Google Archives - gettectonic.com - Page 5
Data Cloud Model
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

Read More
October 8, 2024in Data, Google, Salesforce, Salesforce Data Cloud, Snowflake, Technology, Unified Knowledge
AI All Grown Up
07Oct

AI All Grown Up

If you thought Salesforce had fully embraced AI, think again. The company has much more in store. AI All Grown Up and Salesforce is the educator! Alongside the announcement of the new Agentforce platform, Salesforce has teased plans to offer free premium instructor-led courses and AI certifications throughout 2025, reflecting a bold commitment to fostering AI skills and expertise. We’ve talked quite a bit over the last year about the need for AI education, and lo and behold here comes Salesforce to the rescue! AI All Grown Up Ah, they grow up so fast. Once just a baby cradeled in our arms with endless possibilities and potential. It was just like a year or so ago we heard of ChatGPT. Prior to that most people’s main exposure to artificial intelligence was their smart phones, which today we realize weren’t reall that smart. Generative, predictive and agentic AI have barreled down the pipeline increasing our vocabulary, and understanding, of what artificial intelligence can do. From generative content to sounds and images, AI continued to amaze us. Then predictive AI did our calculations faster than we could have imagined. Then agentic AI did nearly everything imaginable. AI All Grown Up. Like a very proud mentor of the process, I want to talk about Salesforce’s major contribution. Addressing the AI Skills Gap: Salesforce’s $50 Million Investment As the veritable plethora of AI tools rapidly expands, Salesforce is taking proactive steps to address the growing AI skills gap by investing $50 million into workforce upskilling initiatives. The company aims to ensure that businesses and individuals are prepared to utilize their new wave of AI tools effectively. While the full details have yet to be released, Salesforce has revealed that its premium AI courses and certifications will be made available for free via Trailhead by the end of 2025. This could mean certifications such as AI Associate and AI Specialist, which currently cost $75 and $200 respectively, may soon be offered at no cost. Gratis. Free, Salesforce has also mentioned “premium instructor-led training,” sparking speculation that AI-focused, instructor-led Trailhead Academy courses could become accessible to everyone in the Salesforce ecosystem. Expanding AI Education with Global AI Centers Salesforce’s AI upskilling push is part of a broader initiative to establish “AI Centers” across the globe. Following the opening of its first center in London in June, Salesforce is planning to launch additional AI hubs in cities like Chicago, Tokyo, Sydney, and even a pop-up center in San Francisco. These centers will host in-person premium courses and serve as gathering spaces for industry experts, partners, and customers. This initiative benefits not only the Salesforce ecosystem by increasing AI knowledge where expertise is scarce, but also aligns with Salesforce’s strategy of bringing AI-driven solutions to market through new products like Copilot Studio, Data Cloud, and the newly launched Agentforce platform. Agentforce: Salesforce’s Third Wave of AI On August 28, 2024, Salesforce introduced Agentforce, a suite of autonomous AI agents that marks a significant leap in how businesses engage with customers. Described as the “Third Wave of AI,” Agentforce goes beyond traditional chatbots, providing intelligent agents capable of driving customer success with minimal human intervention. What is Agentforce? Agentforce is a comprehensive platform designed for organizations to build, customize, and deploy autonomous AI agents across various business functions, such as customer service, sales, marketing, and commerce. These agents operate independently, accessing data, crafting action plans, and executing tasks without needing constant human oversight. It is like Artificial Intelligence just graduated highschool and is off to a world of new adventures and growth opportunities at college or university! Key Features of Agentforce: The Technology Behind Agentforce At the core of Agentforce is the Atlas Reasoning Engine, a system designed to mimic human reasoning. Here’s how it works: Customization Tools: Agent Builder Agentforce provides tools like Agent Builder, a low-code platform for customizing out-of-the-box agents or creating new ones for specific business needs. With this tool, users can: The Agentforce Partner Network Salesforce’s partner ecosystem plays a key role in Agentforce’s versatility, with contributions from companies like AWS, Google, IBM, and Workday. Together, they’ve developed over 20 agent actions available through the Salesforce AppExchange. As proud parents we watch our Artificial Intelligence child venture into the world making friends along the way. Learning social skills. Benefits and Impact of Agentforce Early Adopters and Success Stories Several companies are already benefiting from Agentforce: Availability and Pricing of Salesforce’s AI All Grown Up Agentforce for Service and Sales will be generally available on October 25, 2024, with some components of the Atlas Reasoning Engine launching in February 2025. Pricing starts at $2 per conversation, with volume discounts available. The Future of AI and Work Salesforce’s ambitious vision is to empower one billion AI agents with Agentforce by the end of 2025. This reflects their belief that the future of work will involve a hybrid workforce, where humans and AI agents collaborate to drive customer success. AI All Grown Up and We Couldn’t Be Prouder Our amazing AI child has graduated college and ventured out into the workforce. Agentforce vs. Einstein Bots: What’s the Difference? Conclusion Agentforce represents a major leap forward in AI-powered customer engagement. By providing autonomous, intelligent agents capable of managing complex tasks, Salesforce is positioning itself at the forefront of AI innovation. As businesses continue to explore ways to improve efficiency and customer satisfaction, Agentforce could redefine how organizations interact with customers and streamline their operations. If this is the Third Wave of AI, what will the fourth wave bring? Written by Tectonic’s Solutions Architect, Shannan Hearne 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

Read More
October 7, 2024in Agentforce Platform, AI Tools, Data, Google, Mulesoft, Salesforce, Technology
New Salesforce Maps Experience Auto-Enabled in Winter ‘25 (October) Release
07Oct

New Salesforce Maps Experience Auto-Enabled in Winter ‘25 (October) Release

To enhance your experience in Salesforce Maps on desktop, the new features currently available in all environments will be auto-enabled in the Winter ’25 release this October. The production rollout for Salesforce Maps will begin the night of October 8th and will be completed with all organizations updated by October 18th. The Enhanced User Experience setting in the admin configuration settings will remain and can be manually disabled until the Spring ‘25 release. Take Action Now To ensure a smooth transition, please take the following actions prior to the production release.In Production: From Setup, in the Quick Find box, enter Remote Site Settings, and then select Remote Site Settings. Find and activate the following remote sites: https://lookup.search.hereapi.com, https://autosuggest.search.hereapi.com, and https://revgeocode.search.hereapi.comFailure to do so may result in disruptions to the Points of Interest Search and Click2Create featuresPrior to the deployment to production, we encourage you to explore the enhanced experience in your sandbox environments. All sandbox environments have been updated with the enhanced experience enabled by default. What to Expect Experience a drastic improvement in performance and rendering, plotting layers and mapping content up to 6x as fast!View Maps with updated styling and designs across many parts of the application, such as modernized marker pop-ups, updated drawing tools, and new cluster styling. In addition, map content along with base maps are displayed with increased detail and clarity.Combine the power of ESRI Living Atlas with CRM data directly inside Salesforce Maps. ESRI provides an evolving collection of ready-to-use global geographic content, such as imagery, base maps, demographics, landscape, and boundary data. Identify new leads and opportunities, analyze key geographical-based data, and gain valuable industry insight with lightning speed. Instructions on visualizing Living Atlas data in Maps can be found here.View plotted records in our redesigned List View, providing new capabilities and features for your users, such as the ability to dynamically build a sublist of data.For a full breakdown, please refer to the Maps Summer ‘24 Release Notes and Maps Winter ‘25 Release Notes.How can I get more information or help? Contact your account team or open a case with Salesforce Customer Support. 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

Read More
October 7, 2024in Data, Google, Salesforce
Generative AI and Patient Engagement
07Oct

Generative AI and Patient Engagement

The healthcare industry is undergoing a significant digital transformation, with generative AI and chatbots playing a prominent role in various patient engagement applications. Technologies such as online symptom checkers, appointment scheduling, patient navigation tools, medical search engines, and patient portal messaging are prime examples of how AI is enhancing patient-facing interactions. These advancements aim to alleviate staff workload while improving the overall patient experience, according to industry experts. However, even these patient-centric applications face challenges, such as the risk of generating medical misinformation or biased outcomes. As healthcare professionals explore the potential of generative AI and chatbots, they must also implement safeguards to prevent the spread of false information and mitigate disparities in care. Online Symptom Checkers Online symptom checkers allow patients to input their symptoms and receive a list of potential diagnoses, helping them decide the appropriate level of care, whether it’s urgent care or self-care at home. These tools hold promise for improving patient experiences and operational efficiency, reducing unnecessary healthcare visits. For healthcare providers, they help triage patients, ensuring those who need critical care receive it. However, the effectiveness of online symptom checkers is mixed. A 2022 literature review revealed that diagnostic accuracy ranged between 19% and 37.9%, while triage accuracy was higher, between 48.9% and 90%. Patient reception to these tools has been lukewarm as well, with some expressing dissatisfaction with the COVID-19 symptom checkers during the pandemic, mainly when the tools did not emulate human interaction. Moreover, studies have indicated that these tools might exacerbate health inequities, as users tend to be younger, female, and more digitally literate. To mitigate this, developers must ensure that chatbots can communicate in multiple languages, replicate human interactions, and escalate to human providers when needed. Self-Scheduling and Patient Navigation Generative AI and conversational AI have shown promise in addressing lower-level patient inquiries, such as appointment scheduling and navigation, reducing the strain on healthcare staff. AI-driven scheduling systems help fill gaps in navigation by assisting patients with appointment bookings and answering logistical questions, like parking or directions. A December 2023 review noted that AI-optimized patient scheduling reduces provider time burdens and improves patient satisfaction. However, barriers such as health equity, access to broadband, and patient trust must be addressed to ensure effective implementation. While organizations need to ensure these systems are accessible to all, AI is a valuable tool for managing routine patient requests, freeing staff to focus on more complex issues. Online Medical Research AI tools like ChatGPT are expanding on the “Dr. Google” phenomenon, offering patients a way to search for medical information. Despite initial concerns from clinicians about online medical searches, recent studies show that generative AI tools can provide accurate and understandable information. For instance, ChatGPT accurately answered breast cancer screening questions 88% of the time in one 2023 study and offered adequate colonoscopy preparation information in another. However, patients remain cautious about AI-generated medical advice. A 2023 survey revealed that nearly half of respondents were concerned about potential misinformation, and many were unsure about the sources AI tools use. Addressing these concerns by validating source material and providing supplementary educational resources will be crucial for building patient trust. Patient Portal Messaging and Provider Communication Generative AI is also finding its place in patient portal messaging, where it can generate responses to patient inquiries, helping to alleviate clinician burnout. In a 2024 study, AI-generated responses within a patient portal were often indistinguishable from those written by clinicians, requiring human editing in only 58% of cases. While chatbot-generated messages have been found to be more empathetic than those written by overworked providers, it’s important to ensure AI-generated responses are always reviewed by healthcare professionals to catch any potential errors. In addition to patient engagement, generative AI is being used in clinical decision support and ambient documentation, showcasing its potential to improve healthcare efficiency. However, developers and healthcare organizations must remain vigilant about preventing algorithmic bias and other AI-related risks. 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

Read More
October 7, 2024in AI Tools, Generative AI, Google, Google Analytics, Salesforce Development Services, Salesforce Health and Life Sciences Solutions, Salesforce Implementation Services, Salesforce Optimization, Technology
Natural Language Processing Explained
06Oct

Natural Language Processing Explained

What is Natural Language Processing (NLP)? Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that enables computers to interpret, analyze, and generate human language. By leveraging machine learning, computational linguistics, and deep learning, NLP helps machines understand written and spoken words, making communication between humans and computers more seamless. I apologize folks. I am feeling like the unicorn who missed the Ark. Tectonic has been providing you with tons of great material on artificial intelligence, but we left out a basic building block. Without further ado, Natural Language Processing Explained. Like a lot of components of AI, we often are using it without knowing we are using it. NLP is widely used in everyday applications such as: How Does NLP Work? Natural Language Processing combines several techniques, including computational linguistics, machine learning, and deep learning. It works by breaking down language into smaller components, analyzing these components, and then drawing conclusions based on patterns. If you have ever read a first grader’s reading primer it is the same thing. Learn a little three letter word. Recognize the meaning of the word. Understand it in the greater context of the sentence. Key NLP preprocessing steps include: Why Is NLP Important? NLP plays a vital role in automating and improving human-computer interactions by enabling systems to interpret, process, and respond to vast amounts of textual and spoken data. By automating tasks like sentiment analysis, content classification, and question answering, NLP boosts efficiency and accuracy across industries. For example: Key Use Cases of NLP in Business NLP Tasks NLP enables machines to handle various language tasks, including: Approaches to NLP Future of NLP NLP is becoming more integral in daily life as technology improves. From customer service chatbots to medical record summarization, NLP continues to evolve, but challenges remain, including improving coherence and reducing biases in machine-generated text. Essentially, NLP transforms the way machines and humans interact, making technology more intuitive and accessible across a range of industries. By Tectonic Solutions Architect – Shannan Hearne Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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
October 6, 2024in Artificial Intelligence, Data, Google, Technology
Promising Patient Engagement Use Cases for GenAI and Chatbots
06Oct

Promising Patient Engagement Use Cases for GenAI and Chatbots

Promising Patient Engagement Use Cases for GenAI and Chatbots Generative AI (GenAI) is showing great potential in enhancing patient engagement by easing the burden on healthcare staff and clinicians while streamlining the overall patient experience. As healthcare undergoes its digital transformation, various patient engagement applications for GenAI and chatbots are emerging as promising tools. Let’s look at Promising Patient Engagement Use Cases for GenAI and Chatbots. Key applications of GenAI and patient-facing chatbots include online symptom checkers, appointment scheduling, patient navigation, medical search engines, and even patient portal messaging. These technologies aim to alleviate staff workloads while improving the patient journey, according to some experts. However, patient-facing AI applications are not without challenges, such as the risk of generating medical misinformation or exacerbating healthcare disparities through biased algorithms. As healthcare professionals explore the potential of GenAI and chatbots for patient engagement, they must also ensure safeguards are in place to prevent the spread of inaccuracies and avoid creating health inequities. Online Symptom Checkers Online symptom checkers allow healthcare organizations to assess patients’ medical concerns without requiring an in-person visit. Patients can input their symptoms, and the AI-powered chatbot will generate a list of possible diagnoses, helping them decide whether to seek urgent care, visit the emergency department, or manage symptoms at home. These tools promise to improve both patient experience and operational efficiency by directing patients to the right care setting, thus reducing unnecessary visits. For healthcare providers, symptom checkers can help triage patients and ensure high-acuity areas are available for those needing critical care. Despite their potential, studies show mixed results regarding the diagnostic accuracy of online symptom checkers. A 2022 literature review found that diagnostic accuracy for these tools ranged from 19% to 37.9%. However, triage accuracy—referring patients to the correct care setting—was better, ranging between 48.9% and 90%. Patient reception to symptom checkers has also been varied. For example, during the COVID-19 pandemic, symptom checkers were designed to help patients assess whether their symptoms were virus-related. While patients appreciated the tools, they preferred chatbots that displayed human-like qualities and competence. Tools perceived as similar in quality to human interactions were favored. Furthermore, some studies indicate that online symptom checkers could deepen health inequities, as users tend to be younger, female, and more digitally literate. To mitigate this, AI developers must create chatbots that can communicate in multiple languages, mimic human interaction, and easily escalate issues to human professionals when needed. Self-Scheduling and Patient Navigation GenAI and conversational AI are proving valuable in addressing routine patient queries, like appointment scheduling and patient navigation, tasks that typically fall on healthcare staff. With a strained medical workforce, using AI for lower-level inquiries allows clinicians to focus on more complex tasks. AI-enhanced appointment scheduling systems, for example, not only help patients book visits but also answer logistical questions like parking directions or department locations within a clinic. A December 2023 literature review highlighted that AI-optimized scheduling could reduce provider workload, increase patient satisfaction, and make healthcare more patient-centered. However, key considerations for AI integration include ensuring health equity, broadband access, and patient trust. While AI can manage routine requests, healthcare organizations need to ensure their tools are accessible and functional for diverse populations. Online Medical Research GenAI tools like ChatGPT are contributing to the “Dr. Google” phenomenon, where patients search online for medical information before seeing a healthcare provider. While some clinicians have been cautious about these tools, research suggests they can effectively provide accurate medical information. For instance, an April 2023 study showed that ChatGPT answered 88% of breast cancer screening questions correctly. Another study in May 2023 demonstrated that the tool could adequately educate patients on colonoscopy preparation. In both cases, the information was presented in an easy-to-understand format, essential for improving health literacy. However, GenAI is not without flaws. Patients express concern about the reliability of AI-generated information, with a 2023 Wolters Kluwer survey showing that 49% of respondents worry about false information from GenAI. Additionally, many are uneasy about the unknown sources and validation processes behind the information. To build patient trust, AI developers must ensure the accuracy of their source material and provide supplementary authoritative resources like patient education materials. Patient Portal Messaging and Provider Communication Generative AI has also found use in patient portal messaging, where it can draft responses on behalf of healthcare providers. This feature has the potential to reduce clinician burnout by handling routine inquiries. A study conducted at Mass General Brigham in April 2024 revealed that a large language model embedded in a secure messaging tool could generate acceptable responses to patient questions. In 58% of cases, chatbot-generated messages required human editing. Promising Patient Engagement Use Cases for GenAI and Chatbots Interestingly, other research has found that AI-generated responses in patient portals are often more empathetic than those written by overworked healthcare providers. Nevertheless, AI responses should always be reviewed by a clinician to ensure accuracy before being sent to patients. Generative AI is also making strides in clinical decision support and ambient documentation, further boosting healthcare efficiency. However, as healthcare organizations adopt these technologies, they must address concerns around algorithmic bias and ensure patient safety remains a top priority. 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 don’t need to worry about tracking licenses. Read more

Read More
October 6, 2024in Agentforce Platform, AI Tools, Generative AI, Google, Laws, Life Sciences Cloud, Salesforce Development Services, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud
AI Era Demands an Intelligent Data Infrastructure
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

Read More
October 5, 2024in AI Tools, Data, Enterprise, Generative AI, Google
Databricks Tools
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

Read More
October 4, 2024in AI Tools, Data, Enterprise, Generative AI, Google, Snowflake
AI-Ready Text Data
04Oct

AI-Ready Text Data

Large language models (LLMs) are powerful tools for processing text data from various sources. Common tasks include editing, summarizing, translating, and extracting text. However, one of the key challenges in utilizing LLMs effectively is ensuring that your data is AI-ready. This insight will explain what it means to have AI-Ready Text Data and present a few no-code solutions to help you achieve this. What Does AI-Ready Mean? We are surrounded by vast amounts of unstructured text data—web pages, PDFs, emails, organizational documents, and more. These unstructured documents hold valuable information, but they can be difficult to process using LLMs without proper preparation. Many users simply copy and paste text into a prompt, but this method is not always effective. Consider the following challenges: To be AI-ready, your data should be formatted in a way that LLMs can easily interpret, such as plain text or Markdown. This ensures efficient and accurate text processing. Plain Text vs. Markdown Plain text (.txt) is the most basic file type, containing only raw characters without any stylization. Markdown files (.md) are a type of plain text but include special characters to format the text, such as using asterisks for italics or bolding. LLMs are adept at processing Markdown because it provides both content and structure, enhancing the model’s ability to understand and organize information. Markdown’s simple syntax for headers, lists, and links allows LLMs to extract additional meaning from the document’s structure, leading to more accurate interpretations. Markdown is widely supported across various platforms (e.g., Slack, Discord, GitHub, Google Docs), making it a versatile option for preparing AI-ready text. Tools for AI-Ready Data Here are some essential tools to help you manage Markdown and integrate it into your LLM workflows: Recommended Tools for Managing AI-Ready Data Obsidian: Save and Store Plain Text Obsidian is a great tool for saving and organizing Markdown files. It’s a free text editor that supports plain-text workflows, making it an excellent choice for storing content extracted from PDFs or web pages. Jina AI Reader: Convert Web Pages to Markdown Jina AI Reader is an easy-to-use tool for converting web pages into Markdown. Simply add https://r.jina.ai/ before a webpage URL, and it will return the content in Markdown format. This method streamlines the process of extracting relevant text without the clutter of formatting. LlamaParse: Extract Plain Text from Documents Highly formatted documents like PDFs can present unique challenges when working with LLMs. LlamaParse, part of LlamaIndex’s suite, helps strip away formatting to focus on the content. By using LlamaParse, you can extract plain text or Markdown from documents and ensure only the relevant sections are processed. Our Thoughts Preparing text data for AI involves strategies to convert, store, and process content efficiently. While this may seem daunting at first, using the right tools will streamline your workflow and allow you to maximize the power of LLMs for your specific tasks. Tectonic is ready to assist. Contact us 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 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 don’t need to worry about tracking licenses. Read more

Read More
October 4, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Google
Agentforce Unveiled
03Oct

Agentforce Unveiled

Salesforce Unveils ‘Agentforce’ to Empower a Billion AI Agents by 2025 Salesforce has introduced Agentforce, a revolutionary suite of autonomous AI agents aimed at transforming service, sales, marketing, and commerce sectors. With Agentforce, companies can rapidly scale their operations, boost efficiency, and elevate customer satisfaction by leveraging intelligent agents that handle routine tasks and complex workflows. The Agentforce Atlas Reasoning Engine powers these agents, autonomously analyzing data, making decisions, and completing tasks. This engine enables organizations to easily build, customize, and deploy their own agents using intuitive low-code tools. In addition, the Agentforce Partner Network allows customers to integrate pre-built agents from industry leaders like AWS, Google, IBM, and Workday, offering even more flexibility. Real-world impact Companies like OpenTable, Saks, and Wiley are already deploying Agentforce within their existing systems to enhance workforce capabilities and scale operations. Agentforce works autonomously, retrieving the right data on demand, building action plans, and executing them without human intervention. However, when needed, it can seamlessly hand off tasks to human employees, providing an overview of interactions, customer details, and suggested next steps. For example, Wiley has reported a 40% increase in case resolution after implementing Agentforce to handle routine inquiries. During busy seasons, like back-to-school, Agentforce has helped Wiley streamline operations, freeing up employees to handle more complex cases. Saks is also leveraging Agentforce to elevate its personalized customer experiences, empowering employees with real-time insights to deliver exceptional service. A new era of AI-driven customer success Salesforce CEO Marc Benioff is confident that Agentforce represents the third wave of AI, surpassing traditional chatbots and copilots with its fully autonomous capabilities. “Agentforce is a revolutionary and trusted solution that seamlessly integrates AI across every workflow, embedding itself deeply into the heart of the customer journey. This means anticipating needs, strengthening relationships, driving growth, and taking proactive action at every touchpoint,” Benioff said. Unlike its predecessors, Agentforce operates independently, adapting to changing conditions using real-time data. Whether responding to a customer service inquiry, qualifying sales leads, or optimizing marketing campaigns, Agentforce makes timely, relevant decisions based on an organization’s custom settings. When more human oversight is required, the platform provides detailed summaries and recommendations to assist employees in making informed decisions. Agentforce’s scalability and future Salesforce’s ambitious goal is to empower one billion AI agents by the end of 2025. This bold vision stems from the understanding that 41% of employee time is often spent on repetitive, low-impact work, according to the Salesforce Trends in AI Report. By automating these tasks, Agentforce allows employees to focus on more strategic, high-value initiatives, creating a hybrid workforce that is more effective and adaptable. Benioff noted, “While others require you to DIY your AI, Agentforce offers a fully tailored, enterprise-ready platform designed for immediate impact and scalability. Our vision is bold, and this is what AI is meant to be.” As businesses worldwide continue to explore AI’s potential, Agentforce positions Salesforce as a leader in the next wave of AI innovation, where autonomous agents enhance every facet of business operations. With over 1,000 agents already active, the future of work is a dynamic blend of human expertise and AI-powered agents, enabling organizations to thrive in an increasingly competitive landscape. 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

Read More
October 3, 2024in Einstein 1 Platform, Salesforce Einstein, Salesforce Optimization
Salesforce and the Customer-Centric Dealership
03Oct

Salesforce and the Customer-Centric Dealership

Building Blocks for a Customer-Centric Dealership For a dealership to thrive, it must be truly customer-centric. As a Salesforce Implementation Partner, we at Tectonic know the key to success lies in prioritizing customer needs at every step. Growth, profitability, and market expansion come from consistently putting customers at the center of your strategies. Tectonic can help you implement Salesforce and the Customer-Centric Dealership. Customers as the Ultimate Scorekeepers Your customers are the true measure of your dealership’s success. Their evaluations extend far beyond pricing—they assess every touchpoint, from your website’s ease of use to the cleanliness of your parking lot, from your voicemail system to post-sale follow-ups. Each interaction shapes their perception. A single negative encounter, such as a poor experience with a parts associate, may not drive them away, but it certainly won’t motivate them to return. Creating positive, seamless interactions is crucial. Optimizing Customer-Centric Processes Are your processes truly serving your customers? Are they complimenting your dealership’s efficiency? Take something as routine as monthly statements. While they may be straightforward for your team, have you considered your customers’ perspectives? They may not even want a statement, or they might need quicker, more accessible responses to their inquiries. As customer expectations rise due to experiences with brands like Amazon and Starbucks, your dealership must ensure its processes meet or exceed those standards. A Customer portal like Salesforce Experience Cloud can put resources at your customers’ fingertips with the click of a button. Treating Customer Data as a Strategic Asset Customer data is one of your dealership’s most valuable assets. Yet, many dealerships struggle to fully utilize this resource. Are you effectively visualizing and leveraging your customer data? This data, gathered over years of operation, can drive strategic decision-making. To maximize its potential, it’s crucial to have a dedicated role—perhaps a Chief Data Officer—focused on managing, updating, and safeguarding this information. A well-managed data strategy unlocks insights that fuel customer-centric improvements. Letting Data Shape Your Processes With Salesforce, you can use customer data to quickly identify inefficiencies and enhance processes. Streamlined operations lead to happier customers and a more engaged team. For instance, if your manufacturer can deliver most parts within 24 hours, why maintain an overstocked inventory? By analyzing your inventory data, Salesforce can help you identify which parts are essential and which are surplus, allowing you to optimize stock levels and ensure smoother operations. Aligning your inventory with actual demand ensures your dealership functions efficiently, keeping both your customers and staff satisfied. Expert Guidance in Leveraging Salesforce for Growth Adopting a customer-centric approach and utilizing Salesforce to analyze and act on your data can transform your dealership. As your Salesforce Implementation Partner, we’re here to help you integrate these solutions to meet and exceed customer expectations while driving growth and profitability. Together, we can build a dealership that not only competes but thrives in today’s competitive market. With the future of AI, the connected car, and more; there has never been a betrter time to add Salesforce to your customer-centric tool box. Reach out to schedule an introductory call and start your journey toward a more customer-centric future. 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

Read More
October 3, 2024in Data, Google, Salesforce, Salesforce Experience Cloud
Scale and AI Influence Shape Partner Ecosystems
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

Read More
October 3, 2024in Data, Enterprise, Generative AI, Google, Hyperforce, Salesforce, Technology
Paradigm Shifts That Changed Marketing
03Oct

Marc Benioff Interviewed

Salesforce, now one of the most influential companies in tech history, was founded by Marc Benioff after his successful tenure at Oracle. Having witnessed the potential of the internet, Benioff realized it would usher in a new era for delivering enterprise software through the cloud. However, this new approach demanded a complete overhaul of traditional business models, marketing strategies, and software development practices. Salesforce would embody this transformation, evolving over the decades into a platform supporting a diverse array of enterprise applications. Today, Salesforce’s flagship initiative is Agentforce, a pioneering wave of AI agents built on the Salesforce platform. This initiative, which I covered in detail earlier this month, exemplifies Benioff’s forward-thinking vision for AI. He passionately argues that Salesforce is well-positioned to lead in this space, and his insights on AI and its potential are as compelling as they are thought-provoking. Before diving into Agentforce and Benioff’s views on AI, it’s important to explore his early life and the founding of Salesforce. Benioff’s path to becoming the CEO of one of the most powerful software companies in the world was shaped by an early interest in technology. His first foray into software development was creating an app called “How to Juggle,” which he sold for $75 to CLOAD Magazine in Goleta, California. This early success marked the beginning of a long career in tech. Benioff went on to establish an Atari game company called Liberty Software and later wrote assembly code for the Mac as part of an internship. Despite his success as a software developer, he transitioned to a leadership role after realizing his interest in the broader business of technology. He credits his time at Oracle, where he learned the intricacies of sales, marketing, and customer communication, as critical to shaping his vision for Salesforce. Oracle, during Larry Ellison’s prime, was an ideal place to hone these business skills, which Benioff would later apply to revolutionize the enterprise software space. With this foundation, Benioff founded Salesforce, a company that has fundamentally changed the way businesses engage with technology. The company’s success lies not just in its cloud-based software, but in its ongoing evolution to address new challenges, including the AI-powered Agentforce initiative, which stands at the forefront of Salesforce’s future growth. Benioff’s journey, from a young programmer to the CEO of a global tech giant, is a testament to his visionary approach and adaptability in an ever-changing tech landscape. Marc Benioff’s early entrepreneurial instincts were evident even before he fully embraced the title of entrepreneur. From a young age, he knew he wanted to build something of his own, though he hadn’t yet honed the full set of skills needed to succeed in business. He had a knack for technology, excelling as a programmer working in assembly code, but his mentors, including university professors and colleagues at Apple, recognized that being an entrepreneur required more than just technical ability. They encouraged him to develop his sales, marketing, and business acumen. His early exposure to these lessons was pivotal in shaping his future trajectory. Benioff’s transition from a programmer to a business leader began when he sought advice from Bill Jost at Apple, who connected him with Gary Gibson, an Oracle executive. After a memorable and unexpected interview, Benioff joined Oracle, starting with a role answering calls at the company’s 800-number. He quickly moved into sales and marketing, eventually working directly with Larry Ellison, who would become a significant mentor over the next 13 years. Benioff’s success at Oracle set the stage for his eventual departure to start Salesforce in 1999. In the mid-1990s, while at Oracle, Benioff had an epiphany while using a web browser. The internet was transforming how software could be delivered, and Benioff saw the potential for a new business model based on cloud computing. At this time, Oracle was focused on the network computer—a vision Benioff did not fully share. After discussing his ideas with Larry Ellison and taking some time off for reflection and research, Benioff recognized that the world was on the cusp of a major shift in technology. He felt compelled to create something new that would leverage the internet, offering software over the web through a subscription model. During his time off, Benioff traveled to Hawaii, reflecting on the future of the tech industry and contemplating ideas. He also experimented with domain names, purchasing You.com and later gifting it to a friend who turned it into a search and AI company. Benioff returned to Larry Ellison to share his vision of a cloud-based business model and later found inspiration from spiritual gurus during a trip to India, who reminded him that success in business should also be grounded in positive values. Benioff’s experience led to the founding of Salesforce with its revolutionary 1-1-1 Model—dedicating 1% of equity, profit, and employee time to philanthropy. Salesforce also embraced emerging technologies such as social, mobile, and AI, and created a new business model through subscription-based services. This combination of technological innovation and a strong sense of social responsibility has set Salesforce apart, attracting both admiration and emulation from other companies. Benioff’s Career Benioff’s career is a testament to the power of surrounding oneself with great people, continuing to learn, and having a clear vision. His leadership at Salesforce has been guided by values he believes are crucial to the company’s success and its ability to make a positive impact on the world. His insights on the Innovator’s Dilemma, a concept popularized by Clayton Christensen, highlight the challenges companies face as they evolve with changing technologies. While Salesforce’s core principles remain intact, the company has continued to adapt to new technology paradigms, ensuring it remains at the forefront of innovation in the software industry. Marc Benioff (MB) recently shared his excitement about the future of AI and its potential to revolutionize business and technology. He explained that, from a business perspective, this is the most thrilling moment in his career. AI, according to him, will transform how enterprises operate, making them more

Read More
October 3, 2024in Agentforce Platform, Data, Enterprise, Google, Hyperforce, Salesforce, Salesforce Data Cloud, Snowflake, Technology
Agentforce to the Team
03Oct

Supercharged Sales and Service Excellence

When sales and service teams are seamlessly aligned, organizations can transform every service interaction into a powerful opportunity to drive revenue and build customer loyalty. By delivering contextual, personalized offers at the right time, businesses can turn each customer touchpoint into a chance to solve problems, create value, and enhance engagement. With the help of real-time data and AI, we move beyond treating customer interactions as isolated events. Instead, we focus on understanding the unique needs and preferences of each customer, whether they’re reaching out for support, visiting a store, or engaging online. Every touchpoint becomes an opportunity to deliver tailored solutions, strengthen loyalty, and increase revenue. These examples are just a glimpse of the solutions we help our clients deploy. With our deep expertise in AI implementation and industry-specific insights, Tectonic is ideally positioned to guide you through activating AI agents that build on your successes. Our extensive experience across diverse sectors enables us to identify the most impactful opportunities for AI, ensuring tailored solutions that align with your business strategy. Tectonic’s proven approach ensures AI agents are deployed efficiently and effectively, driving ROI and delivering measurable results. Agentforce: Open, Extensible, and Transformative Agentforce is designed to foster smooth interactions between agents created on the platform and external agents and applications. With integration into Salesforce Data Cloud, it enables real-time data access for better, faster decision-making. Tectonic can help facilitate this interconnectivity across various channels and platforms, ensuring seamless operations. In a society that prioritizes digital, artificial intelligence is changing sales and service tactics. Adopting AI agents is crucial for business leaders to keep ahead of the competition and develop more effective and efficient methods of customer and employee support. AI agents are not just upgrades—they are transformative forces that redefine what’s possible. They empower organizations to move from envisioning possibilities to realizing a future of enhanced sales performance, streamlined operations, and unmatched customer satisfaction, all powered by AI. Salesforce’s Agentforce: Scaling Your Workforce with Autonomous AI Agents Agentforce provides fully customizable autonomous AI agents that can integrate with enterprise data and take action across sales, service, marketing, and commerce. The Agent Builder allows users to create agents that automate any business process, in any industry, using existing Salesforce tools such as workflows, Apex code, and prompt templates. Leading companies like The Adecco Group, BACA Systems, OpenTable, Saks, and Wiley are already leveraging Agentforce Service Agents to deliver faster, more personalized customer responses while improving operational efficiency. Salesforce (NYSE: CRM), the world’s #1 AI CRM, has announced the general availability of Agentforce. This new platform layer enables businesses to create and deploy AI agents that autonomously take action across any business function. Unlike traditional chatbots and copilots, Agentforce agents possess advanced reasoning abilities to make decisions and take actions such as resolving customer issues, qualifying sales leads, and optimizing marketing efforts. These agents operate independently—triggered by changes in data, business rules, pre-built automations, or external signals from APIs. Agentforce includes out-of-the-box agents that can be easily customized and deployed with low-code or no-code tools, working around the clock across any channel. The first of these, the Agentforce Service Agent, outperforms traditional chatbots by handling a wide range of tasks—from simple to complex—using pre-built topics and actions for customer support. These agents are fully customizable to serve various industries, such as retail with order management or financial services with billing and payment support. “Agentforce is redefining business possibilities, ushering in an era of AI-driven abundance and limitless workforces that enhance every employee’s capabilities, strengthen customer relationships, and fuel unprecedented growth,” said Marc Benioff, Chair and CEO of Salesforce. “Built on Salesforce’s trusted, fully customizable platform—integrating enterprise data, metadata, AI models, workflows, security, and applications—Agentforce is everything AI was meant to be.” The Power of Agentforce With Agentforce, there’s no need for DIY AI. Unlike other platforms that require complex integrations and custom builds, Agentforce is natively built into the Salesforce Platform. Customers can instantly convert their existing Flows, prompt templates, Apex code, and APIs into agent actions, seamlessly connecting to enterprise data and security models. With the added power of native tools like Data Cloud, Slack, and MuleSoft, Agentforce offers a streamlined and efficient approach to scaling your workforce with AI. The new Agent Builder empowers Salesforce admins and developers to easily create AI agents with natural language instructions and guardrails, enabling even more customization and control. 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

Read More
October 3, 2024in Agentforce Platform, Artificial Intelligence, Data, Enterprise, Google, Hyperforce, Mulesoft, Salesforce, Salesforce Data Cloud
Small Language Models Explained
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

Read More
September 30, 2024in Salesforce

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