2024 - gettectonic.com - Page 9
AI Won't Hurt Salesforce

AI Won’t Hurt Salesforce

Marc Benioff Dismisses AI Threats, Sets Sights on a Billion AI Agents in One Year Salesforce CEO Marc Benioff has no doubts about the transformative potential of AI for enterprise software, particularly Salesforce itself. At the core of his vision are AI agentsโ€”autonomous software bots designed to handle routine tasks, freeing up human workers to focus on more strategic priorities. โ€œWhat if your workforce had no limits? Thatโ€™s a question we couldnโ€™t even ask over the past 25 years of Salesforceโ€”or the 45 years Iโ€™ve been in software,โ€ Benioff said during an appearance on TechCrunch’s Equity podcast. The Billion-Agent Goal Benioff revealed that Salesforceโ€™s recently launched Agentforce platform is already being adopted by โ€œhundreds of customersโ€ and aims to deploy a billion AI agents within a year. These agents are designed to handle tasks across industriesโ€”from enhancing customer experiences at retail brands like Gucci to assisting patients with follow-ups in healthcare. To illustrate, Benioff shared his experience with Disneyโ€™s virtual Private Tour Guides. “The AI agent analyzed park flow, ride history, and preferences, then guided me to attractions I hadnโ€™t visited before,โ€ he explained. Competition with Microsoft and the AI Landscape While Benioff is bullish on AI, he hasnโ€™t hesitated to criticize competitorsโ€”particularly Microsoft. When Microsoft unveiled its new autonomous agents for Dynamics 365 in October, Benioff dismissed them as uninspired. โ€œCopilot is the new Clippy,โ€ he quipped, referencing Microsoftโ€™s infamous virtual assistant from the 1990s. Benioff also cited Gartner research highlighting data security issues and administrative flaws in Microsoftโ€™s AI tools, adding, โ€œCopilot has disappointed so many customers. Itโ€™s not transforming companies.โ€ However, industry skeptics argue that the real challenge to Salesforce isnโ€™t Microsoft but the wave of AI-powered startups disrupting traditional enterprise software. With tools like OpenAIโ€™s ChatGPT and Klarnaโ€™s in-house AI assistant โ€œKiki,โ€ companies are starting to explore GenAI solutions that can replace legacy platforms like Salesforce altogether. For example, Klarna recently announced it was moving away from Salesforce and Workday, favoring GenAI tools that enable seamless, conversational interfaces and faster data access. Why Salesforce Is Positioned to Win Despite the noise, Benioff remains confident that Salesforceโ€™s extensive data infrastructure gives it a significant edge. “We manage 230 petabytes of customer data with robust security and sharing models. Thatโ€™s what allows AI to thrive in our ecosystem,โ€ he said. While companies may question how other platforms like OpenAI handle data, Salesforce offers an integrated approach, reducing the need for complex data migrations to other clouds, such as Microsoft Azure. Salesforceโ€™s Own Use of AI Benioff also highlighted Salesforceโ€™s internal adoption of Agentforce, using AI agents in its customer service operations, sales processes, and help centers. โ€œIf youโ€™re authenticated on help.salesforce.com, youโ€™re already interacting with our agent,โ€ he noted. AI Startups: Threat or Opportunity? As for concerns about AI startups overtaking Salesforce, Benioff sees them as acquisition opportunities rather than existential threats. โ€œWeโ€™ve made over 60 acquisitions, many of them startups,โ€ he said. He pointed to Agentforce itself, which was built using technology from Airkit.ai, a startup founded by a former Salesforce employee. Salesforce Ventures initially invested in Airkit.ai before acquiring and integrating it into its platform. The Path Forward Benioff is resolute in his belief that AI wonโ€™t hurt Salesforceโ€”instead, it will revolutionize how businesses operate. While skeptics warn of a seismic shift in enterprise software, Benioffโ€™s strategy is clear: lean into AI, leverage data, and stay agile through innovation and acquisitions. โ€œWeโ€™re just getting started,โ€ he concluded, reiterating his vision for a future where AI agents expand the possibilities of work and customer experience like never before. 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 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

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Snowflake Security and Development

Snowflake Security and Development

Snowflake Unveils AI Development and Enhanced Security Features At its annual Build virtual developer conference, Snowflake introduced a suite of new capabilities focused on AI development and strengthened security measures. These enhancements aim to simplify the creation of conversational AI tools, improve collaboration, and address data security challenges following a significant breach earlier this year. AI Development Updates Snowflake announced updates to its Cortex AI suite to streamline the development of conversational AI applications. These new tools focus on enabling faster, more efficient development while ensuring data integrity and trust. Highlights include: These features address enterprise demands for generative AI tools that boost productivity while maintaining governance over proprietary data. Snowflake aims to eliminate barriers to data-driven decision-making by enabling natural language queries and easy integration of structured and unstructured data into AI models. According to Christian Kleinerman, Snowflakeโ€™s EVP of Product, the goal is to reduce the time it takes for developers to build reliable, cost-effective AI applications: โ€œWe want to help customers build conversational applications for structured and unstructured data faster and more efficiently.โ€ Security Enhancements Following a breach last May, where hackers accessed customer data via stolen login credentials, Snowflake has implemented new security features: These additions come alongside existing tools like the Horizon Catalog for data governance. Kleinerman noted that while Snowflakeโ€™s previous security measures were effective at preventing unauthorized access, the company recognizes the need to improve user adoption of these tools: โ€œItโ€™s on us to ensure our customers can fully leverage the security capabilities we offer. Thatโ€™s why weโ€™re adding more monitoring, insights, and recommendations.โ€ Collaboration Features Snowflake is also enhancing collaboration through its new Internal Marketplace, which enables organizations to share data, AI tools, and applications across business units. The Native App Framework now integrates with Snowpark Container Services to simplify the distribution and monetization of analytics and AI products. AI Governance and Competitive Position Industry analysts highlight the growing importance of AI governance as enterprises increasingly adopt generative AI tools. David Menninger of ISGโ€™s Ventana Research emphasized that Snowflakeโ€™s governance-focused features, such as LLM observability, fill a critical gap in AI tooling: โ€œTrustworthy AI enhancements like model explainability and observability are vital as enterprises scale their use of AI.โ€ With these updates, Snowflake continues to compete with Databricks and other vendors. Its strategy focuses on offering both API-based flexibility for developers and built-in tools for users seeking simpler solutions. By combining innovative AI development tools with robust security and collaboration features, Snowflake aims to meet the evolving needs of enterprises while positioning itself as a leader in the data platform and 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 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 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 Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You donโ€™t need to worry about tracking licenses. Read more

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AI Research Agents

AI Research Agents

AI Research Agents: Transforming Knowledge Discovery by 2025 (Plus the Top 3 Free Tools) The research world is on the verge of a groundbreaking shift, driven by the evolution of AI research agents. By 2025, these agents are expected to move beyond being mere tools to becoming transformative assets for knowledge discovery, revolutionizing industries such as marketing, science, and beyond. Human researchers are inherently limitedโ€”they cannot scan 10,000 websites in an hour or analyze data at lightning speed. AI agents, however, are purpose-built for these tasks, providing efficiency and insights far beyond human capabilities. Here, we explore the anticipated impact of AI research agents and highlight three free tools redefining this space (spoiler alert: itโ€™s not ChatGPT or Perplexity!). AI Research Agents: The New Era of Knowledge Exploration By 2030, the AI research market is projected to skyrocket from $5.1 billion in 2024 to $47.1 billion. This explosive growth represents not just advancements in AI but a fundamental transformation in how knowledge is gathered, analyzed, and applied. Unlike traditional AI systems, which require constant input and supervision, AI research agents function more like dynamic research assistants. They adapt their approach based on outcomes, handle vast quantities of data, and generate actionable insights with remarkable precision. Key Differentiator: These agents leverage advanced Retrieval Augmented Generation (RAG) technology, ensuring accuracy by pulling verified data from trusted sources. Equipped with anti-hallucination algorithms, they maintain factual integrity while citing their sourcesโ€”making them indispensable for high-stakes research. The Technology Behind AI Research Agents AI research agents stand out due to their ability to: For example, an AI agent can deliver a detailed research report in 30 minutes, a task that might take a human team days. Why AI Research Agents Matter Now The timing couldnโ€™t be more critical. The volume of data generated daily is overwhelming, and human researchers often struggle to keep up. Meanwhile, Googleโ€™s focus on Experience, Expertise, Authoritativeness, and Trustworthiness (EEAT) has heightened the demand for accurate, well-researched content. Some research teams have already reported time savings of up to 70% by integrating AI agents into their workflows. Beyond speed, these agents uncover perspectives and connections often overlooked by human researchers, adding significant value to the final output. Top 3 Free AI Research Tools 1. Stanford STORM Overview: STORM (Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking) is an open-source system designed to generate comprehensive, Wikipedia-style articles. Learn more: Visit the STORM GitHub repository. 2. CustomGPT.ai Researcher Overview: CustomGPT.ai creates highly accurate, SEO-optimized long-form articles using deep Google research or proprietary databases. Learn more: Access the free Streamlit app for CustomGPT.ai. 3. GPT Researcher Overview: This open-source agent conducts thorough research tasks, pulling data from both web and local sources to produce customized reports. Learn more: Visit the GPT Researcher GitHub repository. The Human-AI Partnership Despite their capabilities, AI research agents are not replacements for human researchers. Instead, they act as powerful assistants, enabling researchers to focus on creative problem-solving and strategic thinking. Think of them as tireless collaborators, processing vast amounts of data while humans interpret and apply the findings to solve complex challenges. Preparing for the AI Research Revolution To harness the potential of AI research agents, researchers must adapt. Universities and organizations are already incorporating AI training into their programs to prepare the next generation of professionals. For smaller labs and institutions, these tools present a unique opportunity to level the playing field, democratizing access to high-quality research capabilities. Looking Ahead By 2025, AI research agents will likely reshape the research landscape, enabling cross-disciplinary breakthroughs and empowering researchers worldwide. From small teams to global enterprises, the benefits are immenseโ€”faster insights, deeper analysis, and unprecedented innovation. As with any transformative technology, challenges remain. But the potential to address some of humanityโ€™s biggest problems makes this an AI revolution worth embracing. Now is the time to prepare and make the most of these groundbreaking tools. 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 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 Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You donโ€™t need to worry about tracking licenses. Read more

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healthcare Can prioritize ai governance

Healthcare Can Prioritize AI Governance

As artificial intelligence gains momentum in healthcare, itโ€™s critical for health systems and related stakeholders to develop robust AI governance programs. AIโ€™s potential to address challenges in administration, operations, and clinical care is drawing interest across the sector. As this technology evolves, the range of applications in healthcare will only broaden.

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Salesforce CPQ Check Up

Salesforce CPQ Check Up

A Salesforce CPQ Check Up is a comprehensive review of your systemโ€™s configuration and performance. It assesses how well your CPQ solution integrates with your business processes, highlighting any gaps hindering your sales efforts. From pricing rules to approval processes, a health check ensures seamless functionality and equips your sales reps with the tools they need to succeed.

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Pioneering AI-Driven Customer Engagement

Pioneering AI-Driven Customer Engagement

With Salesforce at the forefront of the AI revolution, Agentforce, introduced at Dreamforce, represents the next phase in customer service automation. It integrates AI and human collaboration to automate repetitive tasks, freeing human talent for more strategic activities, ultimately improving customer satisfaction. Tallapragada emphasized how this AI-powered tool enables businesses, particularly in the Middle East, to scale operations and enhance efficiency, aligning with the region’s appetite for growth and innovation.

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Agentic AI Race

Agentic AI Race

This announcement precedes the release of Salesforceโ€™s competing Agentforce platform, set to debut for general use on Oct. 25. Salesforce CEO Marc Benioff has publicly criticized Microsoftโ€™s AI technology, calling out Copilotโ€™s data security risks and expressing doubts about its value for business customers.

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Insurance Brokerage Financial Services Cloud

Insurance Brokerage Financial Services Cloud

Salesforce has introduced Financial Services Cloud for Insurance Brokerages, an AI-powered platform set to launch in February 2025, designed to automate and enhance client management, policy servicing, and commission processing for insurance brokerages. Built on Salesforceโ€™s core CRM system, Insurance Brokerage Financial Services Cloud streamlines traditionally time-consuming tasks like policy renewals, employee benefits management, and commission splits, aiming to consolidate operations and reduce operational expenses.

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Where LLMs Fall Short

Where LLMs Fall Short

Large Language Models (LLMs) have transformed natural language processing, showcasing exceptional abilities in text generation, translation, and various language tasks. Models like GPT-4, BERT, and T5 are based on transformer architectures, which enable them to predict the next word in a sequence by training on vast text datasets. How LLMs Function LLMs process input text through multiple layers of attention mechanisms, capturing complex relationships between words and phrases. Here’s an overview of the process: Tokenization and Embedding Initially, the input text is broken down into smaller units, typically words or subwords, through tokenization. Each token is then converted into a numerical representation known as an embedding. For instance, the sentence โ€œThe cat sat on the matโ€ could be tokenized into [“The”, “cat”, “sat”, “on”, “the”, “mat”], each assigned a unique vector. Multi-Layer Processing The embedded tokens are passed through multiple transformer layers, each containing self-attention mechanisms and feed-forward neural networks. Contextual Understanding As the input progresses through layers, the model develops a deeper understanding of the text, capturing both local and global context. This enables the model to comprehend relationships such as: Training and Pattern Recognition During training, LLMs are exposed to vast datasets, learning patterns related to grammar, syntax, and semantics: Generating Responses When generating text, the LLM predicts the next word or token based on its learned patterns. This process is iterative, where each generated token influences the next. For example, if prompted with โ€œThe Eiffel Tower is located in,โ€ the model would likely generate โ€œParis,โ€ given its learned associations between these terms. Limitations in Reasoning and Planning Despite their capabilities, LLMs face challenges in areas like reasoning and planning. Research by Subbarao Kambhampati highlights several limitations: Lack of Causal Understanding LLMs struggle with causal reasoning, which is crucial for understanding how events and actions relate in the real world. Difficulty with Multi-Step Planning LLMs often struggle to break down tasks into a logical sequence of actions. Blocksworld Problem Kambhampati’s research on the Blocksworld problem, which involves stacking and unstacking blocks, shows that LLMs like GPT-3 struggle with even simple planning tasks. When tested on 600 Blocksworld instances, GPT-3 solved only 12.5% of them using natural language prompts. Even after fine-tuning, the model solved only 20% of the instances, highlighting the modelโ€™s reliance on pattern recognition rather than true understanding of the planning task. Performance on GPT-4 Temporal and Counterfactual Reasoning LLMs also struggle with temporal reasoning (e.g., understanding the sequence of events) and counterfactual reasoning (e.g., constructing hypothetical scenarios). Token and Numerical Errors LLMs also exhibit errors in numerical reasoning due to inconsistencies in tokenization and their lack of true numerical understanding. Tokenization and Numerical Representation Numbers are often tokenized inconsistently. For example, โ€œ380โ€ might be one token, while โ€œ381โ€ might split into two tokens (โ€œ38โ€ and โ€œ1โ€), leading to confusion in numerical interpretation. Decimal Comparison Errors LLMs can struggle with decimal comparisons. For example, comparing 9.9 and 9.11 may result in incorrect conclusions due to how the model processes these numbers as strings rather than numerically. Examples of Numerical Errors Hallucinations and Biases Hallucinations LLMs are prone to generating false or nonsensical content, known as hallucinations. This can happen when the model produces irrelevant or fabricated information. Biases LLMs can perpetuate biases present in their training data, which can lead to the generation of biased or stereotypical content. Inconsistencies and Context Drift LLMs often struggle to maintain consistency over long sequences of text or tasks. As the input grows, the model may prioritize more recent information, leading to contradictions or neglect of earlier context. This is particularly problematic in multi-turn conversations or tasks requiring persistence. Conclusion While LLMs have advanced the field of natural language processing, they still face significant challenges in reasoning, planning, and maintaining contextual accuracy. These limitations highlight the need for further research and development of hybrid AI systems that integrate LLMs with other techniques to improve reasoning, consistency, and overall performance. 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 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 Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You donโ€™t need to worry about tracking licenses. Read more

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user q and a

Handling Duplicate Phone Numbers in Salesforce Automations

Iโ€™m working with a list of customers in Salesforce, where duplicate detection is enabled for the phone field. When manually creating a new customer with an existing phone number, Salesforce displays a warning prompt asking if I want to proceed. The warning doesnโ€™t block the save; it simply alerts me to the duplicate. However, when attempting to insert a record with the same phone number using MAKE, I encounter the following error: RuntimeError[400]: A duplicate record was found. Are you sure you want to create the record? This indicates that the automation is being blocked due to the duplicate detection rules. Solution Options Here are a few strategies to address this and allow the record to be inserted: Best Practices If you need help setting up any of these solutions, let Tectonic know! 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 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

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Salesforce and Firmable

Salesforce and Firmable

Firmable Launches Salesforce Integration to Enhance CRM Workflows Firmable has unveiled its latest integration with Salesforce, further expanding its CRM ecosystem to support over 20,000 Salesforce users across Australia. By embedding its extensive Australian dataset directly into Salesforce, Firmable empowers businesses to optimize workflows, improve productivity, and elevate their sales and marketing efforts. This integration adds to Firmable’s suite of CRM solutions, which also includes compatibility with platforms like HubSpot, making its rich dataset an integral part of daily business operations. Key Benefits of the Firmable-Salesforce Integration A Comprehensive Solution for Australian Businesses Firmableโ€™s integration with Salesforce brings unparalleled ease of use and precision to CRM workflows. By embedding its rich Australian data into everyday tools, businesses can streamline lead generation, enhance customer engagement, and boost sales effectiveness. ๐Ÿ””๐Ÿ”” Follow us on LinkedIn ๐Ÿ””๐Ÿ”” Ready to transform your sales and marketing strategies? Firmable is now available for trial or purchase at firmable.com. 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 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

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