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Necessary Salesforce Admin Skills
28Oct

Necessary Salesforce Admin Skills

In 2023, Salesforce Admins created 230,000 objects, generated over 2.7 billion reports monthly, and performed 1 trillion AI predictions weekly. These figures highlight the critical role of an Admin in the Salesforce ecosystem. However, becoming an expert Salesforce Admin requires more than just technical know-how. A blend of both technical and soft skills is essential to truly excel in this role. Whether you’re new to the role or looking to enhance your skills, learning the right abilities is key to thriving as a Salesforce Admin. In this guide, we’ll cover the essential Salesforce Admin skills you need to effectively manage the platform and drive business success. Key Takeaways 14 Essential Salesforce Admin Skills to Master These are some of the key skills outlined in Salesforce’s “Admin Skills Kit” that will help you secure top Salesforce Admin positions. 1. Communication Strong communication skills are critical for Salesforce Admins, who act as the link between technical teams, stakeholders, and users. You’ll need to explain complex processes clearly so that all parties can understand. Why It Matters: Clear communication ensures users can effectively engage with the platform and make informed decisions based on Salesforce data. How To Improve: Practice simplifying technical concepts, actively listen to others’ needs, and refine your ability to convey information clearly. 2. Problem Solving Challenges will arise in your role, from system errors to user issues. Having strong problem-solving skills allows you to identify the root cause of issues and quickly implement effective solutions. Why It Matters: Efficient problem resolution minimizes downtime and keeps the Salesforce platform running smoothly. How To Improve: Break down problems into manageable parts, brainstorm solutions, and always evaluate the impact of your decisions. 3. Attention to Detail Salesforce admins deal with complex data and processes that require accuracy. From maintaining data integrity to configuring processes, attention to detail is crucial. Why It Matters: Even small errors can result in inaccurate data, security risks, or inefficient processes. How To Improve: Double-check your work, use Salesforce validation tools, and ensure all workflows are correctly configured before launching. 4. Learner’s Mindset Salesforce evolves constantly, with frequent updates and new features. A learner’s mindset helps you stay on top of these changes and continuously improve your skills. Why It Matters: The more you know, the more value you bring. Keeping up with updates ensures you’re using the latest tools to benefit your organization. How To Improve: Engage with Salesforce communities, complete Trailhead modules, and attend webinars to stay current. 5. User Management As an Admin, you’ll be responsible for managing users, creating profiles, setting roles and permissions, and ensuring proper access to data. Why It Matters: Proper user management boosts productivity while ensuring data security. How To Improve: Learn the ins and outs of Salesforce profiles, roles, and permission sets, and practice managing users in a sandbox environment. 6. Security Management In today’s digital age, data security is a top priority. Salesforce Admins are responsible for safeguarding organizational data from unauthorized access or breaches. Why It Matters: Poor security can lead to data leaks, damaging the company’s reputation and finances. How To Improve: Master security settings, understand field-level permissions, and stay informed on two-step verification and audit tracking. 7. Business Analysis Admins need to understand the business needs of their organization. Business analysis skills help you gather requirements, understand workflows, and tailor Salesforce to meet those needs. Why It Matters: The better you understand the business, the more effectively you can customize Salesforce to add value. How To Improve: Collaborate with stakeholders to identify pain points and design solutions that address specific business needs. 8. Data Analysis Working with large data sets is a regular part of being a Salesforce Admin. Knowing how to analyze data and generate insights is essential. Why It Matters: Data analysis drives informed decision-making, streamlines workflows, and improves communication. How To Improve: Familiarize yourself with Salesforce’s reporting tools, dashboards, and data export features. 9. Data Management Effective data management is critical to maintaining a well-functioning Salesforce system. This involves data transfers, cleaning, deduplication, and archiving. Why It Matters: Clean and organized data supports accurate reporting and better decision-making. How To Improve: Learn best practices for data imports, validation rules, and data maintenance tools like Data Loader. 10. Designer’s Mindset Admins with a designer’s mindset can create user-friendly interfaces and workflows that enhance the system’s usability. Why It Matters: A well-designed interface improves the user experience, making work more efficient and enjoyable. How To Improve: Use the Lightning App Builder to create custom page layouts and optimize user flows. 11. Change Management When implementing new processes or updates, effective change management is key. This includes communication, ensuring smooth transitions, and training users on new features. Why It Matters: Proper change management ensures high adoption rates and a smooth transition to new features or updates. How To Improve: Develop communication plans, conduct training, and gather user feedback during transitions. 12. Process Automation Salesforce’s automation capabilities allow admins to streamline repetitive tasks. Automation skills help you create workflows, approval processes, and automated communications. Why It Matters: Automating repetitive tasks saves time and ensures that critical business processes are followed consistently. How To Improve: Learn how to use Flows, Process Builder, and Workflow Rules to automate business operations. 13. Product Management A product management mindset helps Admins align the platform with users’ and stakeholders’ needs, ensuring Salesforce delivers value to the organization. Why It Matters: Meeting evolving business needs ensures you continue to add value as an Admin. How To Improve: Collect user feedback, prioritize requests, and align updates with overall business goals. 14. Project Management Salesforce Admins often lead projects such as implementing new features or migrating data. Strong project management skills are essential to execute these tasks effectively. Why It Matters: Good project management ensures that goals are met on time and within budget. How To Improve: Get comfortable with project management tools, scheduling, resource management, and stakeholder communication. Conclusion The role of a Salesforce Admin combines both technical and soft skills like communication,

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October 28, 2024in Data, Salesforce
Transform Customer Experience
25Oct

Transform Customer Experience

In today’s AI-driven business environment, customer experience (CX) has evolved from being a buzzword to a critical factor in determining success. It’s no longer enough for businesses to offer high-quality products or excellent service alone—today’s customers are always online, engaged, and seeking the most convenient, relevant, and enjoyable experiences. This is where Salesforce Data Cloud becomes a game-changer, providing the tools needed to meet modern customer expectations. Transforming Customer Experience with Salesforce Data Cloud Salesforce enables businesses to collect, integrate, and leverage critical customer information within its ecosystem, offering an all-encompassing view of each customer. This unified customer data allows organizations to forecast visitor trends, assess marketing impact, and predict customer behavior. As data-driven decision-making becomes increasingly central to business strategy, Salesforce Data Cloud and its Customer Data Platform (CDP) features provide a significant competitive edge—whether in e-commerce, fintech, or B2B industries. Data Cloud is more than just your traditional CDP. It’s the only data platform native to the world’s #1 AI CRM. This means that marketers can quickly access and easily action on unified data – from across the entire business – to drive growth and increase customer lifetime value. Data Cloud’s Role in Enhancing CX By unifying data in one place, Salesforce Data Cloud enables organizations to access real-time customer insights. This empowers them to track customer activity across channels like email, social media, and online sales, facilitating targeted marketing strategies. Businesses can analyze customer behavior and deliver personalized messaging, aligning marketing, sales, and customer service efforts to ensure consistency. With these capabilities, Salesforce customers can elevate the CX by delivering the right content, at the right time, to the right audience, ultimately driving customer satisfaction and growth. New Features of Salesforce Data Cloud Salesforce continues to evolve, introducing cutting-edge features that reshape customer interaction: To fully maximize these features, partnering with a Salesforce Data Cloud consultant can help businesses unlock the platform’s full potential and refine their customer engagement strategies. Agentic AI Set to Supercharge Business Processes Salesforce’s vision extends beyond customer relationship management with the integration of Agentic AI through its Customer 360 platform. According to theCUBE Research analysts, this signals a shift toward using AI agents to automate complex business processes. These AI agents, built on Salesforce’s vast data resources, promise to revolutionize how companies operate, offering customized, AI-driven business tools. “If they can pull this off, where it becomes a more dynamic app platform, more personalized, really focused on those processes all the way back to the data, it’s going to be a clear win for them,” said Strechay. “They’re sitting on cloud; they’re sitting on IaaS. That’s a huge win from that perspective.” AI agents create a network of microservices that think and act independently, involving human intervention only when necessary. This division of labor allows businesses to capture expertise in routine tasks while freeing human workers to focus on more complex decision-making. However, the success of these AI agents depends on access to accurate and reliable data. As Gilbert explained, “Agents can call on other agents, and when they’re not confident of a step in a process or an outcome, they can then bounce up to an inbox for a human to supervise.” The goal isn’t to eliminate humans but to capture their expertise for simpler processes. Empowering Developers and Citizen Creators At the core of this AI-driven transformation is Salesforce’s focus on developers. The platform’s low-code tools allow businesses to easily customize AI agents and automate business processes, empowering both experienced developers and citizen creators. With simple language commands or goal-setting, companies can build and train these AI agents, streamlining operations. “It’s always going to be about good data—that’s the constant,” Bertrand said. “The second challenge is how to train agents and humans to work together effectively. While some entry-level jobs may be replaced, AI will continue to evolve, creating new opportunities in the future.” Is Salesforce Data Cloud the Right Fit for Your Business? Salesforce Data Cloud offers comprehensive capabilities for businesses of all sizes, but it’s essential to assess whether it aligns with your specific needs. The platform is particularly valuable for: For businesses that fit these scenarios, working with Salesforce’s partner ecosystem or a Data Cloud consultant can help ensure successful integration and optimization. What’s New in Salesforce’s Latest Release? The latest Salesforce Spring Release introduced several exciting features, further enhancing Salesforce Data Cloud: These updates reflect Salesforce’s commitment to providing innovative, data-driven solutions that enhance customer experiences and drive business success. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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October 25, 2024in AI Tools, Data, Generative AI, Salesforce, Salesforce Data Cloud
Scaling Generative AI
23Oct

Scaling Generative AI

Many organizations follow a hybrid approach to AI infrastructure, combining public clouds, colocation facilities, and on-prem solutions. Specialized GPU-as-a-service vendors, for instance, are becoming popular for handling high-demand AI computations, helping businesses manage costs without compromising performance. Business process outsourcing company TaskUs, for example, focuses on optimizing compute and data flows as it scales its gen AI deployments, while Cognizant advises that companies distinguish between training and inference needs, each with different latency requirements.

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October 23, 2024in Data, Enterprise, Generative AI, Google, Salesforce
AI Agents and Digital Transformation
23Oct

Ready for AI Agents

Brands that can effectively integrate agentic AI into their operations stand to gain a significant competitive edge. But as with any innovation, success will depend on balancing the promise of automation with the complexities of trust, privacy, and user experience.

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October 23, 2024in Data, Enterprise, Generative AI, Google, Salesforce
Recent advancements in AI
18Oct

Recent advancements in AI

Recent advancements in AI have been propelled by large language models (LLMs) containing billions to trillions of parameters. Parameters—variables used to train and fine-tune machine learning models—have played a key role in the development of generative AI. As the number of parameters grows, models like ChatGPT can generate human-like content that was unimaginable just a few years ago. Parameters are sometimes referred to as “features” or “feature counts.” While it’s tempting to equate the power of AI models with their parameter count, similar to how we think of horsepower in cars, more parameters aren’t always better. An increase in parameters can lead to additional computational overhead and even problems like overfitting. There are various ways to increase the number of parameters in AI models, but not all approaches yield the same improvements. For example, Google’s Switch Transformers scaled to trillions of parameters, but some of their smaller models outperformed them in certain use cases. Thus, other metrics should be considered when evaluating AI models. The exact relationship between parameter count and intelligence is still debated. John Blankenbaker, principal data scientist at SSA & Company, notes that larger models tend to replicate their training data more accurately, but the belief that more parameters inherently lead to greater intelligence is often wishful thinking. He points out that while these models may sound knowledgeable, they don’t actually possess true understanding. One challenge is the misunderstanding of what a parameter is. It’s not a word, feature, or unit of data but rather a component within the model‘s computation. Each parameter adjusts how the model processes inputs, much like turning a knob in a complex machine. In contrast to parameters in simpler models like linear regression, which have a clear interpretation, parameters in LLMs are opaque and offer no insight on their own. Christine Livingston, managing director at Protiviti, explains that parameters act as weights that allow flexibility in the model. However, more parameters can lead to overfitting, where the model performs well on training data but struggles with new information. Adnan Masood, chief AI architect at UST, highlights that parameters influence precision, accuracy, and data management needs. However, due to the size of LLMs, it’s impractical to focus on individual parameters. Instead, developers assess models based on their intended purpose, performance metrics, and ethical considerations. Understanding the data sources and pre-processing steps becomes critical in evaluating the model’s transparency. It’s important to differentiate between parameters, tokens, and words. A parameter is not a word; rather, it’s a value learned during training. Tokens are fragments of words, and LLMs are trained on these tokens, which are transformed into embeddings used by the model. The number of parameters influences a model’s complexity and capacity to learn. More parameters often lead to better performance, but they also increase computational demands. Larger models can be harder to train and operate, leading to slower response times and higher costs. In some cases, smaller models are preferred for domain-specific tasks because they generalize better and are easier to fine-tune. Transformer-based models like GPT-4 dwarf previous generations in parameter count. However, for edge-based applications where resources are limited, smaller models are preferred as they are more adaptable and efficient. Fine-tuning large models for specific domains remains a challenge, often requiring extensive oversight to avoid problems like overfitting. There is also growing recognition that parameter count alone is not the best way to measure a model’s performance. Alternatives like Stanford’s HELM and benchmarks such as GLUE and SuperGLUE assess models across multiple factors, including fairness, efficiency, and bias. Three trends are shaping how we think about parameters. First, AI developers are improving model performance without necessarily increasing parameters. A study of 231 models between 2012 and 2023 found that the computational power required for LLMs has halved every eight months, outpacing Moore’s Law. Second, new neural network approaches like Kolmogorov-Arnold Networks (KANs) show promise, achieving comparable results to traditional models with far fewer parameters. Lastly, agentic AI frameworks like Salesforce’s Agentforce offer a new architecture where domain-specific AI agents can outperform larger general-purpose models. As AI continues to evolve, it’s clear that while parameter count is an important consideration, it’s just one of many factors in evaluating a model’s overall capabilities. To stay on the cutting edge of artificial intelligence, contact Tectonic today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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October 18, 2024in Data, Generative AI, Google, Salesforce
Amazon DynamoDB to Salesforce Data Cloud
18Oct

Amazon DynamoDB to Salesforce Data Cloud

Ingesting Data from Amazon DynamoDB to Salesforce Data Cloud Salesforce Data Cloud serves as your organization’s digital command center, enabling real-time ingestion, unification, and activation of data from any source. By transforming scattered customer information into actionable insights, it empowers businesses to operate with unparalleled efficiency. Integrating Amazon DynamoDB with Salesforce Data Cloud exemplifies the platform’s capacity to unify and activate enterprise data seamlessly. Follow this step-by-step guide to ingest data from Amazon DynamoDB into Salesforce Data Cloud. Prerequisites Part 1: Amazon DynamoDB Setup 1. AWS Account Setup 2. Create a DynamoDB Table 3. Populate the Table with Data 4. Security Credentials Part 2: Salesforce Data Cloud Configuration 1. Creating the Data Connection 2. Configuring Data Streams Create a New Data Stream Configure the Data Model 3. Data Modeling and Mapping Custom Object Creation Conclusion After completing the setup: This integration underscores Salesforce Data Cloud’s role as a centralized hub, capable of harmonizing diverse data sources, ensuring real-time synchronization, and enabling actionable insights. By connecting Amazon DynamoDB, businesses can unlock the full potential of their data, driving better decision-making and customer experiences. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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October 18, 2024in Data, Enterprise, Salesforce, Salesforce Data Cloud
Life of a Salesforce Admin in the AI Era
18Oct

Life of a Salesforce Admin in the AI Era

The life of Salesforce admins is rapidly evolving as artificial intelligence (AI) becomes integral to business operations. Let’s examine the Life of a Salesforce Admin in the AI Era. By 2025, the Salesforce admin’s role will expand beyond managing CRM systems to include leveraging AI tools to enhance efficiency, boost productivity, and maintain security. While this future offers exciting opportunities, it also comes with new responsibilities that require admins to adapt and learn. So, what will Salesforce admins need to succeed in this AI-driven landscape? The Salesforce Admin’s Role in 2025 In 2025, Salesforce admins will be at the forefront of digital transformation, helping organizations harness the full potential of the Salesforce ecosystem and AI-powered tools. These AI tools will automate processes, predict trends, and improve overall efficiency. Many professionals are already enrolling in Salesforce Administrator courses focused on AI and automation, equipping them with the essential skills to thrive in this new era. Key Responsibilities in Life of a Salesforce Admin in the AI Era 1. AI Integration and Optimization Admins will be responsible for integrating AI tools like Salesforce Einstein AI into workflows, ensuring they’re properly configured and tailored to the organization’s needs. Core tasks include: 2. Automating Processes with AI AI will revolutionize automation, making complex workflows more efficient. Admins will need to: 3. Data Management and Predictive Analytics Admins will leverage AI to manage data and generate predictive insights. Key responsibilities include: 4. Enhancing Security and Compliance AI-powered security tools will help admins proactively protect systems. Responsibilities include: 5. Supporting AI-Driven Customer Experiences Admins will deploy AI tools that enhance customer interactions. Their responsibilities include: 6. Continuous Learning and Upskilling As AI evolves, so too must Salesforce admins. Key learning areas include: 7. Collaboration with Cross-Functional Teams Admins will work closely with IT, marketing, and sales teams to deploy AI solutions organization-wide. Their collaborative efforts will include: Skills Required for Future Salesforce Admins 1. AI and Machine Learning Proficiency Admins will need to understand how AI models like Einstein AI function and how to deploy them. While not requiring full data science expertise, a solid grasp of AI concepts—such as predictive analytics and machine learning—will be essential. 2. Advanced Data Management and Analysis Managing large datasets and ensuring data accuracy will be critical as admins work with AI tools. Proficiency in data modeling, SQL, SOQL, and ETL processes will be vital for handling AI-powered data management. 3. Automation and Process Optimization AI-enhanced automation will become a key responsibility. Admins must master tools like Salesforce Flow and Einstein Automate to build intelligent workflows and ensure smooth process automation. 4. Security and Compliance Expertise With AI-driven security protocols, admins will need to stay updated on data privacy regulations and deploy tools that ensure compliance and prevent data breaches. 5. Collaboration and Leadership Admins will lead the implementation of AI tools across departments, requiring strong collaboration and leadership skills to align AI-driven solutions with business objectives. Advanced Certifications for AI-Era Admins To stay competitive, Salesforce admins will need to pursue advanced certifications. Key certifications include: Tectonic’s Thoughts The Salesforce admin role is transforming as AI becomes an essential part of the platform. By mastering AI tools, optimizing processes, ensuring security, and continuously upskilling, Salesforce admins can become pivotal players in driving digital transformation. The future is bright for those who embrace the AI-powered Salesforce landscape and position themselves at the forefront of 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

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October 18, 2024in AI Tools, Artificial Intelligence, Data, Salesforce, Salesforce Einstein
Third Wave of AI at Salesforce
16Oct

Third Wave of AI at Salesforce

The Third Wave of AI at Salesforce: How Agentforce is Transforming the Landscape At Dreamforce 2024, Salesforce unveiled several exciting innovations, with Agentforce taking center stage. This post explores the key changes and enhancements designed to improve efficiency and elevate customer interactions. Introducing Agentforce Agentforce is a customizable AI agent builder that empowers organizations to create and manage autonomous agents for various business tasks. But what exactly is an agent? An agent is akin to a chatbot but goes beyond traditional capabilities. While typical chatbots are restricted to scripted responses and predefined questions, Agentforce agents leverage large language models (LLMs) and generative AI to comprehend customer inquiries contextually. This enables them to make independent decisions, whether processing requests or resolving issues using real-time data from your company’s customer relationship management (CRM) system. The Role of Atlas At the heart of Agentforce’s functionality lies the Atlas reasoning engine, which acts as the operational brain. Unlike standard assistive tools, Atlas is an agentic system with the autonomy to act on behalf of the user. Atlas formulates a plan based on necessary actions and can adjust that plan based on evaluations or new information. When it’s time to engage, Atlas knows which business processes to activate and connects with customers or employees via their preferred channels. This sophisticated approach allows Agentforce to significantly enhance operational efficiency. By automating routine inquiries, it frees up your team to focus on more complex tasks, delivering a smoother experience for both staff and customers. Speed to Value One of Agentforce’s standout features is its emphasis on rapid implementation. Many AI projects can be resource-intensive and take months or even years to launch. However, Agentforce enables quick deployment by leveraging existing Salesforce infrastructure, allowing organizations to implement solutions rapidly and with greater control. Salesforce also offers pre-built Agentforce agents tailored to specific business needs—such as Service Agent, Sales Development Representative Agent, Sales Coach, Personal Shopper Agent, and Campaign Agent—all customizable with the Agent Builder. Agentforce for Service and Sales will be generally available starting October 25, 2024, with certain elements of the Atlas Reasoning Engine rolling out in February 2025. Pricing begins at $2 per conversation, with volume discounts available. Transforming Customer Insights with Data Cloud and Marketing Cloud Dreamforce also highlighted enhancements to Data Cloud, Salesforce’s backbone for all cloud products. The platform now supports processing unstructured data, which constitutes up to 90% of company data often overlooked by traditional reporting systems. With new capabilities for analyzing various unstructured formats—like video, audio, sales demos, customer service calls, and voicemails—businesses can derive valuable insights and make informed decisions across Customer 360. Furthermore, Data Cloud One enables organizations to connect siloed Salesforce instances effortlessly, promoting seamless data sharing through a no-code, point-and-click setup. The newly announced Marketing Cloud Advanced edition serves as the “big sister” to Marketing Cloud Growth, equipping larger marketing teams with enhanced features like Path Experiment, which tests different content strategies across channels, and Einstein Engagement Scoring for deeper insights into customer behavior. Together, these enhancements empower companies to engage customers more meaningfully and measurably across all touchpoints. Empowering the Workforce Through Education Salesforce is committed to making AI accessible for all. They recently announced free instructor-led courses and AI certifications available through 2025, aimed at equipping the Salesforce community with essential AI and data management skills. To support this initiative, Salesforce is establishing AI centers in major cities, starting with London, to provide hands-on training and resources, fostering AI expertise. They also launched a global Agentforce World Tour to promote understanding and adoption of the new capabilities introduced at Dreamforce, featuring repackaged sessions from the conference and opportunities for specialists to answer questions. The Bottom Line What does this mean for businesses? With the rollout of Agentforce, along with enhancements to Data Cloud and Marketing Cloud, organizations can operate more efficiently and connect with customers in more meaningful ways. Coupled with a focus on education through free courses and global outreach, getting on board has never been easier. If you’d like to discuss how we can help your business maximize its potential with Salesforce through data and AI, connect with us and schedule a meeting with our team. Legacy systems can create significant gaps between operations and employee needs, slowing lead processes and resulting in siloed, out-of-sync data that hampers business efficiency. Responding to inquiries within five minutes offers a 75% chance of converting leads into customers, emphasizing the need for rapid, effective marketing responses. Salesforce aims to help customers strengthen relationships, enhance productivity, and boost margins through its premier AI CRM for sales, service, marketing, and commerce, while also achieving these goals internally. Recognizing the complexity of its decade-old processes, including lead assignment across three systems and 2 million lines of custom code, Salesforce took on the role of “customer zero,” leveraging Data Cloud to create a unified view of customers known as the “Customer 360 Truth Profile.” This consolidation of disparate data laid the groundwork for enterprise-wide AI and automation, improving marketing automation and reducing lead time by 98%. As Michael Andrew, SVP of Marketing Decision Science at Salesforce, noted, this initiative enabled the company to provide high-quality leads to its sales team with enriched data and AI scoring while accelerating time to market and enhancing data quality. Embracing Customer Zero “Almost exactly a year ago, we set out with a beginner’s mind to transform our lead automation process with a solution that would send the best leads to the right sales teams within minutes of capturing their data and support us for the next decade,” said Andrew. The initial success metric was “speed to lead,” aiming to reduce the handoff time from 20 minutes to less than one minute. The focus was also on integrating customer and lead data to develop a more comprehensive 360-degree profile for each prospect, enhancing lead assignment and sales rep productivity. Another objective was to boost business agility by cutting the average time to implement assignment changes from four weeks to mere days. Accelerating Success with

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October 16, 2024in Data, Enterprise, Generative AI, Google, Google Analytics, Marketing Automation, Salesforce
Multi AI Agent Systems
15Oct

Multi AI Agent Systems

Building Multi-AI Agent Systems: A Comprehensive Guide As technology advances at an unprecedented pace, Multi-AI Agent systems are emerging as a transformative approach to creating more intelligent and efficient applications. This guide delves into the significance of Multi-AI Agent systems and provides a step-by-step tutorial on building them using advanced frameworks like LlamaIndex and CrewAI. What Are Multi-AI Agent Systems? Multi-AI Agent systems are a groundbreaking development in artificial intelligence. Unlike single AI agents that operate independently, these systems consist of multiple autonomous agents that collaborate to tackle complex tasks or solve intricate problems. Key Features of Multi-AI Agent Systems: Applications of Multi-AI Agent Systems: Multi-agent systems are versatile and impactful across industries, including: The Workflow of a Multi-AI Agent System Building an effective Multi-AI Agent system requires a structured approach. Here’s how it works: Building Multi-AI Agent Systems with LlamaIndex and CrewAI Step 1: Define Agent Roles Clearly define the roles, goals, and specializations of each agent. For example: Step 2: Initiate the Workflow Establish a seamless workflow for agents to perform their tasks: Step 3: Leverage CrewAI for Collaboration CrewAI enhances collaboration by enabling autonomous agents to work together effectively: Step 4: Integrate LlamaIndex for Data Handling Efficient data management is crucial for agent performance: Understanding AI Inference and Training Multi-AI Agent systems rely on both AI inference and training: Key Differences: Aspect AI Training AI Inference Purpose Builds the model. Uses the model for tasks. Process Data-driven learning. Real-time decision-making. Compute Needs Resource-intensive. Optimized for efficiency. Both processes are essential: training builds the agents’ capabilities, while inference ensures swift, actionable results. Tools for Multi-AI Agent Systems LlamaIndex An advanced framework for efficient data handling: CrewAI A collaborative platform for building autonomous agents: Practical Example: Multi-AI Agent Workflow Conclusion Building Multi-AI Agent systems offers unparalleled opportunities to create intelligent, responsive, and efficient applications. By defining clear agent roles, leveraging tools like CrewAI and LlamaIndex, and integrating robust workflows, developers can unlock the full potential of these systems. As industries continue to embrace this technology, Multi-AI Agent systems are set to revolutionize how we approach problem-solving and task execution. 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

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October 15, 2024in Artificial Intelligence, Data, Technology
Generative AI Replaces Legacy Systems
15Oct

Generative AI Replaces Legacy Systems

Generative AI Will Overtake Legacy Stack Vendors With the rise of generative AI, legacy software vendors like Appian, IBM, Salesforce, SAP, Pegasystems, IFS, Oracle, Software AG, TIBCO, and UIPath are becoming increasingly obsolete. These vendors represent the old guard, clinging to outdated business process automation systems, while the future clearly belongs to AI-driven innovation. Back in the early 2010s, discussions around dynamic processes—self-assembling workflows created by artificial intelligence—were already gaining traction. The vision was to bypass the need for traditional process mapping or manually designing new interfaces. Instead, AI would dynamically generate processes in response to specific tasks, allowing for far greater flexibility and adaptability. However, business rules within BPMS (Business Process Management Systems) often imposed constraints that limited decision-making flexibility. Today, this vision is finally within reach. Many traditional stack vendors are scrambling to integrate generative AI into their offerings in a desperate bid to remain relevant. But the truth is, generative AI renders these vendors largely unnecessary. For instance, Pegasystems, like many others, now incorporates generative AI into its software, but users are still bound to old workflows and low-code development systems. The reliance on building processes, regardless of AI assistance, keeps them stuck in the past. Across the board—whether it’s ERP, CRM, or RPA—vendors such as Salesforce, SAP, and IFS remain tethered to their outdated systems, even though they possess all the necessary data, both structured and unstructured, to benefit from a simpler, AI-powered approach. All that’s needed is a generative AI layer on top to handle tasks like customer complaints. Consider a customer complaint scenario: traditionally, a complaint is processed through a defined workflow, often requiring the creation of expensive, custom SaaS solutions. But what if an LLM (Large Language Model) could handle this instead? The LLM could analyze the complaint, extract key information, assess urgency through sentiment analysis, and generate a custom workflow on the fly. It could even generate backend code in real-time to process refunds or update databases, all without relying on legacy front-end systems. The LLM’s ability to create and execute dynamic workflows eliminates the need for static business processes. The AI generates temporary code and UI elements to handle a specific interaction, then discards them once the task is complete. This shifts the focus away from traditional, bloated enterprise systems and towards dynamic, JIT (Just-In-Time) interactions that are tailored to each individual customer. The efficiency gains are not in cutting jobs but in eliminating the need for costly, antiquated software and lengthy digital transformation programs. Generative AI doesn’t require massive ERP or CRM implementations, and businesses can converse directly with customer data through AI, bypassing the need for complex system integrations. Master Data Management, which once consumed millions of dollars and years of effort, is now positioned to become a simple, AI-powered solution. Enterprises already have well-structured and clean data, and adding a generative AI layer could remove the need for integrating or syncing legacy systems. The era of major vendors selling AI-enhanced solutions built on top of decaying software stacks is coming to an end. The idea of using generative AI as the foundation for a new business operating system, without the need for bloated, legacy software, is increasingly appealing. With the global workflow automation market projected to grow to .4 billion by 2030, the future clearly belongs to AI-driven solutions. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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October 15, 2024in Artificial Intelligence, Data, Enterprise, Generative AI, Salesforce
Impact of Generative AI
14Oct

Impact of Generative AI

Generative AI has emerged as the most dominant trend in data management and analytics, overshadowing all other technologies. This prominence began with the launch of ChatGPT by OpenAI in November 2022, which significantly advanced the capabilities of large language models (LLMs) and demonstrated the transformative potential of generative AI (GenAI) for enterprises. Generative AI’s impact is profound, particularly in making advanced business intelligence tools accessible to a broader range of employees, not just data scientists and analysts. Before the advent of GenAI, complex data management and analytics platforms required computer science skills, statistical expertise, and extensive data literacy. Generative AI has reduced these barriers, enabling more people to leverage data insights for decision-making. Another key advantage of generative AI is its ability to greatly enhance efficiency. It can automate time-consuming, repetitive tasks previously performed manually by data engineers and experts, acting as an independent agent in managing data processes. The landscape of generative AI has evolved rapidly. Following the launch of ChatGPT, a wave of competing LLMs has emerged. Initially, the transformative potential of these technologies was theoretical, but it is now becoming tangible. Companies like Google are developing tools to help customers build and deploy their own generative AI models and applications. Enterprises are increasingly moving from pilot testing to developing and implementing production models. Generative AI does not operate in isolation. Enterprises are also focusing on complementary aspects such as data quality and governance. Ensuring that the data feeding and training generative AI is reliable is crucial. Additionally, real-time data and automation are essential for making generative AI a proactive technology rather than a reactive one. Generative AI has highlighted the need for a robust data foundation. The main challenge now is ensuring that enterprise data is trusted, governed, and ready for AI applications. With the rise of multimodal data, enterprises require a unified approach to manage and govern diverse data types effectively. In addition to generative AI, other significant trends in data management and analytics include the focus on real-time data processing and automation. Integrating generative AI with real-time data streams and automated systems is expected to drive substantial business transformation. By enabling real-time insights and actions, businesses can achieve a level of operational efficiency previously unattainable. The convergence of these technologies is transforming business operations. Unified and simplified technology stacks, integrating foundational technologies, LLMs, and real-time data platforms, are essential for driving this transformation. The industry is making strides towards creating integrated solutions that support comprehensive data management and analytics. 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

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October 14, 2024in Generative AI
Smartsheet and AWS Collaborate
09Oct

Smartsheet and AWS Collaborate

Smartsheet and AWS Collaborate to Enhance AI-Driven Decision-Making with New Amazon Q Business Connector October 8, 2024 — During its annual ENGAGE customer conference, Smartsheet (NYSE: SMAR), the enterprise work management platform, announced a partnership with AWS to introduce a new connector that integrates Smartsheet data with Amazon Q Business. This generative AI-powered assistant can answer questions, provide summaries, generate content, and securely complete tasks using data from customers’ enterprise systems. This integration will allow Amazon Q Business users to access insights about their projects and processes managed in Smartsheet, facilitating a cohesive search experience that empowers employees to make informed, data-driven decisions. Smartsheet and AWS Collaborate. As organizations increasingly recognize the importance of data-driven decisions, data silos remain a major hurdle. Research from Salesforce in 2024 indicates that only about 28% of business applications are interconnected. The new connector aims to address this issue by securely merging Smartsheet data with other sources integrated into Amazon Q Business, such as Salesforce, Slack, Microsoft Teams, and AWS. This will benefit over 13 million Smartsheet users globally, including around 85% of the 2024 Fortune 500 companies, allowing them to access their work management data, including sheets, conversations, and files, through AWS’s generative AI-powered assistant. This integration enhances decision-making, productivity, and efficiency. Smartsheet and AWS Collaborate “The Smartsheet connector furthers our strategy to securely integrate Smartsheet with leading enterprise AI tools, allowing customers to work seamlessly across their business applications,” said Ben Canning, SVP of Product Experiences at Smartsheet. “By combining our flexible data model with Amazon Q Business, we’re unlocking access to work management data for our mutual customers, enabling them to focus on achieving business outcomes without worrying about data storage.” For instance, service operations managers can utilize the new connector to manage complex projects more effectively. By posing specific questions to the Amazon Q Business assistant, teams can gain insights from various data sources, including sheets, conversations, and attachments in Smartsheet. The AI assistant conducts thorough searches while respecting access permissions, saving time and enhancing project oversight. This streamlined approach improves client retention, accuracy, and overall service quality. “Generative AI presents a unique opportunity for organizations to transform their internal workflows. The key is securely accessing their own data, regardless of its location or format,” stated Dilip Kumar, Vice President of Amazon Q Business at AWS. “Many enterprises use Smartsheet as their primary collaboration hub, storing billions of rows of data. Allowing Amazon Q Business users to interact with their Smartsheet data in a simple, secure manner boosts productivity, analysis, and decision-making.” “Generative AI is driving a significant shift in how enterprise knowledge is stored, accessed, and utilized,” noted Dion Hinchcliffe, VP of the CIO Practice at The Futurum Group. “This transition offers a chance to redefine what’s possible in data management. A strategic, informed approach to adopting this technology is crucial. By integrating work management data into Amazon Q Business, Smartsheet and AWS are creating a unified AI search experience across their knowledge base, unlocking the true potential of their data.” Empowering Teams to Achieve More with Generative AI Smartsheet is collaborating with industry leaders like AWS to develop AI capabilities that help enterprises manage their critical tasks more strategically and efficiently. Earlier this year, Smartsheet implemented Amazon Q Business internally to enhance knowledge management and boost employee productivity in the cloud. The Smartsheet connector exemplifies how both organizations are delivering powerful AI tools that revolutionize team workflows. Smartsheet continues to integrate generative AI throughout its platform, designed with practicality, transparency, and customer needs in mind. Smartsheet’s AI tools enable organizations to swiftly extract insights from data, create automated processes, generate text and summaries, and accomplish more with the AI assistant. Through the end of December, Smartsheet is offering its entire suite of AI tools to all customers, allowing everyone to leverage AI’s capabilities within the platform. The Smartsheet connector is currently available to Amazon Q Business customers in public preview. About Smartsheet Smartsheet is a modern enterprise work management platform trusted by millions globally, including approximately 85% of the 2024 Fortune 500 companies. As a pioneering leader in its category, Smartsheet delivers powerful solutions that drive performance and foster innovation. Visit www.smartsheet.com for more information. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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October 9, 2024in AI Tools, Data, Enterprise, Generative AI, Salesforce, Technology
Ethical AI Implementation
06Oct

Ethical AI Implementation

AI technologies are rapidly evolving, becoming a practical solution to support essential business operations. However, creating true business value from AI requires a well-balanced approach that considers people, processes, and technology. Ethical AI Implementation. AI encompasses various forms, including machine learning, deep learning, predictive analytics, natural language processing, computer vision, and automation. To leverage AI’s competitive advantages, companies need a strong foundation and a realistic strategy aligned with their business goals. “Artificial intelligence is multifaceted,” said John Carey, managing director at AArete, a business management consultancy. “There’s often hype and, at times, exaggeration about how ‘intelligent’ AI truly is.” Business Advantages of AI Adoption Recent advancements in generative AI, such as ChatGPT and Dall-E, have showcased AI’s significant impact on businesses. According to a McKinsey Global Survey, global AI adoption surged from around 50% over the past six years to 72% in 2024. Some key benefits of adopting AI include: Prerequisites for AI Implementation Successfully implementing AI can be complex. A detailed understanding of the following prerequisites is crucial for achieving positive results: 13 Steps for Successful AI Implementation Common AI Implementation Mistakes Organizations often stumble by: Key Challenges in Ethical AI Implementation Human-related challenges often present the biggest hurdles. To overcome them, organizations must foster data literacy and build trust among stakeholders. Additionally, challenges around data management, model governance, system integration, and intellectual property need to be addressed. Ensuring Ethical AI Implementation To ensure responsible AI use, companies should: Ethical AI implementation requires a continuous commitment to transparency, fairness, and inclusivity across all levels of the organization. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more 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

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October 6, 2024in Generative AI
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

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

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