Moment Archives - gettectonic.com

Agentforce: Modernizing 311 and Case Management

Join Tectonic for an informational webinar on Salesforce Agentforce, Modernizing 311 services, and Case management. In this webinar you will hear: For more information fill out the contact us form below or reach out to the Public Sector team [email protected] Get ready for the Next Frontier in Enterprise AI: Shaping Public Policies for Trusted AI Agents! AI agents are a technological revolution – the third wave of artificial intelligence after predictive and generative AI. They go beyond traditional automation, being capable of searching for relevant data, analyzing it to formulate a plan, and then putting the plan into action. Users can configure agents with guardrails that specify what actions they can take and when tasks should be handed off to humans. Agentic AI is already here. Innovative organizations have started deploying and benefitting from AI agents in concrete ways. However, for governments, enterprises, and the workforce to be able to harness the full potential of AI agents, they will need more than just the right technology; they will need the right public policies to help them become agent-ready. At a time when global policymakers seek pathways to economic growth, the advent of AI agents offers a unique opportunity to think deeply about the policies that will enable the diffusion of trusted AI in government and the enterprise, and equip the workforce with the necessary skills to unlock AI’s full potential for enhanced productivity and more meaningful work. AI agents build on earlier innovations, including chatbots and AI assistants. These all play a role in task automation; however, there are differences in their levels of sophistication and personalization in serving their users and the range of tasks they can perform. Key considerations for AI agents: Government services as a role model for agentic AI and humans together Governments today face ever-increasing pressures to serve citizens with constrained resources. Research surveying people in over 40 countries has shown that 75% expect government service quality to be on par with leading private sector companies, while 72% are comfortable with personalized government digital services. Amidst these pressures, there is clear demand for trusted innovative tools from government workers themselves – a recent survey across 14 countries estimated that 49% of government workers have used unapproved generative AI tools at work. Governments have enormous opportunities to safely leverage AI agents – in particular their speed, responsiveness, personalization – to enhance citizen-facing services. For example, if a citizen wants to check on the status of their application for a license or a public benefit, an agent-powered interface could at any hour assist them in ascertaining the status of their application, locate public information on policies and application procedures, or schedule an appointment to access a related service. These interactions would have previously taken the citizen far longer, required valuable government employee time, may have only been available during business hours, or been left undone altogether. By deploying agents in these contexts governments can make the most of their limited resources while building citizen satisfaction with and trust in services. In addition to leading by example in digital transformation, governments can play a pivotal role in boosting diffusion of trusted AI solutions, including AI agents, in the private sector. As governments search for new ways to promote economic growth and combat inflation, this is a key moment for national economies. The rapid development and commercialization of AI tools have created opportunities for businesses large and small around the world to rapidly become more productive and gain a competitive edge by adopting frontier technologies. But to achieve the benefits, businesses need to overcome resource constraints, rapidly acquire new commercial knowledge and practical skills, and have regulatory clarity. For governments to position their businesses for success, they need to set ambitious goals and identify and remedy the blockers that hold back the uptake of new technologies by enterprises. For the past 25 years, Salesforce has led their customers through every major technological shift: from cloud, to mobile, to predictive and generative AI, and, today, agentic AI. We are at the cusp of a pivotal moment for enterprise AI that has the opportunity to supercharge productivity and change the way we work forever. This will require governments working together with industry, civil society, and all stakeholders to ensure responsible technological advancement and workforce readiness. We look forward to continuing our contributions to the public policy discussions on trusted enterprise AI agents. 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

Read More
Salesforce Unveils Agentforce for Consumer Goods

Salesforce Unveils Agentforce for Consumer Goods

Salesforce Unveils Agentforce for Consumer Goods: Accelerating AI Adoption in Retail San Francisco, [April 2025] – Just eight days after launching Agentforce for Field Service, Salesforce has introduced Agentforce for Consumer Goods—a tailored solution designed to help brands quickly deploy AI agents across four key sectors: customer service, key account management, retail sales, and field operations. Unlike previous editions that offered pre-built AI agents for specific roles, this release provides a library of industry-specific skills and actions, empowering consumer goods companies to rapidly customize and deploy their own AI assistants. Why Agentforce for Consumer Goods? While businesses could already build agents on the standard Agentforce platform, this industry-focused edition accelerates deployment with:✔ Pre-configured skills for customer service, sales, and field teams✔ Faster implementation with ready-made automation components✔ Lower-risk experimentation for brands new to agentic AI “Salesforce is curating a smooth onboarding experience for companies entering the agentic AI era,” says Martin Schneider, VP & Principal Analyst at Constellation Research. “This gives quick wins—building confidence before diving into advanced multi-agent workflows.” Key Use Cases for Consumer Goods Brands 🛎️ AI-Powered Customer Service Agents Example: A rep at a home appliance company can ask an AI agent to check a customer’s product health—if maintenance is due, the agent drafts a service quote in seconds. 📈 Smarter Sales Assistants Example: If an account’s order volume drops unexpectedly, an AI agent can recommend new products to pitch, helping sales teams react faster. 🚚 Optimized Field Operations Example: When a customer requests a replacement, an AI agent instantly books delivery, assigns the nearest driver, and updates schedules—no manual input needed. The Bigger Picture: Salesforce’s Agentforce Momentum This launch follows: With 5,000+ customers already on Agentforce, industry-specific editions like this lower the barrier to entry—letting more brands test AI agents in low-stakes scenarios before scaling. What’s Next? Expect more vertical-focused Agentforce releases in 2025, building on earlier launches like Agentforce for Retail. For now, consumer goods brands have a new toolkit to turn repetitive tasks into automated workflows—freeing teams to focus on growth. Ready to explore AI agents for your business? Contact Tectonic today! 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

Read More
time series artificial intelligence

Revolutionizing Time Series AI

Revolutionizing Time Series AI: Salesforce’s Synthetic Data Breakthrough for Foundation Models Revolutionizing Time Series AI. Time series analysis is hindered by critical challenges in data availability, quality, and diversity—key factors in building powerful foundation models. Real-world datasets often suffer from regulatory constraints, inherent biases, inconsistent quality, and a lack of paired textual annotations, making it difficult to develop robust Time Series Foundation Models (TSFMs) and Time Series Large Language Models (TSLLMs). These limitations stifle progress in forecasting, classification, anomaly detection, reasoning, and captioning, restricting AI’s full potential. To tackle these obstacles, Salesforce AI Research has pioneered an innovative approach: leveraging synthetic data to enhance TSFMs and TSLLMs. Their groundbreaking study, “Empowering Time Series Analysis with Synthetic Data,” introduces a strategic framework for using synthetic data to refine model training, evaluation, and fine-tuning—while mitigating biases, expanding dataset diversity, and enriching contextual understanding. This approach is particularly transformative in regulated sectors like healthcare and finance, where real-world data sharing is heavily restricted. The Science Behind Synthetic Data Generation Salesforce’s methodology employs advanced synthetic data generation techniques tailored to replicate real-world time series dynamics, including trends, seasonality, and noise patterns. Key innovations include: These methods enable controlled yet highly varied data generation, capturing a broad spectrum of time series behaviors essential for robust model training. Proven Benefits: How Synthetic Data Supercharges Model Performance Salesforce’s research reveals significant performance gains from synthetic data across multiple stages of AI development: ✅ Pretraining Boost – Models like ForecastPFN, Mamba4Cast, and TimesFM showed marked improvements when pretrained on synthetic data. ForecastPFN, for instance, excelled in zero-shot forecasting after full synthetic pretraining. ✅ Optimal Data Blending – Chronos found peak performance by mixing 10% synthetic data with real-world datasets, beyond which excessive synthetic data could reduce diversity and effectiveness. ✅ Enhanced Evaluation – Synthetic data allowed precise assessment of model capabilities, uncovering hidden biases and gaps. For example, Moment used synthetic sinusoidal waves to analyze embedding sensitivity and trend detection accuracy. Future Directions: Overcoming Limitations While synthetic data offers immense promise, Salesforce identifies key areas for improvement: 🔹 Systematic Integration – Developing structured frameworks to strategically fill gaps in real-world datasets.🔹 Beyond Statistical Methods – Exploring diffusion models and other generative AI techniques for richer, more realistic synthetic data.🔹 Fine-Tuning Potential – Leveraging synthetic data adaptively to address domain-specific weaknesses during fine-tuning. The Path Forward Salesforce AI Research demonstrates that synthetic data is a game-changer for time series analysis, enabling stronger generalization, reduced bias, and superior performance across AI tasks. While challenges like realism and alignment remain, the future is bright—advancements in generative AI, human-in-the-loop refinement, and systematic gap-filling will further propel the reliability and applicability of time series models. By embracing synthetic data, Salesforce is laying the foundation for the next generation of AI-driven time series innovation—ushering in a new era of accuracy, adaptability, and intelligence. 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

Read More
gettectonic.com