, Author at gettectonic.com

23Sep

Next Gen Commerce Cloud

Salesforce has launched the next generation of Commerce Cloud, delivering a unified platform that connects B2C, DTC, and B2B commerce, along with Order Management, Payments, and more, to drive seamless customer experiences and revenue growth. With these innovations, businesses can scale across digital and physical channels while leveraging trusted AI and enterprise-wide data for smarter operations. Next Gen Commerce Cloud. Key features include Autonomous Agentforce Agents, which enhance commerce for merchants, buyers, and shoppers by automating tasks such as product recommendations and order tracking. Companies like MillerKnoll have seen success by using Commerce Cloud’s innovations to scale their workforce and drive revenue across multiple channels. New Agentforce Agents for Commerce — Merchant, Buyer, and Personal Shopper — autonomously manage tasks and improve the customer journey. They handle tasks without human intervention, such as product recommendations or order lookups, drawing insights from rich data sources like customer interactions, inventory, orders, and reviews. By tapping into unified data, these agents augment employees, offering tailored experiences and increasing efficiency, while strictly adhering to privacy and security standards. Salesforce’s Commerce Cloud now natively integrates every part of the commerce journey, helping businesses break down data silos and offer consistent, personalized interactions. As Michael Affronti, SVP and GM of Commerce Cloud, highlights: “Unified commerce is the future, breaking down silos to deliver seamless experiences across all channels.” Key new features and functionalities include: With these advancements, Commerce Cloud empowers businesses to create seamless, AI-powered experiences that drive customer loyalty, operational efficiency, and revenue growth across every touchpoint. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

September 23, 2024in Data, Enterprise, Google, Salesforce, Salesforce Commerce Cloud

22Sep

Wordle Today

Most people are familiar with Wordle by now. It’s that simple yet addictive game where players try to guess a five-letter word within six attempts. Introducing WordMap: Guess the Word of the Day A few weeks ago, while using semantic search in a Retrieval-Augmented Generation (RAG) system (for those curious about RAG, there’s more information in a previous insight), an idea emerged. What if there were a game like Wordle, but instead of guessing a word based on its letter positions, players guessed the word of the day by how close their guesses were in meaning? Players would input various words, and the game would score each one based on its semantic similarity to the target word, evaluating how related the guesses are in terms of meaning or context. The goal would be to guess the word in as few tries as possible, though without a limit on the number of attempts. This concept led to the creation of ☀️ WordMap! To develop the game, it was necessary to embed both the user’s input word and the word of the day, then calculate how semantically similar they were. The game would normalize the score between 0 and 100, displaying it in a clean, intuitive user interface. Diagram of the Workflow The Embedding ChallengeRAGs are frequently used for searching relevant data based on an input. The challenge in this case was dealing with individual words instead of full paragraphs, making the context limited. There are two types of embeddings: word-level and sentence-level. While word-level embeddings might seem like the logical choice, sentence-level embeddings were chosen for simplicity. Word-Level Embeddings Word-level embeddings represent individual words as vectors in a vector space, with the premise that words with similar meanings tend to appear in similar contexts. Key Features However, word embeddings treat words in isolation, which is a limitation. For instance, the word “bank” could refer to either a financial institution or the side of a river, depending on the context. Sentence-Level Embeddings Sentence embeddings represent entire sentences (or paragraphs) as vectors, capturing the meaning by considering the order and relationships between words. Key Features The downside is that sentence embeddings require more computational resources, and longer sentences may lose some granularity. Why Sentence Embeddings Were Chosen The answer lies in simplicity. Most embedding models readily available today are sentence-based, such as OpenAI’s text-embedding-3-large. While Word2Vec could have been an option, it would have required loading a large pre-trained model. Moreover, models like Word2Vec need vast amounts of training data to be precise. Using sentence embeddings isn’t entirely inaccurate, but it does come with certain limitations. Challenges and SolutionsOne limitation was accuracy, as the model wasn’t specifically trained to embed single words. To improve precision, the input word was paired with its dictionary definition, although this method has its own drawbacks, especially when a word has multiple meanings. Another challenge was that semantic similarity scores were relatively low. For instance, semantically close guesses often didn’t exceed a cosine similarity score of 0.45. To avoid discouraging users, the scores were normalized to provide more realistic feedback. The Final Result 🎉The game is available at WordMap, and it’s ready for players to enjoy! Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

September 22, 2024in Salesforce

22Sep

Is Agentforce Different?

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

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

21Sep

ChatGPT Open AI o1

OpenAI has firmly established itself as a leader in the generative AI space, with its ChatGPT being one of the most well-known applications of AI today. Powered by the GPT family of large language models (LLMs), ChatGPT’s primary models, as of September 2024, are GPT-4o and GPT-3.5. In August and September 2024, rumors surfaced about a new model from OpenAI, codenamed “Strawberry.” Speculation grew as to whether this was a successor to GPT-4o or something else entirely. The mystery was resolved on September 12, 2024, when OpenAI launched its new o1 models, including o1-preview and o1-mini. What Is OpenAI o1? The OpenAI o1 family is a series of large language models optimized for enhanced reasoning capabilities. Unlike GPT-4o, the o1 models are designed to offer a different type of user experience, focusing more on multistep reasoning and complex problem-solving. As with all OpenAI models, o1 is a transformer-based architecture that excels in tasks such as content summarization, content generation, coding, and answering questions. What sets o1 apart is its improved reasoning ability. Instead of prioritizing speed, the o1 models spend more time “thinking” about the best approach to solve a problem, making them better suited for complex queries. The o1 models use chain-of-thought prompting, reasoning step by step through a problem, and employ reinforcement learning techniques to enhance performance. Initial Launch On September 12, 2024, OpenAI introduced two versions of the o1 models: Key Capabilities of OpenAI o1 OpenAI o1 can handle a variety of tasks, but it is particularly well-suited for certain use cases due to its advanced reasoning functionality: How to Use OpenAI o1 There are several ways to access the o1 models: Limitations of OpenAI o1 As an early iteration, the o1 models have several limitations: How OpenAI o1 Enhances Safety OpenAI released a System Card alongside the o1 models, detailing the safety and risk assessments conducted during their development. This includes evaluations in areas like cybersecurity, persuasion, and model autonomy. The o1 models incorporate several key safety features: GPT-4o vs. OpenAI o1: A Comparison Here’s a side-by-side comparison of GPT-4o and OpenAI o1: Feature GPT-4o o1 Models Release Date May 13, 2024 Sept. 12, 2024 Model Variants Single Model Two: o1-preview and o1-mini Reasoning Capabilities Good Enhanced, especially in STEM fields Performance Benchmarks 13% on Math Olympiad 83% on Math Olympiad, PhD-level accuracy in STEM Multimodal Capabilities Text, images, audio, video Primarily text, with developing image capabilities Context Window 128K tokens 128K tokens Speed Fast Slower due to more reasoning processes Cost (per million tokens) Input: $5; Output: $15 o1-preview: $15 input, $60 output; o1-mini: $3 input, $12 output Availability Widely available Limited to specific users Features Includes web browsing, file uploads Lacks some features from GPT-4o, like web browsing Safety and Alignment Focus on safety Improved safety, better resistance to jailbreaking ChatGPT Open AI o1 OpenAI o1 marks a significant advancement in reasoning capabilities, setting a new standard for complex problem-solving with LLMs. With enhanced safety features and the ability to tackle intricate tasks, o1 models offer a distinct upgrade over their predecessors. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 21, 2024in Data, Enterprise, Generative AI

21Sep

Salesforce Certified AI Associate

The Salesforce Certified AI Associate certification is a professional credential that demonstrates your knowledge of artificial intelligence (AI) and its application within Salesforce platforms. This certification is perfect for individuals aiming to enhance their ability to use AI to drive business outcomes. Key topics covered in the certification include: Trailblazer Trailhead for Salesforce Certified AI Associate Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

September 21, 2024in AI Tools, Artificial Intelligence, Salesforce

21Sep

Large Action Models and AI Agents

The introduction of LAMs marks a significant advancement in AI, focusing on actionable intelligence. By enabling robust, dynamic interactions through function calling and structured output generation, LAMs are set to redefine the capabilities of AI agents across industries.

September 21, 2024in Data, Enterprise, Salesforce

21Sep

Embedded Salesforce Einstein

In a world where data is everything, businesses are constantly seeking ways to better understand their customers, streamline operations, and make smarter decisions. Enter Salesforce Einstein—a powerful AI solution embedded within the Salesforce platform that is revolutionizing how companies operate, regardless of size. By leveraging advanced analytics, automation, and machine learning, Einstein helps businesses boost efficiency, drive innovation, and deliver exceptional customer experiences. Embedded Salesforce Einstein is the answer. Here’s how Salesforce Einstein is transforming business: Imagine anticipating customer needs, market trends, or operational challenges before they happen. While it’s not magic, Salesforce Einstein’s AI-powered insights and predictions come remarkably close. By transforming vast amounts of data into actionable insights, Einstein enables businesses to anticipate future scenarios and make well-informed decisions. Industry insight: In financial services, success hinges on anticipating market shifts and client needs. Banks and investment firms leverage Einstein to analyze historical market data and client behavior, predicting which financial products will resonate next. For example, investment advisors might receive AI-driven recommendations tailored to individual clients, boosting engagement and satisfaction. Manufacturers also benefit from Einstein’s predictive maintenance tools, which analyze data from machinery to anticipate equipment failures. A car manufacturer, for instance, could use these insights to schedule maintenance during off-peak hours, minimizing downtime and preventing costly disruptions. Personalization is now a necessity. Salesforce Einstein elevates personalization by analyzing customer data to offer tailored recommendations, messages, and services. Industry insight: In e-commerce, personalized recommendations are often the key to converting browsers into loyal customers. An online bookstore using Einstein might analyze browsing history and past purchases to suggest new releases in genres the customer loves, driving repeat sales. In healthcare, Einstein’s personalization can improve patient outcomes by providing customized follow-up care. Hospitals can use Einstein to analyze patient histories and treatment data, offering reminders tailored to each patient’s needs, improving adherence to care plans and speeding recovery. Salesforce Einstein’s sales intelligence tools, such as Lead Scoring and Opportunity Insights, enable sales teams to focus on the most promising leads. This targeted approach drives higher conversion rates and more efficient sales processes. Industry insight: In real estate, Einstein helps agents manage numerous leads by scoring potential buyers based on their engagement with property listings. A buyer who repeatedly views homes in a specific area is flagged, prompting agents to prioritize their outreach, accelerating the sales process. In the automotive industry, Einstein identifies leads closer to purchasing by analyzing behaviors such as online vehicle configuration and test drive bookings. This allows sales teams to focus on high-potential buyers, closing deals faster. Automation is at the heart of Salesforce Einstein’s ability to streamline processes and boost productivity. By automating repetitive tasks like data entry and customer inquiries, Einstein frees employees to focus on strategic activities, improving overall efficiency. Industry insight: In insurance, Einstein Bots can handle routine tasks like policy inquiries and claim submissions, freeing up human agents for more complex issues. This leads to faster response times and reduced operational costs. In banking, Einstein-powered chatbots manage routine inquiries such as balance checks or transaction histories. By automating these interactions, banks reduce the workload on call centers, allowing agents to provide more personalized financial advice. Einstein Discovery democratizes data analytics, making it easier for non-technical users to explore data and uncover actionable insights. This tool identifies key business drivers and provides recommendations, making data accessible for all. Industry insight: In healthcare, predictive insights are helping providers identify patients at risk of chronic conditions like diabetes. With Einstein Discovery, healthcare providers can flag at-risk individuals early, implementing targeted care plans that improve outcomes and reduce long-term costs. For energy companies, Einstein Discovery analyzes data from sensors and weather patterns to predict equipment failures and optimize resource management. A utility company might use these insights to schedule preventive maintenance ahead of storms, reducing outages and enhancing service reliability. More Than a Tool – Embedded Salesforce Einstein Salesforce Einstein is more than just an AI tool—it’s a transformative force enabling businesses to unlock the full potential of their data. From predicting trends and personalizing customer experiences to automating tasks and democratizing insights, Einstein equips companies to make smarter decisions and enhance performance across industries. Whether in retail, healthcare, or technology, Einstein delivers the tools needed to thrive in today’s competitive landscape. Tectonic empowers organizations with Salesforce solutions that drive organizational excellence. Contact Tectonic today. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

September 21, 2024in Salesforce Einstein

21Sep

Data Governance Frameworks

Examples of Data Governance Frameworks Data governance is not a one-size-fits-all approach. Organizations must carefully choose a framework that aligns with their unique goals, structure, and culture. Data is one of an organization’s most valuable assets, and proper governance is key to unlocking its potential. Without a well-designed framework, companies risk poor data quality, privacy breaches, regulatory noncompliance, and missed insights. A data governance framework provides a structured way to manage data throughout its lifecycle, including policies, processes, and standards to ensure data is accurate, accessible, and secure. By putting clear guidelines in place, organizations can increase trust in their data and improve decision-making. Key Pillars of a Data Governance Frameworks A robust data governance framework typically rests on four key pillars: 1. Center-Out Model The center-out model places a centralized team, such as a data governance council, at the core of the governance process. This group establishes policies and oversees data management across the organization, balancing consistency with flexibility for different departments. The Data Governance Institute’s framework is an example of this model. It focuses on creating a Data Governance Office responsible for managing key governance functions such as setting data policies, assigning data stewards, and monitoring compliance. The framework provides a clear structure while allowing business units some leeway in adapting governance practices to their needs. PwC’s model also adopts a center-out approach, with an emphasis on using data governance to monetize data assets. It highlights the importance of maintaining consistency while minimizing the risk of data silos. 2. Top-Down Model In the top-down model, data governance is driven by executive leadership, ensuring alignment with strategic goals. This model provides authority for enforcing governance standards but may face challenges if business units feel disconnected from the central governance team. McKinsey’s framework exemplifies this approach, focusing on integrating data governance with broader business transformation efforts. Executive leadership plays a key role in ensuring that governance initiatives receive the necessary attention and resources. 3. Hybrid Model The hybrid model combines centralized governance with flexibility for individual business units. It establishes an enterprise-wide framework while allowing departments to adapt governance practices to their specific needs. The Eckerson Group’s Modern Data Governance Framework represents a hybrid approach. It emphasizes the importance of people and culture, alongside technology and processes, and encourages organizations to create a roadmap for governance that evolves as needs change. This model provides a balance between centralized control and decentralized flexibility. 4. Bottom-Up Model In the bottom-up model, data governance is driven by subject matter experts and data stakeholders across the organization. This approach promotes collaboration and buy-in from the people closest to the data, ensuring that governance policies are practical and effective. The DAMA-DMBOK framework, developed by the Data Management Association, is a prime example. Although flexible, it often starts as a bottom-up initiative, driven by IT departments and data experts who later gain executive support. 5. Silo-In Model The silo-in model allows individual business units or departments to create their own governance practices. While this approach addresses localized data issues, it often leads to inconsistencies and challenges when the organization needs to integrate data across the enterprise. Though not widely recommended, the silo-in approach may emerge when specific business units take the initiative to establish governance due to regulatory requirements or data management needs within their domains. However, as organizations mature, they often transition to more holistic frameworks to support cross-functional collaboration and data integration. Choosing the Right Framework Selecting the right data governance framework involves evaluating the organization’s needs, structure, and culture. Whether an organization adopts a center-out, top-down, hybrid, bottom-up, or silo-in approach, success depends on involving key stakeholders, securing executive buy-in, and committing to continuous improvement. By treating data as a critical asset and implementing a governance framework that aligns with its business strategy, an organization can ensure that its data management practices support growth, innovation, and regulatory compliance. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

September 21, 2024in Data, Enterprise, Salesforce, Technology

21Sep

Microsoft Copilot

The fundamental capabilities of collaboration platforms have remained largely unchanged since the pandemic began. These platforms typically offer video conferencing, desktop sharing, and text chat, creating a virtual approximation of in-person meetings. While this setup effectively allows teams to collaborate across distances, it raises the question: Is this all there is to the collaboration experience? Enter Copilot. Microsoft is pioneering a new era of collaboration, where AI assistants help users prioritize meetings, manage follow-ups on action items, and integrate meeting outputs into future tasks. This evolution is particularly promising for knowledge workers who are overwhelmed by constant meetings. Copilot aims to redefine the collaboration experience, promising increased productivity and a more strategic approach to meetings. However, OpenAI, Microsoft’s prominent AI partner, is making moves to disrupt the enterprise space as well. OpenAI recently launched ChatGPT Enterprise, which now boasts 600,000 users, including clients from 93% of the Fortune 500. This week, OpenAI also acquired the videoconferencing startup Multi, sparking speculation that the company may integrate collaboration features directly into ChatGPT. Multi’s unique approach to videoconferencing—described as “multiplayer” and drawing parallels to gaming rather than traditional meetings—hints at a potential shift in how meetings are experienced. The Multi tool, set to be discontinued in July following the acquisition, was tailored for software developers, focusing on screen sharing and leveraging Zoom’s video capabilities. Yet, the concept of enhanced document collaboration extends beyond software developers. Integrating document collaboration with AI-driven features like summarization, and linking this to advanced language models, could revolutionize the collaboration experience. This approach promises to streamline the collaborative process, focusing on the work at hand with new functionalities. That said, not all meetings revolve around documents. Many are simply conversations—often the ones people prefer to avoid. Therefore, refining how meetings are managed and integrating them into users’ work lives will remain crucial, even as new technologies enhance screen sharing and video capabilities. So, where does this leave traditional video services? The quest for meeting equity and AI-enhanced directors will likely continue to refine the experience, striving for the “next best thing to being there.” As the collaboration platform evolves, any outdated elements will become more apparent. Ultimately, collaboration is a multifaceted experience, and technology will play a key role in its continued advancement. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

September 21, 2024in Enterprise, Salesforce, Technology

20Sep

Thoughts on Workday With Illuminate

Workday Expands AI Across HR and Finance Platforms with ‘Illuminate’ Workday is significantly enhancing its AI capabilities within its HR and finance platforms through a new set of updates called Illuminate. These updates aim to improve automation and increase productivity by embedding AI more broadly across various HR processes. From routine tasks like content generation to complex problem-solving, Workday’s AI now identifies inefficiencies in HR workflows and offers recommendations for improvement. Thoughts on Workday With Illuminate follow. A key feature of Illuminate is a series of AI agents designed to assist in areas such as succession planning. These agents can suggest internal candidates that HR teams might overlook, helping organizations identify potential leaders within their workforce. During a press briefing ahead of the Workday Rising conference, TechTarget asked if the AI agent used in succession planning could fully capture the intricacies of the employee experience and assess leadership potential. David Somers, Chief Product Officer at Workday, acknowledged the sensitivity of succession planning but emphasized that AI is used to augment—not replace—human decision-making. The agents provide recommendations, while the final hiring decisions still involve talent acquisition professionals and interview panels. Workday’s updates include tools for a wide range of tasks, from content summarization to more advanced capabilities such as detecting bottlenecks in onboarding processes and suggesting optimizations. “These AI agents will streamline common business workflows, boosting productivity and freeing up users to focus on strategic, meaningful work,” Somers explained. While AI has long been part of Workday’s offerings, generative AI is now driving rapid transformation in HR practices. Workday’s Illuminate platform combines data with contextual insights, offering features like compensation data tailored to a company’s specific information. Users can access these AI capabilities through Workday Assistant, a generative AI chatbot that integrates with Microsoft Teams and Slack. This tool will be generally available early next year, making it easier for teams to interact with Workday’s AI-powered systems. HR industry expert Josh Bersin sees Workday’s Illuminate as part of a broader trend of AI agents in the HR space, similar to SAP’s Joule. He believes Workday’s new AI agents will be a major focus for the company, though building out all the necessary Workday transactions into these tools will take time. Bersin does not foresee trust issues among Workday users regarding Illuminate, noting that the platform isn’t open to non-Workday data, which limits concerns around data security. Bersin’s own AI assistant, Galileo, is also expected to integrate with Workday’s platform in the future, further enhancing its capabilities. rativAccording to recent Gartner surveys from March and June, the majority of HR leaders are adopting AI in their organizations. Only 15% of respondents indicated they had no plans to incorporate generative AI into their HR processes, signaling widespread acceptance of AI tools like those Workday is rolling out with Illuminate. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 20, 2024in AI Tools, Data, Generative AI

20Sep

Salesforce Email Deliverability Settings

Salesforce Email Deliverability Settings: Managing Communication in Sandboxes Salesforce provides administrators with control over the types of emails that can be sent from their environments, especially within sandbox environments used for development and testing. These email deliverability settings ensure that sensitive or erroneous emails don’t reach actual users during development. Below, we’ll dive into the details of these settings and explain their impact. Email Deliverability Settings in Salesforce Where to Find Deliverability Settings: Note: If Salesforce has restricted your ability to change these settings, they may not be editable. Three Access Levels for Email Deliverability Salesforce offers three key deliverability settings that control email access in your organization: The Importance of the “System Email Only” Setting The System Email Only setting is particularly valuable in sandbox environments. When testing workflows, triggers, or automations in a sandbox, this setting ensures only critical system emails (e.g., password resets) are sent, preventing development or test emails from reaching real users. New Sandboxes Default to System Email Only Since Salesforce’s Spring ’13 release, new and refreshed sandboxes default to the System Email Only setting. This helps prevent accidental email blasts during testing. For sandboxes created before Spring ’13, the default setting is All Email, but it’s recommended to switch to System Email Only to avoid sending test emails. Example: If you’re testing a custom email alert in a sandbox for a retail company, this setting allows you to safely test without worrying about sending emails to actual customers. Bounce Management in Salesforce Bounce management helps you track and manage email deliverability issues, particularly for emails sent via Salesforce or through an email relay. Key Points for Managing Bounces: Creating Custom Bounce Reports in Lightning Experience If the standard bounce reports aren’t available in your organization, or if you’re using Salesforce Lightning, you can create custom reports using the Email Bounced Reason and Email Bounced Date fields. To create a report in Lightning: By configuring Salesforce email deliverability settings and managing bounces, administrators can ensure smooth, secure communication across their organization—especially when working in sandbox environments. These tools help maintain control over outbound emails, protecting users from erroneous communication while providing valuable insights into email performance. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

September 20, 2024in Salesforce

20Sep

Critical Field Service Challenges with Connected Data and AI

Set Up for Success: Tackling Critical Field Service Challenges with Connected Data and AI Today’s customers demand faster, more personalized service, and field service is no exception. Research shows that 74% of mobile workers report that customer expectations have risen, with 73% noting an increased demand for a personal touch. This is shaping key trends in the field service industry. Trend #1: Rising Customer Expectations Amid a Shrinking Workforce Field service teams are grappling with rising customer expectations while dealing with a declining mobile workforce. In fact, 74% of mobile workers report increasing workloads. Given that mobile workers are often the only in-person company representatives, they face intense pressure to deliver exceptional service. At the same time, fewer young people are entering skilled trades, with applications dropping nearly 50% from 2020 to 2022, while seasoned technicians are retiring. This has led to high burnout rates, with 57% of mobile workers experiencing job-related fatigue. Trend #2: Connected Data Empowers Mobile Workers Mobile workers thrive when equipped with connected data. Yet, they spend only 32% of their time interacting with customers, as much of their time is consumed by manual tasks and disjointed systems. With the right technology, mobile workers can access up-to-date customer information through a CRM mobile app, streamlining processes and enabling more personalized service. Connected data also improves sustainability, with features like route optimization and drones reducing time on the road and minimizing worker stress. Trend #3: AI is Revolutionizing Field Service AI is rapidly transforming field service operations. Today, 79% of service organizations are investing in AI, and 83% of decision-makers plan to increase their AI investments next year. AI helps mobile teams save time and cut costs by analyzing customer data to generate personalized responses and streamline processes. By automating workflows with AI, mobile workers can deliver faster, more efficient service. AI-generated summaries of asset history and service interactions help prepare workers before they arrive at a job site, enabling better service and potential upsell opportunities. What’s Next in Field Service? Technologies like generative AI, augmented reality, and mobile solutions are shaping the future of field service. Companies that embrace these innovations now will gain a competitive edge. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

September 20, 2024in Generative AI, Salesforce Optimization, Salesforce Service Cloud

20Sep

Bye Klarna

Fintech firm Klarna is cutting ties with two major enterprise software providers, opting to automate its services using AI, and hints that more cuts could follow. Klarna co-founder and CEO Sebastian Siemiatkowski discussed the move during a recent conference call, as reported by Seeking Alpha. The company has already stopped using Salesforce, a platform that helps businesses manage sales and marketing data, and has also removed Workday, an HR and hiring platform, from its tech stack, according to a spokesperson from Klarna. This shift towards AI-driven automation is part of a larger strategy at Klarna. “We have multiple large-scale initiatives combining AI, standardization, and simplification that will allow us to eliminate several SaaS providers,” a company spokesperson said, though they did not specify which providers or services might be next. Founded in 2005, Klarna provides payment processing for e-commerce and reports over 150 million active users worldwide. Despite posting a net loss of $241 million last year—down from nearly $1 billion in 2022—the company reported a reduced loss of $32 million for the first half of 2024. With reports suggesting that Goldman Sachs has been tapped to underwrite Klarna’s potential IPO, the company’s focus on AI could strengthen its profitability prospects. This isn’t Klarna’s first AI initiative. Earlier in 2024, the company introduced an AI-powered customer service assistant in collaboration with OpenAI, which reportedly handled 2.3 million interactions in its first month and replaced the work of 700 agents. Klarna was among the early adopters of OpenAI’s enterprise ChatGPT package, and the company claims that 90% of its employees use the tool daily for process automation. Klarna’s decision to drop Salesforce and Workday is part of a broader effort to replace third-party SaaS solutions with internally developed applications, likely built on OpenAI’s infrastructure. Siemiatkowski stated in the August call, “We are shutting down a lot of our SaaS providers as we are able to consolidate.” However, not everyone is convinced. HR technology analyst Josh Bersin questioned whether Klarna could successfully replace a robust platform like Workday. “Workday systems have decades of workflows and complex data structures, including payroll and attendance,” he told Inc.. Bersin warned that developing an in-house system could lead Klarna into a “black hole of features,” with a poor user experience as a result. Many in the tech world share Bersin’s skepticism, with some suggesting the move is more of a PR tactic as Klarna gears up for its IPO. Investors and executives voiced doubts on social media, with financial insights account BuccoCapital posting on X, “Is it actually the best use of capital to rebuild in-house? Feels like a massive distraction,” while Ryan Jones, CEO of Flighty, called the move “free marketing.” Critics also point out that Klarna has downsized its workforce significantly, reducing its headcount by 1,200 over the past year, and Siemiatkowski has hinted at further reductions, suggesting the company could benefit from cutting staff from 3,800 to 2,000 employees. Siemiatkowski remains adamant that AI will allow Klarna to maintain growth despite these cuts. Bersin also noted that many tech giants have struggled to build their own HR platforms, citing examples like Google, which recently abandoned its internally developed HR software, and Amazon, which undergoes similar cycles regularly. “Microsoft,” Bersin added, “spends money on their own products but partners with SAP for HR software.” If Klarna does succeed in developing an in-house HR platform, it would be an achievement where even some of the biggest tech companies have fallen short. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

September 20, 2024in Salesforce

20Sep

Collaborative Business Intelligence

Collaborative Business Intelligence: Connecting Data and Teams In today’s data-driven world, the ability to interact with business intelligence (BI) tools is essential for making informed decisions. Collaborative business intelligence (BI), also known as social BI, allows users to engage with their organization’s data and communicate with data experts through the same platforms where they already collaborate. While self-service BI empowers users to generate insights, understanding the data’s context is critical to avoid misunderstandings that can derail decision-making. Collaborative BI integrates BI tools with collaboration platforms to bridge the gap between data analysis and communication, reducing the risks of misinterpretation. Traditional Business Intelligence Traditional BI involves the use of technology to analyze data and present insights clearly. Before BI platforms became widespread, data scientists and statisticians handled data analysis, making it challenging for non-technical professionals to digest the insights. BI evolved to automate visualizations, such as charts and dashboards, making data more accessible to business users. Previously, BI reports were typically available only to high-level executives. However, modern self-service BI tools democratize access, enabling more users—regardless of technical expertise—to create reports and visualize data, fostering better decision-making across the organization. The Emergence of Collaborative BI Collaborative BI is a growing trend, combining BI applications with collaboration tools. This approach allows users to work together synchronously or asynchronously within a shared platform, making it easier to discuss data reports in real time or leave comments for others to review. Whether it’s through Slack, Microsoft Teams, or social media apps, users can receive and discuss BI insights within their usual communication channels. This seamless integration of BI and collaboration tools offers a competitive edge, simplifying the process of sharing knowledge and clarifying data without switching between applications. Key Benefits of Collaborative Business Intelligence Leading Collaborative BI Platforms Here’s a look at some of the top collaborative BI platforms driving innovation in the market: Conclusion Collaborative BI empowers organizations by improving decision-making, democratizing data access, optimizing data quality, and ensuring data security. By integrating BI tools with collaboration platforms, businesses can streamline their operations, foster a culture of data-driven decision-making, and enhance overall efficiency. Choosing the right platform is key to maximizing the benefits of collaborative BI. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

September 20, 2024in Data, Google, Salesforce, Technology

19Sep

Apple New AI

Apple Unveils New AI Features at “Glowtime” Event In typical fashion, Apple revealed its latest product updates on Monday with a pre-recorded keynote titled “Glowtime,” referencing the glowing ring around the screen when Apple Intelligence is activated. Though primarily a hardware event, the real highlight was the suite of AI-powered features coming to the new iPhone models this fall. The 98-minute presentation covered updates to iPhones, AirPods, and the Apple Watch, with Apple Intelligence being the thread tying together user experiences across all devices. MacRumors has published a detailed list of all announcements, including the sleep apnea detection feature for the Apple Watch and new hearing health tools for AirPods Pro 2. Key AI Developments for Brand Marketers Apple Intelligence was first introduced at its WWDC event in June, focusing on using Apple’s large language model (LLM) to perform tasks on-device with personalized results. It draws from user data in native apps like Calendar and Mail, enabling AI to handle tasks like image generation, photo searches, and AI-generated notifications. The keynote also introduced a new “Visual Intelligence” feature for iPhone 16 models, acting as a native visual search tool. By pressing the new “camera control” button, users can access this feature to perform searches directly from their camera, such as getting restaurant info or recognizing a dog breed. Apple’s AI-powered visual search offers a strategic opportunity for brands. The information for local businesses is pulled from Apple Maps, which relies on sources like Yelp and Foursquare. Brands should ensure their listings are well-maintained on these platforms and consider optimizing their digital presence for visual search tools like Google Lens, which integrates with Apple’s search. The Camera as an Input Device and the Rise of Spatial Content The camera’s role as an input device has been expanding, with Apple emphasizing photography as a key feature of its new iPhones. This year, the iPhone 16 introduces a new camera control button, offering enhanced haptic feedback for smoother control. Third-party apps like Snapchat will also benefit from this addition, giving users more refined camera capabilities. More importantly, iPhone 16 models can now capture spatial content, including both photos and audio, optimized for the Vision Pro mixed-reality headset. Apple’s move to integrate spatial content aligns with its goal to position the iPhone as a professional creator tool. Brands can capitalize on this by exploring augmented reality (AR) features or creating immersive user-generated content experiences. Apple’s Measured Approach to AI While Apple is clearly pushing AI, it is taking a cautious, phased approach. Though the new iPhones will hit the market soon, the full range of Apple Intelligence features will roll out gradually, starting in October with tools like the AI writing assistant and photo cleanup. More advanced features will debut next spring. This measured approach allows Apple to fine-tune its AI, avoiding rushed releases that could compromise user experience. For brands, this offers a lesson in pacing AI adoption: prioritize quality and customer experience over speed. Rather than rushing to integrate AI, companies should take time to understand how it can meaningfully enhance user interactions, focusing on trust and consistency to maintain customer loyalty. By following Apple’s lead and gradually introducing AI capabilities, brands can build trust, sustain anticipation, and ensure they offer technology that genuinely improves the customer experience. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 19, 2024in Data, Google, Technology

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