Meta Archives - gettectonic.com
No-Code Generative AI
11Feb

Generative-Driven Development

Nowhere has the rise of generative AI tools been more transformative than in software development. It began with GitHub Copilot’s enhanced autocomplete, which then evolved into interactive, real-time coding assistants like Aider and Cursor that allow engineers to dictate changes and see them applied live in their editor. Today, platforms like Devin.ai aim even higher, aspiring to create autonomous software systems capable of interpreting feature requests or bug reports and delivering ready-to-review code. At its core, the ambition of these AI tools mirrors the essence of software itself: to automate human work. Whether you were writing a script to automate CSV parsing in 2005 or leveraging AI today, the goal remains the same—offloading repetitive tasks to machines. What makes generative AI tools distinct, however, is their focus on automating the work of automation itself. Framing this as a guiding principle enables us to consider the broader challenges and opportunities generative AI brings to software development. Automate the Process of Automation The Doctor-Patient Strategy Most contemporary generative AI tools operate under what can be called the Doctor-Patient strategy. In this model, the GenAI tool acts on a codebase as a distinct, external entity—much like a doctor treats a patient. The relationship is one-directional: the tool modifies the codebase based on given instructions but remains isolated from the architecture and decision-making processes within it. Why This Strategy Dominates: However, the limitations of this strategy are becoming increasingly apparent. Over time, the unidirectional relationship leads to bot rot—the gradual degradation of code quality due to poorly contextualized, repetitive, or inconsistent changes made by generative AI. Understanding Bot Rot Bot rot occurs when AI tools repeatedly make changes without accounting for the macro-level architecture of a codebase. These tools rely on localized context, often drawing from semantically similar code snippets, but lack the insight needed to preserve or enhance the overarching structure. Symptoms of Bot Rot: Example:Consider a Python application that parses TPS report IDs. Without architectural insight, a code bot may generate redundant parsing methods across multiple modules rather than abstracting the logic into a centralized model. Over time, this duplication compounds, creating a chaotic and inefficient codebase. A New Approach: Generative-Driven Development (GDD) To address the flaws of the Doctor-Patient strategy, we propose Generative-Driven Development (GDD), a paradigm where the codebase itself is designed to enable generative AI to enhance automation iteratively and sustainably. Pillars of GDD: How GDD Improves the Development Lifecycle Under GDD, the traditional Test-Driven Development (TDD) cycle (red, green, refactor) evolves to integrate AI processes: This complete cycle eliminates the gaps present in current generative workflows, reducing bot rot and enabling sustainable automation. Over time, GDD-based codebases become easier to maintain and automate, reducing error rates and cycle times. A Day in the Life of a GDD Engineer Imagine a GDD-enabled workflow for a developer tasked with updating TPS report parsing: By embedding AI into the development process, GDD empowers engineers to focus on high-level decision-making while ensuring the automation process remains sustainable and aligned with architectural goals. Conclusion Generative-Driven Development represents a significant shift in how we approach software development. By prioritizing architecture, embedding automation into the software itself, and writing GenAI-optimized code, GDD offers a sustainable path to achieving the ultimate goal: automating the process of automation. As AI continues to reshape the industry, adopting GDD will be critical to harnessing its full potential while avoiding the pitfalls of bot rot. 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
February 11, 2025in AI Tools, Generative AI, Laws0 Comments
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 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
February 3, 2025in Agentic AI, AI Tools, Artificial Intelligence, Data, Generative AI, Google, Laws, Salesforce, Technology0 Comments
Unstructured Data
27Jan

Unstructured Data

Ultimately, combining the semantic depth of embeddings with the precision of structured retrieval methods will create more intelligent, robust AI systems—ones that understand the full context of the data and can deliver more precise, relevant results.

Read More
January 27, 2025in Data, Generative AI0 Comments
20Jan

Einstein Copilot No More

Salesforce Rebrands Einstein Copilot as Agentforce Salesforce has officially retired its Einstein Copilot feature, rebranding it as “Agentforce (Default).” While hints of this change surfaced earlier, with confirmation arriving in January 2025, many users were still surprised by the move. Ashish Agarwal, a Salesforce Architect, Consultant, and Trainer, highlighted the change in a LinkedIn post, noting that the conversational AI assistant had been integrated into Salesforce’s broader Agentforce platform. Salesforce’s release notes confirmed the update, stating there would be no changes to functionality but minor refinements to permissions, user interface elements, and help documentation. “In Setup, you’ll see Agentforce or Agentforce (Default) for the agent name and type. This change won’t impact your implementations,” Salesforce assured its users. The company reiterated that the tool remains a vital resource for assisting Salesforce users with daily business interactions. The Journey of Einstein Copilot Einstein Copilot, introduced in April 2024, was Salesforce’s flagship generative AI (GenAI) solution. Integrated into Salesforce applications, it allowed users to perform workflows and ask questions using natural language. Built on the Einstein 1 Platform, it leveraged Salesforce’s Data Cloud and metadata to deliver personalized experiences. Despite its relatively short lifespan, the update positions Einstein Copilot as a component of Agentforce, aligning it with Salesforce’s focus on AI agents. Moving Beyond the “Copilot” Label Salesforce CEO Marc Benioff’s critical remarks about Microsoft’s Copilot may offer clues to the rebranding decision. Over recent months, Benioff has labeled Microsoft’s Copilot as “repackaged ChatGPT” and “Clippy 2.0,” aiming to distinguish Salesforce’s offerings from its competitor. This shift also reflects Salesforce’s evolving AI strategy. The company’s “Four Waves of AI” framework identifies copilots as part of the second wave, now replaced by the third wave: autonomous AI agents. Agarwal encapsulated this shift in his LinkedIn post, stating: “In today’s Agentic world, you don’t need a Copilot to assist you. Agents are taking the controls themselves. They can pilot the plane, handle turbulence, and even serve the in-flight snacks—all while you sit back, relax, and enjoy the ride.” Reactions and Reservations Despite Salesforce’s enthusiasm, the rebranding has drawn some skepticism. Richard Clark, Vice President of EMEA Alliances and Success at Elements.cloud, expressed surprise at the decision, citing Einstein Copilot’s unique role as an internal user agent. Agarwal also questioned the naming convention, arguing that “Agentforce” represents Salesforce’s entire AI platform, which includes components like the Prompt Builder, agents, and the Einstein Trust Layer. Assigning the name to a single agent, he suggested, could create unnecessary confusion. The Future of Agentforce Since its debut in October 2024, Agentforce has quickly evolved. The introduction of Agentforce 2.0, dubbed “The Digital Labor Platform,” has expanded its capabilities, allowing users to create and deploy autonomous AI agents. Salesforce has also tailored its offerings for specific industries. For example, Agentforce for Retail provides AI-driven solutions for customer service, guided shopping, order management, and appointment scheduling. Conclusion Salesforce Agentforce Details The transition from Einstein Copilot to Agentforce signifies Salesforce’s commitment to advancing its AI strategy. While some users may need time to adjust, the move aligns with the company’s vision of autonomous AI agents driving productivity and innovation. Whether viewed as a strategic pivot or a deliberate effort to distance itself from competitors, the rebranding underscores Salesforce’s ongoing evolution in the AI space. 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
January 20, 2025in Agentforce Platform, Data, Einstein 1 Platform, Generative AI, Salesforce0 Comments
agetnforce for nonprofits
08Jan

A Revolution in Enterprise AI

Salesforce Unveils Agentforce 2.0: A Revolution in Enterprise AI Salesforce has launched Agentforce 2.0, a cutting-edge update poised to redefine how enterprises design, deploy, and manage AI-driven solutions. With a suite of innovative features, this upgrade emphasizes seamless collaboration, enhanced integration, and unprecedented flexibility in creating AI-powered workflows. Agentforce 2.0 focuses on three core advancements: headless agents for programmatic control, deep Slack integration for streamlined teamwork, and a revamped API-driven architecture that simplifies development and deployment. Core Highlights of Agentforce 2.0 1. Enhanced Integration Architecture Agentforce 2.0 introduces a sophisticated integration framework designed to streamline workflows and accelerate development. By leveraging MuleSoft for Flow, it provides 40 pre-built connectors for seamless integration with enterprise systems. At the heart of this architecture is the API Catalog, a centralized hub for discovering, managing, and optimizing APIs within Salesforce. The Topic Center simplifies deployment by embedding Agentforce metadata into API design workflows, minimizing manual configurations and reducing time-to-market. Key features of the API Catalog include: This API-first approach empowers DevOps teams to manage and optimize AI capabilities efficiently through a single interface. 2. Upgraded Atlas Reasoning Engine The Atlas Reasoning Engine takes enterprise AI capabilities to the next level, enabling smarter and more effective AI-driven operations. Key enhancements include: Preliminary testing shows a 33% improvement in response accuracy and a doubling of relevance in complex scenarios compared to previous models. The engine uniquely combines System 1 reasoning (fast, intuitive responses) with System 2 reasoning (deep analytical processing), setting a new benchmark for enterprise AI intelligence. 3. Headless Agents for Greater Control A game-changing feature of Agentforce 2.0 is the introduction of headless agents, which operate autonomously without requiring direct user interaction. Key capabilities include: This feature enables enterprises to build robust, autonomous workflows, making Agentforce 2.0 an indispensable tool for optimizing digital operations. 4. Deep Slack Integration Agentforce 2.0 seamlessly integrates AI with Slack, Salesforce’s collaboration platform, fostering real-time, AI-enhanced teamwork. Technical advancements include: By embedding AI agents directly into Slack, teams can break down silos, enabling smoother collaboration and faster decision-making across departments. 5. Data Cloud Integration Agentforce 2.0 leverages Salesforce’s Data Cloud to enhance data accessibility and intelligence. Key features include: This integration ensures that AI agents can provide real-time, actionable insights by tapping into a comprehensive, unified data ecosystem. Final Thoughts Agentforce 2.0 represents a transformative leap in enterprise AI, combining cutting-edge technologies like headless agents, deep Slack integration, and an advanced API-driven architecture. By empowering organizations to create intelligent, autonomous workflows, Agentforce 2.0 sets a new standard for AI innovation in the enterprise sector. With its ability to streamline collaboration, optimize operations, and unlock new levels of productivity, Agentforce 2.0 positions Salesforce as a leader in the evolving AI landscape, helping businesses stay ahead in the digital age. 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
January 8, 2025in Data, Enterprise, Mulesoft, Salesforce0 Comments
Standards in Healthcare Cybersecurity
21Dec

Deploying Large Language Models in Healthcare

Study Identifies Cost-Effective Strategies for Deploying Large Language Models in Healthcare Efficient deployment of large language models (LLMs) at scale in healthcare can streamline clinical workflows and reduce costs by up to 17 times without compromising reliability, according to a study published in NPJ Digital Medicine by researchers at the Icahn School of Medicine at Mount Sinai. The research highlights the potential of LLMs to enhance clinical operations while addressing the financial and computational hurdles healthcare organizations face in scaling these technologies. To investigate solutions, the team evaluated 10 LLMs of varying sizes and capacities using real-world patient data. The models were tested on chained queries and increasingly complex clinical notes, with outputs assessed for accuracy, formatting quality, and adherence to clinical instructions. “Our study was driven by the need to identify practical ways to cut costs while maintaining performance, enabling health systems to confidently adopt LLMs at scale,” said Dr. Eyal Klang, director of the Generative AI Research Program at Icahn Mount Sinai. “We aimed to stress-test these models, evaluating their ability to manage multiple tasks simultaneously and identifying strategies to balance performance and affordability.” The team conducted over 300,000 experiments, finding that high-capacity models like Meta’s Llama-3-70B and GPT-4 Turbo 128k performed best, maintaining high accuracy and low failure rates. However, performance began to degrade as task volume and complexity increased, particularly beyond 50 tasks involving large prompts. The study further revealed that grouping tasks—such as identifying patients for preventive screenings, analyzing medication safety, and matching patients for clinical trials—enabled LLMs to handle up to 50 simultaneous tasks without significant accuracy loss. This strategy also led to dramatic cost savings, with API costs reduced by up to 17-fold, offering a pathway for health systems to save millions annually. “Understanding where these models reach their cognitive limits is critical for ensuring reliability and operational stability,” said Dr. Girish N. Nadkarni, co-senior author and director of The Charles Bronfman Institute of Personalized Medicine. “Our findings pave the way for the integration of generative AI in hospitals while accounting for real-world constraints.” Beyond cost efficiency, the study underscores the potential of LLMs to automate key tasks, conserve resources, and free up healthcare providers to focus more on patient care. “This research highlights how AI can transform healthcare operations. Grouping tasks not only cuts costs but also optimizes resources that can be redirected toward improving patient outcomes,” said Dr. David L. Reich, co-author and chief clinical officer of the Mount Sinai Health System. The research team plans to explore how LLMs perform in live clinical environments and assess emerging models to determine whether advancements in AI technology can expand their cognitive thresholds. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
December 21, 2024in Data, Generative AI, Technology

Transitioning to Granular Case Categorization in Salesforce

Making Data Work: Transitioning to Granular Case Categorization in Salesforce As businesses grow and their reporting needs evolve, the demand for more granular data categorization becomes essential to maintaining clarity and driving decision-making. Salesforce’s Case Object, which traditionally uses Type and Sub-type fields to classify cases, may no longer suffice for organizations with increasingly complex reporting standards. To address this, a new approach is being implemented: introducing New Type, New Sub-type, and New Sub-sub-Type fields. These fields enable deeper categorization and allow organizations to align their data architecture with modern reporting demands. However, adopting these new fields presents challenges, especially for legacy systems that rely on the older fields. Here’s how organizations can navigate this transition seamlessly using Apex and custom metadata. The Challenge: Transitioning to New Fields The move toward granular categorization brings clear benefits, but it also introduces complexities: To ensure a smooth transition, organizations need a solution that automates field mapping while maintaining data consistency across both legacy and updated systems. The Solution: Apex Mapping with Custom Metadata By leveraging Apex and custom metadata, organizations can create an automated system to map old field values (Type and Sub-type) to the new fields (New Type, New Sub-type, and New Sub-sub-Type). Here’s how the mapping process works: The Benefits of Apex Mapping The Technical Process Real-World Impact Final Thoughts In today’s data-driven world, evolving reporting standards demand more precise data categorization. Transitioning from Type and Sub-type fields to New Type, New Sub-type, and New Sub-sub-Type fields ensures your organization can meet modern reporting needs. By implementing Apex mapping with custom metadata, you can automate the transition, maintain legacy compatibility, and unlock the full potential of granular reporting. While the shift requires planning and effort, the long-term benefits of improved accuracy, efficiency, and decision-making make it an invaluable step for any organization. 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
December 20, 2024in Data
Meta Joins the Race to Reinvent Search with AI
18Dec

Meta Joins the Race to Reinvent Search with AI

Meta Joins the Race to Reinvent Search with AI Meta, the parent company of Facebook, Instagram, and WhatsApp, is stepping into the evolving AI-driven search landscape. As vendors increasingly embrace generative AI to transform search experiences, Meta aims to challenge Google’s dominance in this space. The company is reportedly developing an AI-powered search engine designed to provide conversational, AI-generated summaries of recent events and news. These summaries would be delivered via Meta’s AI chatbot, supported by a multiyear partnership with Reuters for real-time news insights, according to The Information. AI Search: A Growing Opportunity The push comes as generative AI reshapes search technology across the industry. Google, the long-standing leader, has integrated AI features such as AI Overviews into its search platform, offering users summarized search results, product comparisons, and more. This feature, now available in over 100 countries as of October 2024, signals a shift in traditional search strategies. Similarly, OpenAI, the creator of ChatGPT, has been exploring its own AI search model, SearchGPT, and forging partnerships with media organizations like the Associated Press and Hearst. However, OpenAI faces legal challenges, such as a lawsuit from The New York Times over alleged copyright infringement. Meta’s entry into AI-powered search aligns with a broader trend among tech giants. “It makes sense for Meta to explore this,” said Mark Beccue, an analyst with TechTarget’s Enterprise Strategy Group. He noted that Meta’s approach seems more targeted at consumer engagement than enterprise solutions, particularly appealing to younger audiences who are shifting away from traditional search behaviors. Shifting User Preferences Generational changes in search habits are creating opportunities for new players in the market. Younger users, particularly Gen Z and Gen Alpha, are increasingly turning to platforms like TikTok for lifestyle advice and Amazon for product recommendations, bypassing traditional search engines like Google. “Recent studies show younger generations are no longer using ‘Google’ as a verb,” said Lisa Martin, an analyst with the Futurum Group. “This opens the playing field for competitors like Meta and OpenAI.” Forrester Research corroborates this trend, noting a diversification in search behaviors. “ChatGPT’s popularity has accelerated this shift,” said Nikhil Lai, a Forrester analyst. He added that these changes could challenge Google’s search ad market, with its dominance potentially waning in the years ahead. Meta’s AI Search Potential Meta’s foray into AI search offers an opportunity to enhance user experiences and deepen engagement. Rather than pushing news content into users’ feeds—an approach that has drawn criticism—AI-driven search could empower users to decide what content they see and when they see it. “If implemented thoughtfully, it could transform the user experience and give users more control,” said Martin. This approach could also boost engagement by keeping users within Meta’s ecosystem. The Race for Revenue and Trust While AI-powered search is expected to increase engagement, monetization strategies remain uncertain. Google has yet to monetize its AI Overviews, and OpenAI’s plans for SearchGPT remain unclear. Other vendors, like Perplexity AI, are experimenting with models such as sponsored questions instead of traditional results. Trust remains a critical factor in the evolving search landscape. “Google is still seen as more trustworthy,” Lai noted, with users often returning to Google to verify AI-generated information. Despite the competition, the conversational AI search market lacks a definitive leader. “Google dominated traditional search, but the race for conversational search is far more open-ended,” Lai concluded. Meta’s entry into this competitive space underscores the ongoing evolution of search technology, setting the stage for a reshaped digital landscape driven by AI innovation. 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
December 18, 2024in Enterprise, Generative AI, Google, Laws, Salesforce, Technology
Transforming the Role of Data Science Teams
15Dec

Transforming the Role of Data Science Teams

GenAI: Transforming the Role of Data Science Teams Challenges, Opportunities, and the Evolving Responsibilities of Data Scientists Generative AI (GenAI) is revolutionizing the AI landscape, offering faster development cycles, reduced technical overhead, and enabling groundbreaking use cases that once seemed unattainable. However, it also introduces new challenges, including the risks of hallucinations and reliance on third-party APIs. For Data Scientists and Machine Learning (ML) teams, this shift directly impacts their roles. GenAI-driven projects, often powered by external providers like OpenAI, Anthropic, or Meta, blur traditional lines. AI solutions are increasingly accessible to non-technical teams, but this accessibility raises fundamental questions about the role and responsibilities of data science teams in ensuring effective, ethical, and future-proof AI systems. Let’s explore how this evolution is reshaping the field. Expanding Possibilities Without Losing Focus While GenAI unlocks opportunities to solve a broader range of challenges, not every problem warrants an AI solution. Data Scientists remain vital in assessing when and where AI is appropriate, selecting the right approaches—whether GenAI, traditional ML, or hybrid solutions—and designing reliable systems. Although GenAI broadens the toolkit, two factors shape its application: For example, incorporating features that enable user oversight of AI outputs may prove more strategic than attempting full automation with extensive fine-tuning. Differentiation will not come from simply using LLMs, which are widely accessible, but from the unique value and functionality they enable. Traditional ML Is Far from Dead—It’s Evolving with GenAI While GenAI is transformative, traditional ML continues to play a critical role. Many use cases, especially those unrelated to text or images, are best addressed with ML. GenAI often complements traditional ML, enabling faster prototyping, enhanced experimentation, and hybrid systems that blend the strengths of both approaches. For instance, traditional ML workflows—requiring extensive data preparation, training, and maintenance—contrast with GenAI’s simplified process: prompt engineering, offline evaluation, and API integration. This allows rapid proof of concept for new ideas. Once proven, teams can refine solutions using traditional ML to optimize costs or latency, or transition to Small Language Models (SMLs) for greater control and performance. Hybrid systems are increasingly common. For example, DoorDash combines LLMs with ML models for product classification. LLMs handle cases the ML model cannot classify confidently, retraining the ML system with new insights—a powerful feedback loop. GenAI Solves New Problems—But Still Needs Expertise The AI landscape is shifting from bespoke in-house models to fewer, large multi-task models provided by external vendors. While this simplifies some aspects of AI implementation, it requires teams to remain vigilant about GenAI’s probabilistic nature and inherent risks. Key challenges unique to GenAI include: Data Scientists must ensure robust evaluations, including statistical and model-based metrics, before deployment. Monitoring tools like Datadog now offer LLM-specific observability, enabling teams to track system performance in real-world environments. Teams must also address ethical concerns, applying frameworks like ComplAI to benchmark models and incorporating guardrails to align outputs with organizational and societal values. Building AI Literacy Across Organizations AI literacy is becoming a critical competency for organizations. Beyond technical implementation, competitive advantage now depends on how effectively the entire workforce understands and leverages AI. Data Scientists are uniquely positioned to champion this literacy by leading initiatives such as internal training, workshops, and hackathons. These efforts can: The New Role of Data Scientists: A Strategic Pivot The role of Data Scientists is not diminishing but evolving. Their expertise remains essential to ensure AI solutions are reliable, ethical, and impactful. Key responsibilities now include: By adapting to this new landscape, Data Scientists will continue to play a pivotal role in guiding organizations to harness AI effectively and responsibly. GenAI is not replacing them; it’s expanding their impact. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
December 15, 2024in Data, Generative AI
Clippy-gate
10Dec

Clippy-gate

Clippy-gate: Why Salesforce vs. Microsoft is More Than a Marketing Spat Salesforce and Microsoft are clashing once again, this time over AI and intelligent agents. What started as a seemingly lighthearted joke by Salesforce CEO Marc Benioff at Dreamforce—mocking Microsoft’s Copilot as a “Clippy 2.0″—has escalated into a significant dispute. This confrontation extends beyond the typical vendor rivalry, touching on enterprise AI’s future and its implications for businesses. A History of Rivalry Salesforce and Microsoft have a long history of competition. Past disputes included lawsuits and marketing stunts, such as Microsoft’s ill-fated 2010 Dreamforce gimmick involving a “defecting” Salesforce customer. While the two companies briefly found common ground under Microsoft CEO Satya Nadella, this détente dissolved amidst antitrust challenges over Microsoft’s LinkedIn acquisition and an aborted attempt to acquire Salesforce. Benioff has since reverted to openly criticizing Microsoft. The latest round of hostilities centers on Microsoft’s announcement of ten AI agents for Dynamics 365, coinciding with the release of Salesforce’s Agentforce. Benioff has doubled down, accusing Microsoft of overhyping Copilot and failing to meet enterprise AI standards. Benioff’s Allegations Benioff’s critiques have been relentless. He has accused Microsoft of misleading customers about AI capabilities, claiming that Copilot “spews data all over the floor” and lacks the security, metadata, and integration necessary for enterprise use. On social media, he dismissed Copilot as “panic mode” rebranding, contrasting it with Agentforce, which he describes as a transformative platform integrated with enterprise data and workflows. Microsoft’s Position Microsoft has largely refrained from engaging directly. Nadella, during a European AI tour, promoted Copilot as “the UI for AI,” positioning it as the interface to a broader AI ecosystem. However, his remarks avoided addressing Benioff’s criticisms, leaving Salesforce’s claims unanswered. Implications for Enterprise AI This debate highlights critical issues for businesses adopting AI technologies. While Salesforce has emphasized pragmatism, acknowledging AI’s uncertainties, Microsoft’s perceived lack of response risks leaving questions unanswered. The stakes are high, with both companies vying for leadership in a rapidly evolving market. What’s Next? How Microsoft responds—or doesn’t—will shape this rivalry’s trajectory. A direct rebuttal could clarify its AI vision, but staying silent risks ceding thought leadership to Salesforce. For businesses navigating the complexities of enterprise AI, understanding these dynamics is essential to making informed decisions. As the AI landscape unfolds, this conflict underscores the competitive importance of securing trust and demonstrating actionable value. For now, the war of words is as much about shaping perceptions as it is about delivering results. 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
December 10, 2024in Data, Enterprise, Laws, Salesforce
salesforce agentforce ai powered agentic agents
09Dec

Agentforce AI Transforming the Workplace

AI is Transforming the WorkplaceArtificial intelligence is revolutionizing how businesses operate. By driving productivity, efficiency, and scalability, AI agents are unlocking new possibilities in the modern business landscape. However, achieving AI’s full potential requires navigating a complex web of data sources, workflows, and legacy systems. Enter Agentforce, a complete AI system for enterprises unveiled at Dreamforce. This agentic platform integrates data, automation, and human collaboration into a unified solution, enabling organizations to scale autonomous AI agents across their operations using low-code tools. Here are five key takeaways from Dreamforce to help organizations leverage AI agents to transform business operations and software development. 1. An Agentic Platform is Crucial for Building Exceptional AI Agents “What we build and how we build it is changing dramatically in this world of AI.”— Alice Steinglass, Executive Vice President and General Manager, Salesforce Platform CEOs are increasingly prioritizing AI, recognizing its potential to solve complex problems and enhance experiences for employees, partners, and customers. However, many teams face significant challenges in meeting AI demands safely and effectively. Issues such as disconnected data, IT backlogs, tech debt, and limited resources create a gap between AI ambition and execution. Agentforce addresses these challenges by providing an integrated platform that connects data, AI, automation, and humans. During the Platform keynote, a live demo showcased how an Agentforce agent streamlined casting for a fictional sports reality TV show, “Hooping with the Stars.” The agent seamlessly integrated audition videos, casting data, and applicant details, enabling faster, more informed decisions. This demo highlighted the potential of an agentic platform to turn challenges into opportunities, empowering organizations to build secure, dynamic AI agents. 2. Low-Code Tools Make AI Agents Accessible to Everyone “The true value is about getting work done. Agents being able to take action is what empowers employees and drives customer success.”— Claire Cheng, Vice President of Software Engineering AI agents surpass traditional chatbots by not only answering questions but also making decisions and taking actions. This capability is transforming customer and employee experiences by eliminating repetitive tasks and enabling teams to focus on higher-value activities. For example, Wiley, a global leader in education and research, deployed Agentforce agents to handle routine inquiries. Within weeks, these agents improved case resolution rates by over 40%, outperforming the company’s previous chatbot solution. Despite their sophistication, Agentforce agents are easy to build. With no-code and low-code tools like Agent Builder and Prompt Builder, businesses can create agents tailored to their unique needs, enabling even non-technical users to innovate without technical headaches. 3. Unified Data Enhances Accuracy and Intelligence of AI Agents “By analyzing unstructured data, you can uncover deeper patterns and sentiments that structured data alone might miss, leading to better personalization and decision-making.”— Taraneh Whitefield, Senior Director, Product Marketing Data is the foundation of effective AI strategies. Salesforce’s Data Cloud has become a leader in addressing data challenges by eliminating silos and simplifying data integration. This year’s introduction of the Data Cloud Vector Database marked a significant milestone. The platform now enables the integration of unstructured data — such as PDFs, videos, and audio files — alongside structured data, offering a complete 360-degree view of customers. For example, Mascoma Bank leveraged Data Cloud to consolidate data from 66 legacy systems. This streamlined sales workflows and improved personalized recommendations, reducing manual data handling by 45 minutes per loan application. 4. API-Led Integration is Essential for Scalable AI Systems “The world of AI is truly the world of APIs.”— Param Kahlon, EVP/GM, Automation & Integration, Salesforce The success of AI depends on seamless integration. Organizations often manage hundreds of applications with unique update schedules and security requirements, making integration a critical challenge. MuleSoft’s API-led approach provides a solution, allowing organizations to unlock data from disparate systems. With the introduction of MuleSoft Topic Center, developers can now package APIs and metadata into actionable “Topics” for Agentforce agents. This innovation simplifies building agents that can interact with enterprise systems, as demonstrated in a live demo featuring Aston Martin’s VIP vehicle customization experience. 5. Better Decision-Making Depends on Empowering People with the Right Data “I’m free to focus on the things that really, really matter to me.”— Anya A’Hearn, Hall of Fame Tableau Visionary Efficient decision-making relies on fast access to actionable insights. Tools like Tableau Einstein, Tableau Pulse, and Tableau Agent empower users to interact with data through natural language queries. For instance, with Tableau Agent, users can ask questions in plain language, and the agent will create visualizations instantly. Tasks that once took hours now take minutes, freeing up time for more strategic decision-making. Empowering Autonomous Agents with Data, AI, and Automation The future of AI is here, and it hinges on the seamless integration of data, automation, and human expertise. Agentforce, alongside Salesforce Platform, Data Cloud, MuleSoft, and Tableau, is equipping organizations with the tools to harness AI’s full potential — whether it’s 24/7 customer service, automated workflows, or real-time data visualization. Discover how Agentforce can revolutionize your business 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
December 9, 2024in Artificial Intelligence, Data, Enterprise, Mulesoft, Salesforce
agentforce testing center
08Dec

Agentforce Testing Center

Salesforce has launched the Agentforce Testing Center, a groundbreaking tool for testing, deploying, and managing AI agents at scale. This innovative platform uses synthetically generated data to ensure AI agents perform accurate actions and deliver precise responses while providing comprehensive monitoring of usage and feedback. A New Era of AI Testing The Agentforce Testing Center operates within Salesforce’s enterprise-grade platform, integrated seamlessly with Data Cloud. It introduces no-code tools that allow organizations to test AI agents securely in sandbox environments, ensuring minimal risk to live production systems. By enabling robust testing and optimization, this tool helps businesses confidently deploy AI agents tailored to their needs. According to Adam Evans, EVP and GM for the Salesforce AI Platform, the Agentforce Testing Center is a critical tool for modern enterprises: “Agentforce is helping businesses create a limitless workforce. CIOs need tools for testing and monitoring agentic systems. This offering marks the next evolution of lifecycle management, enabling businesses to deploy trusted AI agents in a secure, repeatable way.” Key Capabilities of the Agentforce Testing Center Industry Implications Keith Kirkpatrick, Research Director at The Futurum Group, emphasized the importance of this offering: “To build trust in AI, businesses need robust tools to pressure test autonomous agents. Salesforce’s Testing Center delivers this capability, ensuring AI agents can reason, retrieve data, and adapt effectively.” Enhancing AI Agent Strategies The Agentforce Testing Center’s integration with Salesforce tools like DevOps Center and Change Sets further simplifies deployment workflows. Features like User Acceptance Testing (UAT) and real-time alerts in the Digital Wallet give teams deeper control and visibility into AI performance, ensuring business readiness before live deployment. As Salesforce continues to invest in AI innovation, including recent acquisitions to enhance Data Cloud security, the Agentforce Testing Center positions the CRM leader as a pioneer in responsible and scalable AI deployment. This offering exemplifies how businesses can create a “limitless workforce” while maintaining trust, transparency, and efficiency in AI operations. 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
December 8, 2024in Agentforce Platform, Data, Enterprise, Salesforce
computer vision models
05Dec

Computer Vision Models

Introduction to Visual Language Models (VLMs): The Future of Computer Vision Achieving a 28% Boost in Multimodal Image Search Accuracy with VLMs Until recently, artificial intelligence models were narrowly focused—either excelling in understanding text or interpreting images, but rarely both. This siloed approach limited their potential to process and connect different types of data. The development of general-purpose language models, like GPTs, marked a significant leap forward. These models transitioned AI from task-specific systems to powerful, versatile tools capable of handling a wide range of language-driven tasks. Yet, despite their advancements, language models and computer vision systems evolved separately, like having the ability to hear without seeing—or vice versa. Visual Language Models (VLMs) bridge this gap, combining the capabilities of language and vision to create multimodal systems. In this article, we’ll explore VLMs’ architecture, training methods, challenges, and transformative potential in fields like image search. We’ll also examine how implementing VLMs revolutionized an AI-powered search engine. What Are Visual Language Models (VLMs)? VLMs represent the next step in the evolution of AI: multimodal models capable of processing multiple data types, including text, images, audio, and video. Why Multimodal Models? The rise of general-purpose approaches has outpaced narrow, specialized systems in recent years. Here’s why: At their core, VLMs receive input in the form of images and text (called “instructs”) and produce textual responses. Their range of applications includes image classification, description, interpretation, and more. For example: These tasks showcase VLMs’ ability to tackle a variety of challenges, including zero-shot and one-shot scenarios, where minimal prior training is required. How Do VLMs Work? Core Components VLMs typically consist of three main components: Workflow Adapters: The Key to Multimodality Adapters facilitate interaction between the image encoder and LLM. There are two primary types: Training VLMs Pre-Training VLMs are built on pre-trained LLMs and image encoders. Pre-training focuses on linking text and image modalities, as well as embedding world knowledge from visual data. Three types of data are used: Alignment Alignment fine-tunes VLMs for high-quality responses. This involves: Quality Evaluation Evaluating VLM performance involves: Revolutionizing Image Search with VLMs Incorporating VLMs into search engines transforms user experience by integrating text and image inputs. Previous Pipeline VLM-Powered Pipeline With VLMs, image and text inputs are processed together, creating a streamlined, accurate system that outperforms traditional pipelines by 28%. Conclusion Visual Language Models are a game-changer for AI, breaking down barriers between language and vision. From multimodal search engines to advanced problem-solving, VLMs unlock new possibilities in computer vision and beyond. The future of AI is multimodal—and VLMs are leading the way. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
December 5, 2024in Artificial Intelligence, Data, Technology

Subscribe to our mailing list. Join our mail list to receive our newsletter

We'll keep you up to date with our latest news, insights, free resources, and much more.
Don't worry, we won't spam you.
gettectonic.com