Google Cloud

18Dec

Google Prepares AI-Powered Jarvis Agent

Google Prepares AI-Powered Jarvis Agent for Automated Browser Tasks in Chrome Google is reportedly gearing up to launch “Project Jarvis,” an AI-powered browser agent designed to automate tasks directly within the Chrome ecosystem. According to The Information, the tool is expected to roll out in December to select users and will leverage Google’s advanced Gemini 2.0 AI model. Jarvis aims to simplify repetitive online tasks, such as organizing information or booking reservations, offering a seamless and efficient digital assistant embedded within Chrome. This initiative reflects Google’s broader vision to enhance user experiences by automating web-based routines, making its browser a central hub for task automation. Anthropic Expands Desktop Automation with Claude 3.5 Sonnet Anthropic, a key player in the AI landscape, has advanced its Claude 3.5 model with a new “Computer Use” feature, enabling direct interaction with a user’s desktop. This update allows Claude to perform tasks such as typing, clicking, and managing multiple applications, making it a powerful tool for automating workflows like data entry, document management, and customer service. Available through APIs and platforms like Amazon Bedrock and Google Cloud’s Vertex AI, Claude’s new capabilities position it as a versatile solution for businesses seeking desktop-level automation, contrasting Google Jarvis’s browser-specific approach. By interpreting screen elements, Claude’s “Computer Use” mode supports broader applications beyond web tasks, offering businesses an edge in efficiency and scalability. How Google Jarvis Stands Out Unlike Anthropic’s desktop-oriented Claude Sonnet, Google Jarvis focuses on automating tasks within Chrome. Jarvis analyzes screenshots of web pages, interprets user commands, and executes actions like clicks or data entry. While still in development, Jarvis’s design suggests a future where mundane web-based tasks are seamlessly handled by AI. Powered by Google’s Gemini 2.0 language model, Jarvis is tailored for users who prioritize web-specific functions, creating a user-friendly assistant that requires no external software. This aligns with Google’s strategy to deepen integration within its ecosystem, making Chrome a more intuitive and productive environment. Microsoft’s Copilot Agents Lead Business Automation Microsoft, meanwhile, continues to enhance its Copilot AI agents, particularly within Dynamics 365. These specialized agents are designed to automate industry-specific workflows, from lead qualification in sales to financial data reconciliation. Unlike Google Jarvis or Anthropic Claude, Microsoft’s Copilot agents target enterprise users, embedding automation within business applications like Teams, Outlook, and SharePoint. With tools like Copilot Studio, organizations can customize workflows to meet specific needs, offering a level of flexibility that resonates with enterprise clients. Early adopters, including Vodafone and Cognizant, have reported significant productivity gains through these integrations. Microsoft’s efforts position Copilot as a robust partner for day-to-day operations, transforming tasks like analysis, project coordination, and document management into automated, efficient processes. Competing Visions for AI Agents As Google, Anthropic, and Microsoft refine their AI strategies, they’re carving out distinct niches in the AI agent landscape: These approaches highlight the diverse applications of AI agents, from enhancing individual user experiences to transforming business 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 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

December 18, 2024in Data, Enterprise, Google, Salesforce0 Comments

25Nov

Potential of GenAI in Healthcare

Clinicians spend about 28 hours per week on administrative tasks, mainly clinical documentation and communication. Medical and claims staff reported even higher administrative loads, with 34 and 36 hours spent weekly on tasks like documentation, communication, and prior authorization. Many respondents linked these demands directly to burnout, with 77% of claims staff, 81% of medical staff, and 82% of clinicians citing administrative burdens as significant contributors. Additionally, 78% of payer executives and 85% of provider executives noted that administrative work is a key driver of staffing shortages.

November 25, 2024in Generative AI, Salesforce Data Cloud, Salesforce Health and Life Sciences Solutions, Salesforce Implementation Services0 Comments

16Nov

AI Agent Rivalry

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

November 16, 2024in Agentforce Platform, Data, Enterprise, Generative AI, Google, Salesforce0 Comments

30Oct

AI Agents Interview

In the rapidly evolving world of large language models and generative AI, a new concept is gaining momentum: AI agents. AI Agents Interview explores. AI agents are advanced tools designed to handle complex tasks that traditionally required human intervention. While they may be confused with robotic process automation (RPA) bots, AI agents are much more sophisticated, leveraging generative AI technology to execute tasks autonomously. Companies like Google are positioning AI agents as virtual assistants that can drive productivity across industries. In this Q&A, Jason Gelman, Director of Product Management for Vertex AI at Google Cloud, shares insights into Google’s vision for AI agents and some of the challenges that come with this emerging technology. AI Agents Interview How does Google define AI agents? Jason Gelman: An AI agent is something that acts on your behalf. There are two key components. First, you empower the agent to act on your behalf by providing instructions and granting necessary permissions—like authentication to access systems. Second, the agent must be capable of completing tasks. This is where large language models (LLMs) come in, as they can plan out the steps to accomplish a task. What used to require human planning is now handled by the AI, including gathering information and executing various steps. What are current use cases where AI agents can thrive? Gelman: AI agents can be useful across a wide range of industries. Call centers are a common example where customers already expect AI support, and we’re seeing demand there. In healthcare, organizations like Mayo Clinic are using AI agents to sift through vast amounts of information, helping professionals navigate data more efficiently. Different industries are exploring this technology in unique ways, and it’s gaining traction across many sectors. What are some misconceptions about AI agents? Gelman: One major misconception is that the technology is more advanced than it actually is. We’re still in the early stages, building critical infrastructure like authentication and function-calling capabilities. Right now, AI agents are more like interns—they can assist, but they’re not yet fully autonomous decision-makers. While LLMs appear powerful, we’re still some time away from having AI agents that can handle everything independently. Developing the technology and building trust with users are key challenges. I often compare this to driverless cars. While they might be safer than human drivers, we still roll them out cautiously. With AI agents, the risks aren’t physical, but we still need transparency, monitoring, and debugging capabilities to ensure they operate effectively. How can enterprises balance trust in AI agents while acknowledging the technology is still evolving? Gelman: Start simple and set clear guardrails. Build an AI agent that does one task reliably, then expand from there. Once you’ve proven the technology’s capability, you can layer in additional tasks, eventually creating a network of agents that handle multiple responsibilities. Right now, most organizations are still in the proof-of-concept phase. Some companies are using AI agents for more complex tasks, but for critical areas like financial services or healthcare, humans remain in the loop to oversee decision-making. It will take time before we can fully hand over tasks to AI agents. AI Agents Interview What is the difference between Google’s AI agent and Microsoft Copilot? Gelman: Microsoft Copilot is a product designed for business users to assist with personal tasks. Google’s approach with AI agents, particularly through Vertex AI, is more focused on API-driven, developer-based solutions that can be integrated into applications. In essence, while Copilot serves as a visible assistant for users, Vertex AI operates behind the scenes, embedded within applications, offering greater flexibility and control for enterprise customers. The real potential of AI agents lies in their ability to execute a wide range of tasks at the API level, without the limitations of a low-code/no-code interface. 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

October 30, 2024in Generative AI

21Oct

Google on Google AI

As a leading cloud provider, Google Cloud is also a major player in the generative AI market. Google on Google AI provides insights into this new tool. In the past two years, Google has been in a competitive battle with AWS, Microsoft, and OpenAI to gain dominance in the generative AI space. Recently, Google introduced several generative Artificial Intelligence products, including its flagship large language model, Gemini, and the Vertex AI Model Garden. Last week, it also unveiled Audio Overview, a tool that transforms documents into audio discussions. Despite these advancements, Google has faced criticism for lagging in some areas, such as issues with its initial image generation tool, like X’s Grok. However, the company remains committed to driving progress in generative AI. Google’s strategy focuses not only on delivering its proprietary models but also offering a broad selection of third-party models through its Model Garden. Google’s Thoughts on Google AI Warren Barkley, head of product for Google Cloud’s Vertex AI, GenAI, and machine learning, emphasized this approach in a recent episode of the Targeting AI podcast. He noted that a key part of Google’s ongoing effort is ensuring users can easily transition to more advanced models. “A lot of what we did in the early days, and we continue to do now, is make it easy for people to move to the next generation,” Barkley said. “The models we built 18 months ago are a shadow of what we have today. So, providing pathways for people to upgrade and stay on the cutting edge is critical.” Google is also focused on helping users select the right AI models for specific applications. With over 100 closed and open models available in the Model Garden, evaluating them can be challenging for customers. To address this, Google introduced evaluation tools that allow users to test prompts and compare model responses. In addition, Google is exploring advancements in Artificial Intelligence reasoning, which it views as crucial to driving the future of generative AI. 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

October 21, 2024in Generative AI, Google

12Oct

SingleStore Acquires BryteFlow

SingleStore Acquires BryteFlow, Paving the Way for Real-Time Analytics and Next-Gen AI Use Cases SingleStore, the world’s only database designed to transact, analyze, and search petabytes of data in milliseconds, has announced its acquisition of BryteFlow, a leading data integration platform. This move enhances SingleStore’s capabilities to ingest data from diverse sources—including SAP, Oracle, and Salesforce—while empowering users to operationalize data from their CRM and ERP systems. With the acquisition, SingleStore will integrate BryteFlow’s data integration technology into its core offering, launching a new experience called SingleConnect. This addition will complement SingleStore’s existing functionalities, enabling users to gain deeper insights from their data, accelerate real-time analytics, and support emerging generative AI (GenAI) use cases. “This acquisition marks a pivotal step in our mission to deliver unparalleled speed, scale, and simplicity,” said Raj Verma, CEO of SingleStore. “Customer demands are evolving rapidly due to shifts in big data storage formats and advancements in generative AI. We believe that data is the foundation of all intelligence, and SingleConnect comes at a perfect time to address this need.” BryteFlow’s platform provides scalable change data capture (CDC) capabilities across multiple data sources, ensuring data integrity between source and target. It integrates seamlessly with major cloud platforms like AWS, Microsoft Azure, and Google Cloud, making it a powerful tool for cloud-based data warehouses and data lakes. Its no-code interface allows for easy and accessible data integration, ensuring that existing BryteFlow customers will experience uninterrupted service and ongoing support. “By combining BryteFlow’s real-time data integration expertise with SingleStore’s capabilities, we aim to help global organizations extract maximum value from their data and scale modern applications,” said Pradnya Bhandary, CEO of BryteFlow. “With SingleConnect, developers will find it easier and faster to access enterprise data sources, tackle complex workloads, and deliver exceptional experiences to their customers.” 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

October 12, 2024in Data, Enterprise, Generative AI, Google, Salesforce, Technology

08Oct

Data Cloud Model

While many Salesforce products like Sales Cloud, Service Cloud, Education Cloud, Health Cloud, and various industry clouds are built on a common ‘core’ platform that shares the same datastore (an Oracle relational database), Salesforce Data Cloud operates on (Data Cloud Model) a distinct architecture and technology stack. This insight explores the data models and related concepts that underpin the platform. Data Cloud Architecture and Storage Starting with the storage layer, Salesforce Data Cloud integrates multiple services: This combination enables Data Cloud to provide a petabyte-scale data store, overcoming the scalability and performance constraints of traditional relational databases. The physical architecture of Data Cloud is represented as a set of data objects, which form the foundation for how data is ingested, harmonized, and activated within the platform. Key Data Cloud Objects Data Source A Data Source is the initial data layer in Data Cloud, representing an external platform or system from which your data originates. Data Sources can include: Data Stream A Data Stream is an entity extracted from a Data Source, such as ‘Orders’ from Commerce Cloud, ‘Contacts’ from Sales Cloud, or ‘Subscribers’ from Marketing Cloud. Once a Data Source is connected to Data Cloud, Data Streams provide access to these entities and require assignment to a category: Profile, Engagement, or Other. A single Data Source can contain multiple Data Streams. Data Source Object (DSO) Data Streams are ingested into Data Source Objects (DSOs), which serve as temporary staging stores containing data in its raw, native format (e.g., CSV files). Minor transformations can be applied to fields during data ingestion. Data Lake Object (DLO) The next step in the data flow is the Data Lake Object (DLO). DLOs are typed, schema-based, materialized views that reside in the data lake (Amazon S3) and are generally stored as Apache Parquet files. This format, along with Apache Iceberg’s abstraction layer, supports efficient data storage and retrieval. DLOs are the first objects available for inspection and preparation of data, including field mapping and additional transformations. Data Model Object (DMO) Unlike DSOs and DLOs, which use a physical data store, Data Model Objects (DMOs) provide a virtual, non-materialized view into the data lake. Queries against a DMO return results based on the current data snapshot in DLOs, without storing them. DMOs can aggregate attributes from different Data Streams, Calculated Insights, and other sources. DMOs inherit their category from the first DLO mapped to them, and only DLOs with the same category can be mapped to the DMO. Like Salesforce objects, DMOs offer a canonical data model with pre-defined attributes (standard objects), and you can also create custom DMOs (custom objects). DMOs can have one-to-one or many-to-one relationships with other DMOs. Currently, there are 89 standard DMOs in Data Cloud, and this number is growing to support more use cases. DMOs are organized into various subject areas, including: Data Spaces Data Spaces offer logical partitions, allowing data to be segregated between different brands, regions, or departments, without the need for multiple Data Cloud instances. They also align with a Software Development Lifecycle (SDLC), enabling you to stage and test Data Objects in separate environments without affecting production data. Data Sources, Data Streams, and DLOs can be shared across Data Spaces, while DMOs and other platform features are isolated based on user permissions. Final Thoughts With the drop in storage costs and advancements in technology, companies now have vast datasets at their disposal. Every customer interaction, whether it’s a purchase, an email open, or a web page view, can be captured and stored. When properly organized, this data allows businesses to understand customers better, predict their needs, personalize interactions, and much more. However, one of Salesforce’s challenges is that its original platform, built over 20 years ago, was designed for a different era and struggles with handling voluminous, “big” data. Salesforce Data Cloud addresses these limitations with a modern architecture that overcomes the constraints of relational databases. Data Cloud is poised to become the backbone of the Salesforce platform, supporting the data needs of customers and products for the next two decades. If you’re interested in learning how to implement and integrate Salesforce Data Cloud in your organization, following best practices, reach out 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 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

October 8, 2024in Salesforce Data Cloud, Unified Knowledge

05Oct

AI Era Demands an Intelligent Data Infrastructure

Why the AI Era Demands an Intelligent Data Infrastructure NetApp aims to bridge the gap between cloud-based AI models and on-premises data management systems, addressing a major challenge many organizations face as they explore emerging AI capabilities, such as generative AI. AI Era Demands an Intelligent Data Infrastructure. While the most advanced AI tools often reside in hyperscale clouds, the key data of most organizations remains on-premises. As companies move from AI experimentation to scaling their initiatives, they face the complex task of closing the divide between their data and AI. At first glance, the solution seems simple: Organizations can integrate their proprietary data with cloud-based AI services to enhance large language models (LLMs) and other AI tools, leveraging their industry-specific expertise to generate unique insights and drive value. In fact, a recent Enterprise Strategy Group (ESG) survey found that 84% of respondents believed incorporating enterprise data was critical to supporting their generative AI efforts. However, achieving this at scale is far more difficult than it appears. Many organizations are reluctant to send their valuable data to public clouds due to concerns about security, such as exposing sensitive intellectual property or personal information. Additionally, managing the process of transferring large datasets, creating data copies, updating models with fresh data, and keeping track of everything over time adds cost and complexity. Beyond these logistical issues, many organizations struggle with a fundamental challenge: poor data management. Rapid data growth, fragmentation, and a lack of understanding of their own data forces business users and data scientists to spend excessive time “data wrangling”—the process of identifying, gathering, and preparing data for AI models. This inefficiency delays training and deploying models, pushing back the point of inference, which is the key to unlocking value from AI. Increased latency also raises the risk that inferencing is based on outdated data, leading to repeated cycles and further inefficiency. This challenge has made data management a critical obstacle in AI adoption. ESG research identified poor data quality as the top challenge organizations face when implementing AI. Inadequate data is rapidly becoming a roadblock that hampers AI initiatives across industries. To address these challenges, NetApp has shifted its focus toward building an “intelligent data infrastructure” for the AI era. At the company’s Insight 2024 conference, NetApp outlined a strategy to support AI success by bridging the gap between cloud-based AI models and on-premises data environments. The company’s vision includes simplifying, automating, and mitigating risks in the data management workflow needed to scale AI in the enterprise. NetApp’s approach includes innovations like a global metadata namespace and enhancements to its OnTap software, which enable exploration, classification, and management of data across the NetApp ecosystem. These capabilities integrate directly into AI data pipelines, enabling scalable searches and retrieval-augmented generation inferencing. Additionally, NetApp’s new “disaggregated” storage architecture will support more cost-effective scaling for compute-heavy AI tasks such as model training. NetApp’s strategy also extends to the public cloud, where it already has strong partnerships with all three hyperscalers, offering NetApp services as first-party options in their clouds. The company is expanding this by developing additional cloud-native capabilities for AI, such as integrating Azure NetApp Files with Microsoft Azure AI services, FSx for NetApp OnTap with Amazon Bedrock, and Google Cloud NetApp Volumes with Google Vertex AI and BigQuery. These integrations will allow organizations to securely enrich public cloud-based AI models with their on-premises data, creating a scalable and secure environment for AI development. While some aspects of NetApp’s strategy will be delivered over the next year, many cloud integrations will be available sooner. NetApp is also building a partner ecosystem, including hardware partners like Lenovo and Nvidia, software ISVs, and service providers like Domino Data Labs, in addition to its cloud alliances. With this vision for intelligent data infrastructure, NetApp is positioning itself as a key enabler of AI-driven innovation, and it will be intriguing to watch how this strategy unfolds in the coming months. 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

October 5, 2024in AI Tools, Data, Enterprise, Generative AI, Google

04Oct

Databricks Tools

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

October 4, 2024in AI Tools, Data, Enterprise, Generative AI, Google, Snowflake

03Oct

Scale and AI Influence Shape Partner Ecosystems

Hyperscalers’ Scale and AI Influence Shape Partner Ecosystems Despite their seemingly saturated networks, the largest cloud vendors continue to dominate as top ecosystems for service providers, according to a recent survey. Hyperscalers are playing a critical role in partner alliances, a trend that has only intensified in recent years. A study released by Tercera, an investment firm specializing in IT services, highlights the dominance of cloud giants AWS, Google Cloud, and Microsoft Azure in the partner ecosystem landscape. More than 50% of the 250 technology service providers surveyed by Tercera identified one of these three vendors as their primary partner. This data comes from Tercera’s third annual report on the Top 30 Partner Ecosystems. The report emphasizes the “gravitational pull” of these hyperscalers, attracting partners despite their already vast networks. Each of the major cloud vendors maintains relationships with thousands of software and services partners. “The hyperscalers continue to defy the law of large numbers when you look at how many partners are in their ecosystems,” said Michelle Swan, CMO at Tercera. The Shift in Channel Alliances The emergence of cloud vendors as top partners for service providers has been evident since at least 2021. That year, a survey by Accenture of 1,150 channel companies found that AWS, Google, and Microsoft accounted for the majority of revenue for these partners. This represents a significant shift in channel economics, where traditionally large hardware companies occupied the top spots in partner alliances. AI’s Role in Partner Ecosystem Growth The rise of generative AI (GenAI) is reshaping alliance strategies, as service providers increasingly align themselves with hyperscalers and their AI technology partners. For instance, AWS channel partners interested in GenAI are likely to work with Anthropic, following Amazon’s $4 billion investment in the AI company. Meanwhile, Microsoft partners tend to collaborate with OpenAI, as Microsoft has committed up to $13 billion in investments to expand their partnership. “They have their own solar systems,” Swan remarked, referencing AWS, Google, Microsoft, and the AI startups within their ecosystems. Tiers of Partner Ecosystems Tercera categorizes its top 30 ecosystems into three tiers. The first tier, known as “market anchors,” includes AWS, Google, Microsoft, and large independent software vendors (ISVs) such as Salesforce and ServiceNow. The second tier, “market movers,” features publicly traded vendors with evolving partner ecosystems. The third tier, “market challengers,” is made up of privately held vendors with a partner-centric focus, such as Anthropic and OpenAI. Generative AI Ecosystem Survey A 2024 generative AI survey conducted by TechTarget and its Enterprise Strategy Group supports the idea that the leading cloud vendors play a central role in AI ecosystems. In a poll of 610 GenAI decision-makers and users, Microsoft topped the list of ecosystems supporting GenAI initiatives, with 54% of respondents citing it as the best ecosystem. Microsoft’s partner, OpenAI, followed with 35%. Google and AWS ranked third and fourth, with 30% and 24% of the responses, respectively. The survey covered a wide range of industries, including business services and IT, further reinforcing the dominant role hyperscalers play in shaping AI and partner ecosystems. 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

October 3, 2024in Data, Enterprise, Generative AI, Google, Hyperforce, Salesforce, Technology

30Sep

Small Language Models Explained

Exploring Small Language Models (SLMs): Capabilities and Applications Large Language Models (LLMs) have been prominent in AI for some time, but Small Language Models (SLMs) are now enhancing our ability to work with natural and programming languages. While LLMs excel in general language understanding, certain applications require more accuracy and domain-specific knowledge than these models can provide. This has created a demand for custom SLMs that offer LLM-like performance while reducing runtime costs and providing a secure, manageable environment. In this insight, we dig down into the world of SLMs, exploring their unique characteristics, benefits, and applications. We also discuss fine-tuning methods applied to Llama-2–13b, an SLM, to address specific challenges. The goal is to investigate how to make the fine-tuning process platform-independent. We selected Databricks for this purpose due to its compatibility with major cloud providers like Azure, Amazon Web Services (AWS), and Google Cloud Platform. What Are Small Language Models? In AI and natural language processing, SLMs are lightweight generative models with a focus on specific tasks. The term “small” refers to: SLMs like Google Gemini Nano, Microsoft’s Orca-2–7b, and Meta’s Llama-2–13b run efficiently on a single GPU and include over 5 billion parameters. SLMs vs. LLMs Applications of SLMs SLMs are increasingly used across various sectors, including healthcare, technology, and beyond. Common applications include: Fine-Tuning Small Language Models Fine-tuning involves additional training of a pre-trained model to make it more domain-specific. This process updates the model’s parameters with new data to enhance its performance in targeted applications, such as text generation or question answering. Hardware Requirements for Fine-Tuning The hardware needs depend on the model size, project scale, and dataset. General recommendations include: Data Preparation Preparing data involves extracting text from PDFs, cleaning it, generating question-and-answer pairs, and then fine-tuning the model. Although GPT-3.5 was used for generating Q&A pairs, SLMs can also be utilized for this purpose based on the use case. Fine-Tuning Process You can use HuggingFace tools for fine-tuning Llama-2–13b-chat-hf. The dataset was converted into a HuggingFace-compatible format, and quantization techniques were applied to optimize performance. The fine-tuning lasted about 16 hours over 50 epochs, with the cost around $100/£83, excluding trial costs. Results and Observations The fine-tuned model demonstrated strong performance, with over 70% of answers being highly similar to those generated by GPT-3.5. The SLM achieved comparable results despite having fewer parameters. The process was successful on both AWS and Databricks platforms, showcasing the model’s adaptability. SLMs have some limitations compared to LLMs, such as higher operational costs and restricted knowledge bases. However, they offer benefits in efficiency, versatility, and environmental impact. As SLMs continue to evolve, their relevance and popularity are likely to increase, especially with new models like Gemini Nano and Mixtral entering the market. 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

September 30, 2024in Salesforce

28Sep

Salesforce Data Cloud and Zero Copy

As organizations across industries gather increasing amounts of data from diverse sources, they face the challenge of making that data actionable and deriving real-time insights. With Salesforce Data Cloud and zero copy architecture, organizations can streamline access to data and build dynamic, real-time dashboards that drive value while embedding contextual insights into everyday workflows. A session during Dreamforce 2024 with Joanna McNurlen, Principal Solution Engineer for Data Cloud at Salesforce, discussed how zero copy architecture facilitates the creation of dashboards and workflows that provide near-instant insights, enabling quick decision-making to enhance operational efficiency and competitive advantage. What is zero copy architecture?Traditionally, organizations had to replicate data from one system to another, such as copying CRM data into a data warehouse for analysis. This approach introduces latency, increases storage costs, and often results in inconsistencies between systems. Zero copy architecture eliminates the need for replication and provides a single source of truth for your data. It allows different systems to access data in its original location without duplication across platforms. Instead of using traditional extract, transform, and load (ETL) processes, systems like Salesforce Data Cloud can connect directly with external databases, such as Google Cloud BigQuery, Snowflake, Databricks, or Amazon Redshift, for real-time data access. Zero copy can also facilitate data sharing from within Salesforce to other systems. As Salesforce expands its zero copy partner network, opportunities to easily connect data from various sources will continue to grow. How does zero copy work?Zero copy employs virtual tables that act as blueprints for the data structure, enabling queries to be executed as if the data were local. Changes made in the data warehouse are instantly visible across all connected systems, ensuring users always work with the latest information. While developing dashboards, users can connect directly to the zero copy objects within Data Cloud to create visualizations and reports on top of them. Why is zero copy beneficial?Zero copy allows organizations to analyze data as it is generated, enabling faster responses, smarter decision-making, and enhanced customer experiences. This architecture reduces reliance on data transformation workflows and synchronizations within both Tableau and CRM Analytics, where organizations have historically encountered bottlenecks due to runtimes and platform limits. Various teams can benefit from the following capabilities: Unlocking real-time insights in Salesforce using zero copy architectureZero copy architecture and real-time data are transforming how organizations operate. By eliminating data duplication and providing real-time insights, the use of zero copy in Salesforce Data Cloud empowers organizations to work more efficiently, make informed decisions, and enhance customer experiences. Now is the perfect time to explore how Salesforce Data Cloud and zero copy can elevate your operations. Tectonic, a trusted Salesforce partner, can help you unlock the potential of your data and create new opportunities with the Salesforce platform. Connect with us today to get started. 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

September 28, 2024in Data, Google, Salesforce, Salesforce Data Cloud, Snowflake

23Sep

Agentforce – AI’s New Role in Sales and Service

From Science Fiction to Reality: AI’s Game-Changing Role in Service and Sales AI for service and sales has reached a critical tipping point, driving rapid innovation. At Dreamforce in San Francisco, hosted by Salesforce we explored how Salesforce clients are leveraging CRM, Data Cloud, and AI to extract real business value from their Salesforce investments. In previous years, AI features branded under “Einstein” had been met with skepticism. These features, such as lead scoring, next-best-action suggestions for service agents, and cross-sell/upsell recommendations, often required substantial quality data in the CRM and knowledge base to be effective. However, customer data was frequently unreliable, with duplicate records and missing information, and the Salesforce knowledge base was underused. Building self-service capabilities with chatbots was also challenging, requiring accurate predictions of customer queries and well-structured decision trees. This year’s Dreamforce revealed a transformative shift. The advancements in AI, especially for customer service and sales, have become exceptionally powerful. Companies now need to take notice of Salesforce’s capabilities, which have expanded significantly. Agentforce – AI’s New Role in Sales and Service Some standout Salesforce features include: At Dreamforce, we participated in a workshop where they built an AI agent capable of responding to customer cases using product sheets and company knowledge within 90 minutes. This experience demonstrated how accessible AI solutions have become, no longer requiring developers or LLM experts to set up. The key challenge lies in mapping external data sources to a unified data model in Data Cloud, but once achieved, the potential for customer service and sales is immense. How AI and Data Integrate to Transform Service and Sales Businesses can harness the following integrated components to build a comprehensive solution: Real-World Success and AI Implementation OpenTable shared a successful example of building an AI agent for its app in just two months, using a small team of four. This was a marked improvement from the company’s previous chatbot projects, highlighting the efficiency of the latest AI tools. Most CEOs of large enterprises are exploring AI strategies, whether by developing their own LLMs or using pre-existing models. However, many of these efforts are siloed, and engineering costs are high, leading to clunky transitions between AI and human agents. Tectonic is well-positioned to help our clients quickly deploy AI-powered solutions that integrate seamlessly with their existing CRM and ERP systems. By leveraging AI agents to streamline customer interactions, enhance sales opportunities, and provide smooth handoffs to human agents, businesses can significantly improve customer experiences and drive growth. Tectonic is ready to help businesses achieve similar success with AI-driven 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 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

September 23, 2024in Agentforce Platform, AI Tools, Data, Enterprise, Generative AI, Google, Marketing Automation, Salesforce, Salesforce Financial Services Solutions, Salesforce for Nonprofits, Salesforce Health and Life Sciences Solutions, Salesforce Hospitality Travel and Transportation, Salesforce Implementation Services, Salesforce Manufacturing Automotive Energy, Salesforce Optimization, Salesforce Public Sector Solutions, Snowflake

23Sep

Case Study: Salesforce Advanced Forcasting and Streamline Operations Yields Big Change and Bigger Results

Case Study: Salesforce Advanced Forcsting and Streamline Operations Yields Big Change and Bigger Results

September 23, 2024in Data, Enterprise, Google, Mulesoft, Public Sector Solutions, Salesforce, Salesforce Einstein, Salesforce Experience Cloud, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Salesforce Implementation Services, Salesforce Manufacturing Automotive Energy, Salesforce Marketing Cloud, Salesforce Success Stories, Technology

22Sep

Is Agentforce Different?

The Salesforce hype machine is in full swing, with product announcements like Chatter, Einstein GPT, and Data Cloud, all positioned as revolutionary tools that promise to transform how we work. Is Agentforce Different? However, it’s often difficult to separate fact from fiction in the world of Salesforce. The cloud giant thrives on staying ahead of technological advancements, which means reinventing itself every year with new releases and updates. You could even say three times per year with the major releases. Why Enterprises Need Multiple Salesforce Orgs Over the past decade, Salesforce product launches have been hit or miss—primarily miss. Offerings like IoT Cloud, Work.com, and NFT Cloud have faded into obscurity. This contrasts sharply with Salesforce’s earlier successes, such as Service Cloud, the AppExchange, Force.com, Salesforce Lightning, and Chatter, which defined its first decade in business. One notable exception is Data Cloud. This product has seen significant success and now serves as the cornerstone of Salesforce’s future AI and data strategy. With Salesforce’s growth slowing quarter over quarter, the company must find new avenues to generate substantial revenue. Artificial Intelligence seems to be their best shot at reclaiming a leadership position in the next technological wave. Is Agentforce Different? While Salesforce has been an AI leader for over a decade, the hype surrounding last year’s Dreamforce announcements didn’t deliver the growth the company was hoping for. The Einstein Copilot Studio—comprising Copilot, Prompt Builder, and Model Builder—hasn’t fully lived up to expectations. This can be attributed to a lack of AI readiness among enterprises, the relatively basic capabilities of large language models (LLMs), and the absence of fully developed use cases. In Salesforce’s keynote, it was revealed that over 82 billion flows are launched weekly, compared to just 122,000 prompts executed. While Flow has been around for years, this stat highlights that the use of AI-powered prompts is still far from mainstream—less than one prompt per Salesforce customer per week, on average. When ChatGPT launched at the end of 2022, many predicted the dawn of a new AI era, expecting a swift and dramatic transformation of the workplace. Two years later, it’s clear that AI’s impact has yet to fully materialize, especially when it comes to influencing global productivity and GDP. However, Salesforce’s latest release feels different. While AI Agents may seem new to many, this concept has been discussed in AI circles for decades. Marc Benioff’s recent statements during Dreamforce reflect a shift in strategy, including a direct critique of Microsoft’s Copilot product, signaling the intensifying AI competition. This year’s marketing strategy around Agentforce feels like it could be the transformative shift we’ve been waiting for. While tools like Salesforce Copilot will continue to evolve, agents capable of handling service cases, answering customer questions, and booking sales meetings instantly promise immediate ROI for organizations. Is the Future of Salesforce in the Hands of Agents? Despite the excitement, many questions remain. Are Salesforce customers ready for agents? Can organizations implement this technology effectively? Is Agentforce a real breakthrough or just another overhyped concept? Agentforce may not be vaporware. Reports suggest that its development was influenced by Salesforce’s acquisition of Airkit.AI, a platform that claims to resolve 90% of customer queries. Salesforce has even set up dedicated launchpads at Dreamforce to help customers start building their own agents. Yet concerns remain, especially regarding Salesforce’s complexity, technical debt, and platform sprawl. These issues, highlighted in this year’s Salesforce developer report, cannot be overlooked. Still, it’s hard to ignore Salesforce’s strategic genius. The platform has matured to the point where it offers nearly every functionality an organization could need, though at times the components feel a bit disconnected. For instance: Salesforce is even hinting at usage-based pricing, with a potential $2 charge per conversation—an innovation that could reshape their pricing model. Will Agents Be Salesforce’s Key to Future Growth? With so many unknowns, only time will tell if agents will be the breakthrough Salesforce needs to regain the momentum of its first two decades. Regardless, agents appear to be central to the future of AI. Leading organizations like Copado are also launching their own agents, signaling that this trend will define the next phase of AI innovation. In today’s macroeconomic environment, where companies are overstretched and workforce demands are high, AI’s ability to streamline operations and improve customer service has never been more critical. Whoever cracks customer service AI first could lead the charge in the inevitable AI spending boom. We’re all waiting to see if Salesforce has truly cracked the AI code. But one thing is certain: the race to dominate AI in customer service has begun. And Salsesforce may be at the forefront. 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

September 22, 2024in Agentforce Platform, Artificial Intelligence, Data, Enterprise, Google, Salesforce, Snowflake, Technology

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