, significantly enhancing efficiency. Looking Ahead: Emerging Trends in AI Marketing Over the next two years, advancements in AI will continue to reshape marketing. Key trends include: By embracing these advancements, marketers can deliver exceptional customer experiences, drive business growth, and stay competitive in an evolving digital landscape. AI is not just a tool—it’s a transformative force. By integrating AI into your marketing strategy, you can unlock unparalleled opportunities to engage customers, optimize campaigns, and propel your organization into the future. 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, 2025in AI Tools, Data, Generative AI
AI evolves with tools like Agentforce and Atlas
13Jan

Agentforce Powered Marketing

Maximize Team Productivity and Customer Engagement with Agentforce and AI-Powered Marketing Tools Transform your marketing operations with Agentforce, an advanced AI-powered suite seamlessly integrated into your platform. From building end-to-end campaigns to personalizing touchpoints in real time, Agentforce empowers your team to optimize performance with actionable AI insights. Here’s how: Revolutionize Campaign Management with Agentforce Agent-Driven Campaign Briefs Streamline campaign creation with Agentforce, which uses structured and unstructured data from Data Cloud to create tailored campaign briefs. Define your target segments and key messages effortlessly with the support of AI. AI-Powered Content Creation Leverage Agentforce to generate on-brand content at scale, including email subject lines, body copy, and SMS messages. Every piece of content aligns with your brand guidelines and campaign goals, ensuring consistency and relevance across audiences. Unified SMS Conversations Turn static promotions into dynamic, two-way conversations with Agentforce Unified SMS. Automatically connect customers to AI agents for tasks like appointment scheduling and offer redemption, delivering seamless customer experiences. Supercharge Insights and Actions with Data Cloud Agent-Driven AI Segmentation Create target audience segments in minutes using natural language prompts. With Agentforce and Data Cloud working in harmony, agents translate prompts into precise segment attributes—no technical expertise or SQL required. Integrate or Build Custom AI Models Develop predictive AI models with clicks, not code, or bring in existing models via direct integrations with tools like Amazon SageMaker, Google Vertex AI, or Databricks. Use these models to generate actionable predictions, such as purchase propensity or churn likelihood. Secure, Harmonized Data Foundation Keep your data safe on the Einstein Trust Layer while enabling agents to analyze harmonized, structured, and unstructured data in Data Cloud. This ensures informed decision-making without compromising security. Automate Intelligent Journeys with Marketing Cloud Engagement Journey Optimization Automate personalized campaign variations with predictive AI. Optimize engagement by tailoring content, timing, channels, and frequency dynamically across customer journeys. Generative AI for Content Creation Solve the content bottleneck with generative AI tools that instantly create on-brand copy and visuals grounded in first-party data, campaign insights, and brand guidelines—all while safeguarding trust. Real-Time Messaging Insights Stay proactive with Einstein Messaging Insights, which flags engagement anomalies like sudden drops in click-through rates. These real-time insights enable quick resolutions, preventing performance surprises. Unified WhatsApp Conversations Transform WhatsApp into a dynamic two-way engagement channel. Use a single WhatsApp number to connect marketing and service teams while enabling AI-driven self-service actions like appointment booking and offer redemptions. Scale Lead Generation and Account-Based Marketing Agent-Driven Campaign Creation Accelerate campaign planning with Agentforce, which handles everything from briefs to audience segmentation, content, and journey creation. Ground campaigns in real-time customer data for accurate targeting, all with marketer oversight for approvals. AI Lead and Account Scoring Boost alignment between marketing and sales with Einstein AI Scoring, which identifies top leads and prospects automatically. Improve ABM strategies with automated account rankings based on historical and behavioral data, driving higher conversions. Full-Funnel Attribution Gain end-to-end visibility with AI-powered multi-touch attribution. Use models like Einstein Attribution to measure the impact of each channel, event, or team activity on your pipeline, boosting ROI and campaign efficiency. Personalization on Auto-Pilot with AI Objective-Based AI Recommendations Set business objectives and let AI optimize product and content recommendations to achieve those goals. AI-Automated Offers Combine real-time customer behavior data with AI-driven insights to personalize offers across touchpoints. This results in higher satisfaction and conversion rates tailored to each individual customer. Real-Time Affinity Profiling Use AI to uncover customer affinities, preferences, and intent in real time. Deliver hyper-personalized messaging and offers across your website, app, and other channels for maximum engagement. Optimize Spend, Planning, and Performance with Marketing Cloud Intelligence AI-Powered Data Integration Say goodbye to spreadsheets and manual data maintenance. Automate data unification, KPI standardization, and cross-channel analytics with AI-powered connectors, saving time and boosting campaign effectiveness. AI Campaign Performance Insights Get interactive visualizations and AI-generated insights to adjust campaign spend and offers mid-flight. Use these insights to optimize ROI and maximize in-the-moment opportunities. Predictive Budgeting and Planning Allocate budgets more effectively with predictive AI. Real-time alerts help prevent overages or underspending, ensuring your marketing dollars are used efficiently for maximum return. With Agentforce and AI marketing tools, your team can focus on what matters most—building stronger customer relationships and driving measurable results. 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 13, 2025in AI Tools, Data, Generative AI, Google
salesforce agentforce ai powered agentic agents
18Dec

Marketing Agents for Campaigns

Marketing Agents: The Next Frontier in Campaign Creation and Optimization For years, content creation and distribution have been key challenges for marketers. According to recent research from the Content Marketing Institute, 54% of B2B marketers lack the resources needed to produce high-quality content at scale. Similarly, B2C marketers often struggle to create consistent, repeatable, and scalable processes. On top of these challenges, they must balance resource management to ensure campaigns are efficient, impactful, and engaging enough to stand out from the competition. The solution to these challenges lies in marketing agents, powered by data and AI. These intelligent tools streamline content creation, optimize campaigns, and make marketing processes more efficient. At this year’s Dreamforce, Salesforce introduced Agentforce, a suite of tools designed to create, customize, and deploy marketing agents across the Customer 360 platform. As part of this launch, Agentforce Campaigns is already gaining attention from customers eager to harness its potential. According to Salesforce’s 9th State of Marketing Report, 71% of marketers plan to integrate generative and predictive AI into their workflows within the next 18 months. This insight explores how marketing agents, like those offered in Agentforce, can transform customer engagement while enhancing internal team productivity and collaboration. Redefining Campaign Delivery with Marketing Agents Marketing agents are part of a broader suite of AI-powered virtual assistants that collaborate with humans to streamline workflows and optimize decision-making. These agents analyze data, interpret requests, and execute tasks such as content generation, campaign optimization, and performance analysis. Salesforce’s AI agents are designed to be partners, taking on time-consuming tasks so marketers can focus on high-value, strategic activities. With marketing agents, teams spend less time gathering and managing data and more time delivering personalized, impactful campaigns. A key mindset shift is required to maximize the value of these tools: instead of asking, “How can agents make business as usual more efficient?”, marketers should think, “How can agents transform the way we deliver exceptional customer experiences?” Streamlining Campaign Creation with Agentforce Campaigns 1. Intelligent Recommendations for Focused Action Einstein, Salesforce’s AI engine, analyzes data and provides contextual recommendations to help marketers achieve their goals. Agentforce Campaigns takes this a step further by turning these static recommendations into actionable steps, such as adjusting an audience segment or creating an entirely new campaign. 2. Rapid Campaign Brief Creation Agentforce Campaigns can draft campaign briefs in seconds using natural language prompts. These briefs incorporate organizational goals and marketing guidelines, making them ready for review and sharing within Salesforce. This saves time and ensures alignment from the start. 3. Contextual Content Creation Once a campaign brief is approved, marketing agents can generate campaign content such as emails and landing pages. Agentforce Campaigns automatically drafts subject lines, body copy, and calls to action within branded templates. Content can be refined using natural language commands or manual adjustments to fit the brand’s tone and strategy. 4. Simplified Audience Segmentation Marketers no longer need SQL expertise to build audience segments. With Agentforce Campaigns, they can describe their target audience in natural language, and the AI translates this into the necessary segment attributes. 5. Journey Activation at Scale Marketing agents simplify the process of activating multi-channel campaigns. Using natural language prompts, Agentforce Campaigns can configure a draft journey, complete with personalized content, ready for refinement and deployment. Unlocking New Opportunities with Marketing Agents 6. Unlimited Content Variations Time and budget constraints often limit the number of content variations marketers can produce. Agentforce Campaigns overcomes this limitation by generating multiple personalized content versions in seconds. This allows teams to deliver highly tailored messages for different audience segments without additional effort. 7. Nuanced Segment Exploration Traditional segmentation often depends on data science teams, which may have limited capacity. Marketing agents empower marketers to build their own nuanced segments using natural language. For example, separate churn segments can be created based on engagement scores, location, or purchase history, enabling more precise targeting. 8. A Culture of Testing and Learning Testing often falls by the wayside due to time constraints. Marketing agents make it easy to embed testing into campaigns by automating journey flows, enabling marketers to adopt a culture of continuous experimentation and improvement without increasing workloads. Transforming Marketing with Agentforce By integrating marketing agents into workflows, businesses can improve productivity, enhance personalization, and scale campaigns like never before. Agentforce Campaigns enables marketers to automate routine tasks, explore untapped opportunities, and deliver exceptional customer experiences. Marketers who embrace this shift will not only increase efficiency but also elevate their strategies, creating campaigns that are more targeted, impactful, and scalable. Note: Some features and services mentioned may not yet be available. Customers should base purchase decisions on currently available features. 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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December 18, 2024in Agentforce Platform, Agentic AI, Data
rise of digital workers
04Dec

Rise of Digital Workers

The Rise of Digital Workers: Unlocking a New Era of Opportunity Over the past two years, advancements in artificial intelligence have sparked a revolution in how humans work, live, and connect. While impressive generative AI models have garnered significant attention, a new paradigm of autonomous AI agents is emerging, promising transformative changes to industries and societies alike. Unlike traditional “predictive AI,” which analyzes data for recommendations, and “generative AI,” which creates content based on learned patterns, autonomous AI agents go a step further. These agents operate independently, executing tasks, making decisions, and even negotiating with other agents. This evolution introduces an intelligent digital workforce capable of scaling operations, reducing costs, and enhancing productivity. Consider a large retailer during the holiday season. Instead of relying on human workers or pre-programmed software to address customer inquiries or update inventory, autonomous agents can seamlessly manage customer interactions, monitor stock levels, reorder items, and coordinate shipping—all without human intervention. This level of automation represents a groundbreaking shift, enabling businesses to operate on an unprecedented scale. Expanding the Reach of Digital Labor Autonomous AI agents are breaking traditional barriers of human availability and physical constraints, enabling businesses to scale globally and more efficiently. These digital workers are not limited by geography, opening opportunities previously restricted to specific locations. However, this shift comes with challenges. Ensuring trust, accountability, and transparency in AI systems is critical. Equally important is investing in human-centric skills such as creativity, critical thinking, and adaptability, which remain uniquely human. Sustainability is another concern, as AI-driven technologies place increasing demands on energy and resources. By addressing these issues, societies can unlock the full potential of digital labor while safeguarding the planet and human values. Transforming Everyday Lives Beyond businesses, autonomous agents are poised to transform personal lives. Personalized agents can act as tutors for students, guiding them through their learning journeys. For individuals, these agents can manage everyday tasks, from scheduling appointments to coordinating complex logistics. In healthcare, AI agents are already alleviating administrative burdens on providers. For example, intelligent agents can handle patient communications, monitor progress, and schedule follow-ups, freeing doctors and nurses to focus on complex cases. Such innovations hold the potential to revolutionize patient care and improve outcomes across the board. Navigating Disruption and Change Like any transformative technology, the rise of autonomous agents will bring disruptions. Some industries will struggle to adapt, and jobs will inevitably evolve—or, in some cases, disappear. History shows, however, that technological revolutions often create far more opportunities than they displace. For example, the U.S. workforce grew by over 100 million jobs between 1950 and 2020, many in industries that didn’t exist before. The key lies in preparing workers for new roles through education and training. Autonomous agents are essential in addressing global challenges such as labor shortages and stagnant productivity growth. They amplify human capabilities, driving innovation and boosting economic output. For example, in the third quarter of 2024, U.S. productivity rose by 2.2%, fueled in part by AI advancements. Driving Innovation and Collaboration AI agents are also fostering innovation, sparking the creation of new companies and industries. More than 5,000 AI-focused startups have emerged in the past decade in the U.S. alone. This trend mirrors the technological revolutions driven by past innovations like microchips, the internet, and smartphones. However, effectively harnessing agentic AI requires collaboration among governments, businesses, nonprofits, and academia. Initiatives like the G7’s framework for AI accountability and the Bletchley Declaration emphasize transparency, safety, and data privacy, offering critical guardrails as AI adoption accelerates. A Vision for the Future Autonomous agents represent a powerful force for change, offering unprecedented opportunities for businesses and individuals alike. By leveraging these technologies responsibly and investing in human potential, societies can ensure a future of abundance and progress. As Marc Benioff, CEO of Salesforce, emphasizes, “AI has the potential to elevate every company, fuel economic growth, uplift communities, and lead to a future of abundance. If trust is our north star, agents will empower us to make a meaningful impact at an unprecedented scale.” 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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December 4, 2024in Agentforce Platform, Artificial Intelligence, Data, Generative AI, Salesforce, Technology

What Are Sales Channels?

Sales channels are the platforms or methods through which a business sells its products or services to customers. These channels can be direct (e.g., e-commerce sites and retail stores) or indirect (e.g., resellers and marketplaces). Some businesses rely on a single channel, while others use a mix of several.

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November 28, 2024in Data, Generative AI, Salesforce, Technology
NetSuite Salesforce Collaboration
17Nov

NetSuite Salesforce Collaboration

NetSuite Bets on Strategic Growth and Embraces Collaboration with Salesforce Growing on All Fronts At SuiteWorld 2024, the theme, “All Systems Grow,” reflected a pivotal moment for NetSuite. While the event lacked groundbreaking announcements, it showcased a fulfillment of past promises and a notable strategic shift toward openness and collaboration. Oracle and NetSuite are now welcoming competitors as partners, signaling a move toward interoperability that could redefine their market positioning. With over 40,000 customers, NetSuite continues its strong growth in the ERP space, particularly among SMBs. The company’s Q3 sales surged 20% year-over-year, underlining its momentum in the mid-market. Beyond traditional ERP capabilities, NetSuite’s expanded suite of solutions positions it as more than just an ERP provider. Delivering on AI Innovations While there were no splashy acquisitions, NetSuite made significant strides by rolling out 170 new modules and features, many leveraging AI. These enhancements blend predictive AI and generative AI to increase accuracy and user productivity. These updates aim to elevate both the platform’s quality and the efficiency of its users. Redwood Design: A Transformative User Experience NetSuite is adopting Oracle’s Redwood design language, promising a more intuitive and user-friendly interface. While Redwood is not new, its phased rollout within NetSuite is a significant step forward. Notable Additions: SuiteProcurement and Salesforce Integration SuiteProcurement: NetSuite’s new procurement automation solution integrates directly with Amazon Business and Staples Business Advantage, automating ordering, invoicing, approvals, and deliveries. Plans are underway to expand vendor support, offering broader applicability in the future. Salesforce Partnership: NetSuite’s most significant announcement was its strategic partnership with Salesforce, enabling real-time data exchange between the platforms. Evan Goldberg, NetSuite’s founder and EVP, explained the rationale:“It’s up to the customer to decide what software they want to use.” The partnership reflects NetSuite’s commitment to addressing customer needs, with more SaaS integrations expected in the future. Expanding Field Service Management (FSM) NetSuite’s Field Service Management (FSM) capabilities, acquired last year, are now better integrated into its platform. While development progress has been slower than anticipated, significant enhancements are expected in the coming year, leveraging Oracle technology to extend FSM’s functionality across industries. And Field Service Management is available in Salesforce, as well. Positioned for Continued SMB Growth NetSuite’s investments are yielding results, as demonstrated by its rapid growth and deeper integration of Oracle technology. The NetSuite Analytics Data Warehouse and Enterprise Performance Management are driving adoption among existing users, showcasing the platform’s scalability. NetSuite’s ability to quickly integrate Oracle updates into its infrastructure gives it a competitive edge, ensuring customers benefit from the latest innovations without delays. With its robust feature set, AI-powered tools, and strategic partnerships like the one with Salesforce, NetSuite has strengthened its position as a go-to ERP platform for SMBs. Its consistent 20% year-over-year growth indicates a bright future, making it an increasingly attractive option for mid-market businesses. 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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November 17, 2024in Data, Enterprise, Generative AI, Salesforce, Technology
AI Agent Rivalry
16Nov

AI Agent Rivalry

Microsoft and Salesforce’s AI Agent Rivalry Heats Up The battle for dominance in the AI agent space has escalated, with Salesforce CEO Marc Benioff intensifying his criticism of Microsoft’s AI solutions. Following remarks at Dreamforce 2024, Benioff took to X (formerly Twitter) to call out Microsoft for what he called “rebranding Copilot as ‘agents’ in panic mode.” The AI Agent rivalry winner may be determined not by flashy features but by delivering tangible, transformative outcomes for businesses navigating the complexities of AI adoption. AI Agent Rivalry. Benioff didn’t hold back, labeling Microsoft’s Copilot as “a flop”, citing issues like data leaks, inaccuracies, and requiring customers to build their own large language models (LLMs). In contrast, he touted Salesforce’s Agentforce as a solution that autonomously drives sales, service, marketing, analytics, and commerce without the complications he attributes to Microsoft’s offerings. Microsoft’s Copilot: A New UI for AI Microsoft recently unveiled new autonomous agent capabilities for Copilot Studio and Dynamics 365, positioning these agents as tools to enhance productivity across teams and functions. CEO Satya Nadella described Copilot as “the UI for AI” and emphasized its flexibility, allowing businesses to create, manage, and integrate agents seamlessly. Despite the fanfare, Benioff dismissed Copilot’s updates, likening it to “Clippy 2.0” and claiming it fails to deliver accuracy or transformational impact. Salesforce Expands Agentforce with Strategic Partnerships At Dreamforce 2024, Salesforce unveiled its Agentforce Partner Network, a global ecosystem featuring collaborators like AWS, Google Cloud, IBM, and Workday. The move aims to bolster the capabilities of Agentforce, Salesforce’s AI-driven platform that delivers tailored, autonomous business solutions. Agentforce allows businesses to deploy customizable agents without complex coding. With features like the Agent Builder, users can craft workflows and instructions in natural language, making the platform accessible to both technical and non-technical teams. Flexibility and Customization: Salesforce vs. Microsoft Both Salesforce and Microsoft emphasize AI’s transformative potential, but their approaches differ: Generative AI vs. Predictive AI Salesforce has doubled down on generative AI, with Einstein GPT producing personalized content using CRM data while also providing predictive analytics to forecast customer behavior and sales outcomes. Microsoft, on the other hand, combines generative and predictive AI across its ecosystem. Copilot not only generates content but also performs autonomous decision-making in Dynamics 365 and Azure, positioning itself as a comprehensive enterprise solution. The Rise of Multi-Agent AI Systems The competition between Microsoft and Salesforce reflects a broader trend in AI-driven automation. Companies like OpenAI are experimenting with frameworks like Swarm, which simplifies the creation of interconnected AI agents for tasks such as lead generation and marketing campaign development. Similarly, startups like DevRev are introducing conversational AI builders to design custom agents, offering enterprises up to 95% task accuracy without the need for coding. What Lies Ahead in the AI Agent Landscape? As Salesforce and Microsoft push the boundaries of AI integration, businesses are evaluating these tools for their flexibility, customization, and impact on operations. While Salesforce leads in CRM-focused AI, Microsoft’s integrated approach appeals to enterprises seeking cross-functional AI solutions. In the end, the winner may be determined not by flashy features but by delivering tangible, transformative outcomes for businesses navigating the complexities of AI adoption. AI Agent Rivalry. 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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November 16, 2024in Agentforce Platform, Data, Enterprise, Generative AI, Google, Salesforce
gettectonic.com