, Author at gettectonic.com - Page 57
Einstein Prediction Builder
19Apr

Einstein Prediction Builder

Einstein Prediction Builder, a sophisticated yet user-friendly tool from Salesforce Einstein, empowers users to generate predictions effortlessly, without requiring machine learning expertise or coding skills. This capability enables businesses to augment their operations with foresight-driven insights. As of the Spring ’20 release, all Enterprise Edition and above orgs can build one free prediction with Einstein Prediction Builder. Consider the potential business outcomes unlocked by leveraging Einstein Prediction Builder. Let’s delve into a hypothetical scenario: Meet Mr. Claus, the owner of ‘North Claus,’ a business that began as a modest family venture but gradually expanded its footprint. As ‘North Claus’ burgeoned across 10 countries, Mr. Claus recognized the need for Business Intelligence (BI) to navigate market dynamics effectively. BI entails gathering insights to forecast and comprehend market shifts—an imperative echoed by Jack Ma’s famous adage, “Adopt and change before any major trends and changes.” Intrigued by the prospect of BI, especially amidst the disruptive backdrop of Covid-19, Mr. Claus embarked on a journey to implement it in his company. The Formation of Business Intelligence: In today’s digital landscape, businesses amass vast amounts of data from diverse sources such as sales, customer interactions, and website traffic. This data serves as the bedrock for deriving actionable insights, enabling organizations to formulate forward-looking strategies. However, developing robust BI capabilities poses several challenges: Mr. Claus grappled with these challenges as he endeavored to develop BI independently. Recognizing the complexity involved, he turned to Salesforce, particularly intrigued by Einstein Prediction Builder. Einstein Prediction Builder Trailhead Understanding Einstein Prediction Builder: Einstein Prediction Builder, available in various Salesforce editions, leverages checkbox and formula fields to generate predictions. Before utilizing Prediction Builder, certain prerequisites must be met: Creating Einstein Predictions: To initiate the creation of Einstein Predictions, users navigate to Setup and access the Einstein Prediction Builder. The guided Setup simplifies the process, guiding users through relevant data inputs at each step. Once configured, predictions can be enabled, disabled, or cloned as needed. Key Features and Applications: Einstein Predictions integrate seamlessly with Salesforce Lightning, providing predictive insights directly on record pages. These predictions offer invaluable guidance on various aspects, such as sales opportunities and payment delays. Additionally, Prediction Builder facilitates packaging of predictions for seamless deployment across orgs and supports integration with external platforms like Tableau. Prediction Builder equips businesses with the intelligence needed to anticipate market trends, optimize workflows, and enhance customer interactions. As Mr. Claus discovered, embracing predictive analytics can revolutionize decision-making and drive sustainable growth. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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April 19, 2024in Einstein Insights, Salesforce, Salesforce Einstein
CPQ Called in Salesforce
18Apr

Expand the Salesforce CPQ Toolbox

Dealing with highly configurable products poses challenges in the sales process for both buyers and sellers, particularly evident in industries like medical devices or manufacturing equipment. Custom products lack visualization, adjustable pricing complicates financial reporting, and complex billing requirements may discourage customers. Now is the time to Expand the Salesforce CPQ Toolbox. Even with exceptional sales representatives, the failure to provide product information quickly and accurately can result in the loss of leads and longstanding customers. Amid the industry’s abrupt shift due to the Covid-19 pandemic, some companies have successfully navigated hurdles by adopting new and innovative digital sales platforms, as indicated by McKinsey survey results. Salesforce Configure, Price, Quote (CPQ) appears well-suited for the remote sales era, making the B2B sales experience immediate and immersive. Integration with various platforms can further simplify the process and enhance customer relationships and financial visibility. Visual Configurators Augmented Reality Automated Billing Accounting Platform Integration Leveraging CPQ and integrating with these platforms can transform the sales process, providing accurate product visuals, immediate quoting, and streamlined billing systems, meeting customer expectations in an evolving market. Contact Tectonic today to explore CPQ, Salesforce Revenue Cloud, and product configuration tools. Like2 Related Posts 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 Salesforce Government Cloud: Ensuring Compliance and Security Salesforce Government Cloud public sector solutions offer dedicated instances known as Government Cloud Plus and Government Cloud Plus – Defense. Read more PII Explained Personal Identifiable Information (PII) is defined as: Any representation of information that permits the identity of an individual to whom Read more Best CPQ for Salesforce Many businesses, once they select the best Salesforce CPQ tool for their business, turn to an implementation partner like Tectonic Read more

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April 18, 2024in Salesforce CPQ, Salesforce Health and Life Sciences Solutions, Salesforce Manufacturing Automotive Energy
Who Calls AI Ethical
18Apr

Who Calls AI Ethical

Background – Who Calls AI Ethical On March 13, 2024, the European Union (EU) enacted the EU AI Act, a move that some argue has hindered its position in the global AI race. This legislation aims to ‘unify’ the development and implementation of AI within the EU, but it is seen as more restrictive than progressive. Rather than fostering innovation, the act focuses on governance, which may not be sufficient for maintaining a competitive edge. The EU AI Act embodies the EU’s stance on Ethical AI, a concept that has been met with skepticism. Critics argue that Ethical AI is often misinterpreted and, at worst, a monetizable construct. In contrast, Responsible AI, which emphasizes ensuring products perform as intended without causing harm, is seen as a more practical approach. This involves methodologies such as red-teaming and penetration testing to stress-test products. This critique of Ethical AI forms the basis of this insight,and Eric Sandosham article here. The EU AI Act To understand the implications of the EU AI Act, it is essential to summarize its key components and address the broader issues with the concept of Ethical AI. The EU defines AI as “a machine-based system designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment. It infers from the input it receives to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.” Based on this definition, the EU AI Act can be summarized into several key points: Fear of AI The EU AI Act appears to be driven by concerns about AI being weaponized or becoming uncontrollable. Questions arise about whether the act aims to prevent job disruptions or protect against potential risks. However, AI is essentially automating and enhancing tasks that humans already perform, such as social scoring, predictive policing, and background checks. AI’s implementation is more consistent, reliable, and faster than human efforts. Existing regulations already cover vehicular safety, healthcare safety, and infrastructure safety, raising the question of why AI-specific regulations are necessary. AI solutions automate decision-making, but the parameters and outcomes are still human-designed. The fear of AI becoming uncontrollable lacks evidence, and the path to artificial general intelligence (AGI) remains distant. Ethical AI as a Red Herring In AI research and development, the terms Ethical AI and Responsible AI are often used interchangeably, but they are distinct. Ethics involve systematized rules of right and wrong, often with legal implications. Morality is informed by cultural and religious beliefs, while responsibility is about accountability and obligation. These constructs are continuously evolving, and so must the ethics and rights related to technology and AI. Promoting AI development and broad adoption can naturally improve governance through market forces, transparency, and competition. Profit-driven organizations are incentivized to enhance AI’s positive utility. The focus should be on defining responsible use of AI, especially for non-profit and government agencies. Towards Responsible AI Responsible AI emphasizes accountability and obligation. It involves defining safeguards against misuse rather than prohibiting use cases out of fear. This aligns with responsible product development, where existing legal frameworks ensure products work as intended and minimize misuse risks. AI can improve processes such as recruitment by reducing errors compared to human solutions. AI’s role is to make distinctions based on data attributes, striving for accuracy. The concern is erroneous discrimination, which can be mitigated through rigorous testing for bias as part of product quality assurance. Conclusion The EU AI Act is unlikely to become a global standard. It may slow AI research, development, and implementation within the EU, hindering AI adoption in the region and causing long-term harm. Humanity has an obligation to push the boundaries of AI innovation. As a species facing eventual extinction from various potential threats, AI could represent a means of survival and advancement beyond our biological limitations. 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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April 18, 2024in Salesforce
Sales Cloud Einstein
16Apr

Manage Sales Cloud Users Access to Einstein Features

In order to Manage Sales Cloud Users Access to Einstein Features, follow these steps. Duplicate the Sales Cloud Einstein For Everyone permission set, activate the permission, and allocate it to users. Navigate to Setup and enter “Permission Sets” in the Quick Find. Then, choose Permission Sets. Locate and click on the Sales Cloud Einstein For Everyone permission set. Click on “Manage Assignments.” Sales Cloud Einstein Features Einstein performs the tasks of cleansing, unifying, and updating activity data across Salesforce, email, and the calendar. This functionality enables Einstein to automatically search for contacts and opportunities by analyzing information related to emails and events. What’s covered in Sales Cloud Einstein? Sales Cloud Einstein functions as an in-house data science department, learning from your sales team‘s activities and CRM data. It assists in identifying top leads, streamlining opportunity conversions, and facilitating customer retention. Sales Cloud Einstein also encompasses the Sales Analytics app and Inbox. How can users be added to Einstein Activity Capture? In Setup, search for “Permission Sets” in the Quick Find. Then, select Permission Sets. Click on the Standard Einstein Activity Capture permission set. Use “Manage Assignments” to assign the permission set to a maximum of 100 users. What advantages does Einstein Sales Cloud offer? In Sales Cloud, Einstein AI optimizes daily business processes, leading to significant profit rate increases by understanding both current and potential customers. Salesforce provides valuable data sources for Einstein, including access to emails, calendars, tweets, and, importantly, customer data. How do I deactivate Einstein Activity Capture for a user? Utilize the Quick Find search bar on the left-hand side and search for “Einstein Activity Capture.” Under the Einstein Activity Capture section in the search results, select “Settings.” Turn off the Einstein Activity Capture settings and press Save to confirm the change. Is Sales Cloud Einstein a part of Sales Cloud? Salesforce Einstein is the world’s first “generative AI” designed for CRM. It seamlessly integrates into various Salesforce products within the Customer 360 portfolio, including Marketing Cloud and Sales Cloud. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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April 16, 2024in Einstein Insights, Salesforce Einstein, Salesforce Sales Cloud
Salesforce in a Mega-Data Deal with Informatica
15Apr

Salesforce in a Mega-Data Deal with Informatica

Since Salesforce announced its acquisition of Slack for $27.7B in late 2020, the cloud software mega-giant has paused its acquisition strategy due to factors like rising interest rates, declining revenues, and a laser focus on profitability. However, recent leaks from The Wall Street Journal and other news publications suggest that Salesforce in a Mega-Data Deal with Informatica, is in advanced talks to acquire Informatica in a deal worth over $11B. Informatica is a significant player in enterprise data management, boasting revenues of over $1.51B and a workforce of over 5,000 employees. They specialize in AI-powered cloud data management, assisting companies in processing and managing large volumes of data from various sources to derive actionable and real-time insights. Salesforce in a Mega-Data Deal with Informatica The synergies between Informatica and Salesforce are many, with both companies focusing on consolidating data from multiple sources to provide comprehensive business insights. This aligns well with Salesforce’s strategic shift towards AI-driven data processing and analysis, aiming to enhance generative and predictive capabilities. While Salesforce’s previous acquisition of MuleSoft in 2018 for $6.5B has proven successful in facilitating API connectivity for real-time integrations, Informatica brings expertise in ETL (Extract-Transform-Load), data quality, and data movement to and from platforms like Snowflake and Databricks. This potential mega-data deal underscores the growing importance of data in the tech industry, especially with the emergence of generative AI and large language models (LLMs) that enable deeper analysis of vast datasets. Salesforce’s recent rebranding of its platform to “Einstein 1” underscores the convergence of AI and data within its product suite. The company’s emphasis on “AI + Data + CRM” reflects its commitment to leveraging data analytics for CRM enhancement, exemplified by the growth of its Data Cloud product. Partnering with industry leaders like Snowflake, Databricks, AWS, and Google, Salesforce aims to offer comprehensive data solutions that integrate seamlessly with existing systems. Informatica’s capabilities in ETL and Master Data Management (MDM) align with this vision, particularly in streamlining data integration and ensuring data quality across disparate systems. In final thoughts, while the Informatica acquisition is still pending finalization, it represents a strategic move by Salesforce to strengthen its position in the AI and data-driven CRM market. As Salesforce continues to evolve its product ecosystem, this acquisition signals its commitment to innovation and leadership in the era of AI-powered data 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 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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April 15, 2024in Generative AI, Mulesoft, Salesforce, Snowflake
Self Service Customer Service
15Apr

Self Service Customer Service

The importance of effective customer service, particularly through self-service options, cannot be overstated. Both customers and organizations often prefer self-service solutions: customers to avoid waiting on hold and speaking with potentially uninformed agents, and organizations to reduce the load and cost associated with live agent interactions. Despite the clear benefits, the customer experience with self-service often falls short because it tends to prioritize business efficiencies over customer needs. For self-service to truly succeed, it must be mutually beneficial for both businesses and customers. According to Salesforce’s “State of the Connected Customer” study, 61% of customers prefer using self-service over live-agent phone calls for resolving simple issues. This trend is reflected in the growing use of self-service portals and chatbots, with 72% of customers utilizing self-service portals and 55% engaging with self-service chatbots. However, a significant barrier remains: 68% of customers would avoid using a company’s chatbot after a negative experience. The challenge lies in moving from a business-centric approach to a customer-centric one when deploying self-service technologies. Often, businesses implement these solutions primarily to cut costs, which can result in poorly designed interfaces that fail to meet customer expectations. This disconnect can harm customer satisfaction and loyalty in the long run. The integration of AI offers a promising solution. Unlike earlier iterations, today’s AI-driven chatbots can deliver personalized, context-aware interactions based on customer data and history. This capability ensures that customers receive timely, relevant assistance across multiple channels, enhancing the overall self-service experience. When deploying self-service capabilities, organizations should adopt a customer-first mindset: Successful self-service implementation hinges on these considerations, aiming not only to deflect calls but also to elevate customer satisfaction through intuitive, responsive self-service experiences. For further insights on optimizing self-service strategies, join our upcoming webinar discussing holistic CX strategies on July 10. We look forward to exploring how to empower customers to self-serve effectively, ensuring mutual benefits for organizations and their clientele. Customers Expect a Lot from Self-Service, and Too Few Get What They Want or Need Customers expect a lot from self-service channels — more than them just being available 24/365. They want answers to myriad questions or issues, and information about products and services. But the average self-service success rate today is just 14%. Improving this rate is a significant or moderate priority for 90% of customer service and support leaders Gartner recently surveyed. Customer support teams must provide always-on problem-solving across all of the self-service channels they offer — from site search to AI chatbots, to the portal to IVR and messaging apps. To think about the entirety of the modern service delivery model — even as customer demands evolve — focus on a few key areas: Gartner recommends that to meet the support organization’s goals and objectives, the self-service experience should include 11 foundational capabilities. Each improves some aspects of CX and elements of the search-to-resolution process. Together they drive significantly more business value, create effortless customer experiences, and improve overall self-service adoption and success. Here are the 11 capabilities: 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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April 15, 2024in Salesforce
Paradox of Writing With AI
14Apr

Paradox of Writing With AI

It seems like some people honestly believe they can spot AI-generated content immediately, but that’s not always the case. Well-written content isn’t inherently AI-generated, and if it is AI-generated, that doesn’t necessarily mean it’s well-written. The quality of writing often depends more on the writer’s skill than the tools they use. Paradox of Writing With AI is that it will make a good writer better. And it will make a bad writer worse. The real difference in human versus AI content lies in the accessibility of writing tools and the lack of proper ethical regulation for their use. This ease of access makes it simple for people to feel entitled to judge written content. True, if you publish your writing – online or elsewhere – you open it up for judgement. But imagine if UX design or data applications were graded as indiscriminately—those discussions would likely be confined to experts rather than becoming public debates on social media condemning all well-written content. Good writing requires creativity, flair, and uniqueness, among other skills, to truly impress readers. Good writing is well-organized and flows well with consistent style or voice from beginning to end. Good writing is also free from mistakes and errors in spelling, punctuation and grammar. But that alone doesn’t make it engaging or meaningful. A good writer will brainstorm for great ideas and follow them up with research. A good writer can think of fresh angles to view a topic. A good writer is sure to re-write and self-edit to make a better draft. AI has been integrated into various tools and applications long before ChatGPT was launched. Search engines use it to provide relevant results; social media algorithms keep your favorite content visible; Siri and Alexa rely on natural language processing and speech recognition; Netflix and Spotify use AI recommendation systems to cater to your tastes, and so on. AI enhances human ideas, not just in writing, but across many fields. Writing With AI is Inevitable For instance, Chinese Nobel laureate Mo Yan surprised everyone at the 65th-anniversary celebration of Shouhuo magazine by revealing he uses ChatGPT. During his speech praising fellow author Yu Hua, he mentioned that he struggled to write a commendation and asked a doctoral student to use ChatGPT for help. This revelation caused quite a stir, as it was unexpected for a Nobel Prize winner to use AI for writing. Why shouldn’t he? If AI makes a good writer better, then most of us should be employing it. Mo Yan isn’t alone. Rie Kudan, the 17th winner of Japan’s Akutagawa Prize, admitted to using ChatGPT for her novel, Tokyo-to Dojo-to. She stated that about 5% of the book consists of AI-generated sentences. Kudan, who is introverted, shared that frequent interactions with the AI tool allowed her to express personal thoughts she couldn’t comfortably discuss with others. ChatGPT’s responses often sparked dialogue in her novel, adding a unique dimension to her writing process. Grammarly, another AI tool, is why some people’s writing doesn’t reflect their irritation when discussing AI-generated content online. Grammarly has been widely used for editing and proofreading, ensuring users’ writing maintains a promotional tone and corrects errors without sounding sarcastic or bored. The Problem with Sounding Alike & The Uniqueness of a Writer’s Voice A significant issue with AI-generated content is that many written works sound similar. Writers need to develop unique voices. While Jane Austen, Mary Shelley, and the Brontë sisters are admirable, emulating their ornate language can interfere with communication’s primary purpose. Excessive fanciness can make speech overly flamboyant, akin to Oscar Wilde’s works. However asking AI to work through your content and put it in the voice of a known writer, add humor, or change the tense is time saving. The problem isn’t that AI enables people to produce well-crafted content. Many individuals have exceptional writing skills and huge vocabularies. The real issue is the uniformity in everyone’s writing, a lack of diversity that AI can perpetuate. Yet, you only have to Google any topic and you will find many blog posts and articles that share the same view, and perhaps the same voice. Some discussions about AI resemble early 2000s conspiracy theories about cell phones. While the context has changed, the tone remains similar. The Importance of Creativity in Writing & Our Language Creativity is essential in writing. Even AI relies on human creativity. Without our input, machines would repeatedly generate the same content. Machine learning in AI is about learning from people. Our role is crucial, demonstrating the value of our unique voices. Developing a unique voice takes time and effort, which is why creatives like Kelly McKernan, Nicki Minaj, Elin Hilderbrand, and Jonathan Franzen are suing AI companies for copyright infringement. These unique voices significantly impact language evolution, and it’s vital for us to continue growing creatively. Writers play a crucial role in language evolution by creating new words or phrases that captivate readers. Over time, these innovations can enrich the language. A writer’s distinctive style can set trends, leading to significant changes in language use. This power must be used wisely. Famous writers’ narrative structures and dialogue usage can inspire others. For example, Dr. Seuss coined “nerd,” J.R.R. Tolkien introduced “tween,” Milton created “pandemonium”, novelist William Gibson first used “cyberspace”, Johnathon Swift gave us “yahoo” in Gulliver’s Travels, and Charles Dickens gave us “boredom.” The core of a good piece of writing is a great idea. With a strong core idea, the writer can easily layer the content around it. Content even can build the framework from which comes a whole new word. Content includes interesting examples to which the reader can relate. That content needs to be well-organized and clear in form so that the reader can easily see the message or find the intended meaning. In addition, the writing should have style and the right voice that matches its topic and theme while also reflecting what the author believes.  Writing Through the Centuries Writing has evolved over centuries, influencing language development. During

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April 14, 2024in Salesforce Einstein
How AI is Raising the Stakes in Phishing Attacks
13Apr

How AI is Raising the Stakes in Phishing Attacks

Cybercriminals are increasingly using advanced AI, including tools like ChatGPT, to execute highly convincing phishing campaigns that mimic legitimate communications with uncanny accuracy. As AI-powered phishing becomes more sophisticated, cybersecurity practitioners must adopt AI and machine learning defenses to stay ahead. What are AI-Powered Phishing Attacks? Phishing, a long-standing cybersecurity issue, has evolved from crude scams into refined attacks that can mimic trusted entities like Amazon, postal services, or colleagues. Leveraging social engineering, these scams trick people into clicking malicious links, downloading harmful files, or sharing sensitive information. However, AI is elevating this threat by making phishing attacks more convincing, timely, and challenging to detect. General Phishing Attacks Traditionally, phishing emails were often easy to spot due to grammatical errors or poor formatting. AI, however, eliminates these mistakes, creating messages that appear professionally written. Additionally, AI language models can gather real-time data from news and corporate sites, embedding relevant details that create urgency and heighten the attack’s credibility. AI chatbots can also generate business email compromise attacks or whaling campaigns at a massive scale, boosting both the volume and sophistication of these threats. Spear Phishing Spear phishing involves targeting specific individuals with highly customized messages based on data gathered from social media or data breaches. AI has supercharged this tactic, enabling attackers to craft convincing, personalized emails almost instantly. During a cybersecurity study, AI-generated phishing emails outperformed human-crafted ones in terms of convincing recipients to click on malicious links. With the help of large language models (LLMs), attackers can create hyper-personalized emails and even deepfake phone calls and videos. Vishing and Deepfakes Vishing, or voice phishing, is another tactic on the rise. Traditionally, attackers would impersonate someone like a company executive or trusted colleague over the phone. With AI, they can now create deepfake audio to mimic a specific person’s voice, making it even harder for victims to discern authenticity. For example, an employee may receive a voice message that sounds exactly like their CFO, urgently requesting a bank transfer. How to Defend Against AI-Driven Phishing Attacks As AI-driven phishing becomes more prevalent, organizations should adopt the following defense strategies: How AI Improves Phishing Defense AI can also bolster phishing defenses by analyzing threat patterns, personalizing training, and monitoring for suspicious activity. GenAI, for instance, can tailor training to individual users’ weaknesses, offer timely phishing simulations, and assess each person’s learning needs to enhance cybersecurity awareness. AI can also predict potential phishing trends based on data such as attack frequency across industries, geographical locations, and types of targets. These insights allow security teams to anticipate attacks and proactively adapt defenses. Preparing for AI-Enhanced Phishing Threats Businesses should evaluate their risk level and implement corresponding safeguards: AI, and particularly LLMs, are transforming phishing attacks, making them more dangerous and harder to detect. As digital footprints grow and personalized data becomes more accessible, phishing attacks will continue to evolve, including falsified voice and video messages that can trick even the most vigilant employees. By proactively integrating AI defenses, organizations can better protect against these advanced phishing threats. 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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April 13, 2024in Data, Technology
Gen AI Depends on Good Data
13Apr

Gen AI Depends on Good Data

Accelerate Your Generative AI Journey: A Call to Action for Data Leaders Generative AI is generating immense excitement across organizations, with boards of directors conducting educational workshops and senior management teams brainstorming potential use cases. They need to keep in mind, Gen AI Depends on Good Data. Individuals and departments are already experimenting with the technology to enhance productivity and effectiveness. There needs to be as much effort into data quality as to the technology. The critical work required for generative AI success falls to chief data officers (CDOs), data engineers, and knowledge curators. Unfortunately, many have yet to begin the necessary preparations. A survey in late 2023 of 334 CDOs and data leaders, sponsored by Amazon Web Services and the MIT Chief Data Officer/Information Quality Symposium, coupled with interviews, reveals a gap between enthusiasm and readiness. While there’s a shared excitement about generative AI, much work remains to get organizations ready for it. The Current State of Data Preparedness Most companies have yet to develop new data strategies or manage their data to effectively leverage generative AI. This insight outlines the survey results and suggests next steps for data readiness. Maximizing Value with Generative AI Historically, AI has worked with structured data like numbers in rows and columns. Generative AI, however, utilizes unstructured data—text, images, and video—to generate new content. This technology offers both assistance and competition for human content creators. Survey findings show that 80% of data leaders believe generative AI will transform their business environment, and 62% plan to increase spending on it. Yet, many are not yet realizing substantial economic value from generative AI. Only 6% of respondents have a generative AI application in production deployment. A significant 16% have banned employee use, though this is decreasing as companies address data privacy issues with enterprise versions of generative AI models. Focus on Core Business Areas Experiments with generative AI should target core business areas. Universal Music, for instance, is aggressively experimenting with generative AI for R&D, exploring how it can create music, write lyrics, and imitate artists’ voices while protecting intellectual property rights. Gen AI Depends on Good Data For generative AI to be truly valuable, organizations need to customize vendors’ models with their own data and prepare their data for integration. Generative AI relies on well-curated data to ensure accuracy, recency, uniqueness, and other quality attributes. Poor-quality data yields poor-quality AI responses. Data leaders in our survey cited data quality as the greatest challenge to realizing generative AI’s potential, with 46% highlighting this issue. Jeff McMillan, Chief Data, Analytics, and Innovation Officer at Morgan Stanley Wealth Management, emphasizes the importance of high-quality training content and the need to address disparate data sources for successful generative AI implementation. Current Efforts and Challenges Most data leaders have not yet made significant changes to their data strategies. While 93% agree that a data strategy is critical for generative AI, 57% have made no changes, and only 11% strongly agree their organizations have the right data foundation. Organizations making progress are focusing on specific tasks like data integration, cleaning datasets, surveying data, and curating documents for domain-specific AI models. Walid Mehanna, Group Chief Data and AI Officer at Merck Group, and Raj Nimmagadda, Chief Data Officer for R&D at Sanofi, stress the importance of robust data foundations, governance, and standards for generative AI success. Focus on High-Value Data Domains Given the monumental effort required to curate, clean, and integrate all unstructured data for generative AI, organizations should focus on specific data domains where they plan to implement the technology. The most common business areas prioritizing generative AI development include customer operations, software engineering, marketing and sales, and R&D. The Time to Start is Now While other important data projects exist, including improving transaction data and supporting traditional analytics, the preparation for generative AI should not be delayed. Despite some slow pivoting from structured to unstructured data management, and competition among CDOs, CIOs, CTOs, and chief digital officers for leadership in generative AI, the consensus is clear: generative AI is a transformative capability. Preparing a large organization’s data for AI could take several years, and the time to start is now. 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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April 13, 2024in Generative AI
Salesforce Einstein Features
13Apr

Salesforce Einstein Features

Salesforce Einstein Discover the power of the #1 AI for CRM with Einstein. Built into the Salesforce Platform, Einstein uses powerful machine learning and large language models to personalize customer interactions and make employees more productive. With Einstein powering the Customer 360, teams can accelerate time to value, predict outcomes, and automatically generate content within the flow of work. Einstein is for everyone, empowering business users, Salesforce Admins and Developers to embed AI into every experience with low code. Salesforce Einstein Features. Einstein Copilot Sales Actions: Sell faster with an AI assistant in the flow of work.Call Exploration: Ask Einstein to synthesize important call information in seconds. Ask Einstein to identify important takeaways and customer sentiment, so you have the context you need to move deals forward.

 Sales Summaries: Summarize records to identify likelihood the deal will close, the competitors involved, key activities, and more. Forecast Guidance: Ask Einstein to inform your forecast and help you identify which deals need your attention. Close Plan: Generate a customized action plan personalized to your customer and sales process. Increase conversion rates with step-by-step guidance and milestones grounded in CRM data. Salesforce Einstein Features Sales Generative AI features: ° Knowledge Creation: ° Search Answers for Agents and Customers: Einstein Copilot Service Actions: Streamline service operations by drafting Knowledge articles and surfacing answers, grounded in knowledge, to the most commonly asked questions. Summarize support interactions to save agent time and formalize institutional knowledge. Surface generated answers to agents’ & customers’ questions that are grounded in your trusted Knowledge base directly into your search page. Search Answers for Agents is included in the Einstein for Service Add-on SKU and Search Answers for Customers is included in the Einstein 1 Service Edition.
Empower agents to deliver more personalized service and reach resolutions faster with an AI assistant built into the flow of work. You can leverage out-of-the-box actions like summarize conversations or answer questions with Knowledge or you can build custom actions to fit your unique business needs. Service Salesforce Einstein Features This Release Einstein CopilotSell faster with an AI assistant. No data requirements
Included in Einstein 1 Sales Edition.hEinstein Copilot: Sales ActionsSell faster with an AI assistant.No data requirements. 
 Call explorer and meeting follow-up requires Einstein Conversation Insights.
Included in Einstein 1 Sales Edition. Generative AIBoost productivity by automating time-consuming tasks.No data requirements. 
 Call summaries and call explorer requires Einstein Conversation Insights.
Included in Einstein 1 Sales Edition. Einstein will use a global model until enough data is available for a local model. For a local model: ≥1,000 lead records created and ≥120 of those converted in the last 6 monthsEinstein Automated Contacts Automatically add new
contacts & events to your CRM≥ 30 business accounts. If you use Person Accounts, >= 50 percent of accounts must be business accounts Einstein Recommended ConnectionsGet insights about your teams network to see who knows your customers and can help out ona deal ≥ 2 users to be connected to Einstein Activity Capture
and Inbox (5 preferred) Einstein Forecasting Easily predict sales forecasts inside
of Salesforce Collaborative Forecasting enabled; use a standard fiscal year; measure forecasts by opportunity revenue; forecast hierarchy must include at least one forecasting enabled user who reports to a forecast manager; opportunities must be in Salesforce ≥ 24 months;Einstein Email Insights Prioritize your inbox with actionable intelligence Einstein Activity Capture enabledEinstein Activity Metiics (Activity 360) Get insight into the activities you enter
manually and automatically from Einstein
Activity Capture Einstein Activity Capture enabled Sales Analytics Get insights into the most common sales KPIs No data requirements. User specific requirements like browser and device apply Einstein Conveisation Insights Gain actionable insights from your sales calls with conversational intelligenceCall or video recordings from Lightning Dialer, Service Cloud Voice, Zoom and other supported CTI audio and video partners.Buyer Assistant Replace web-to-lead forms with real-time conversations. No data requirements – Sales Cloud UE or Sales Engagement. Einstein Opportunity ScoringEinstein Activity CaptuiePrioritize the opportunities most likely to convertAutomatically capture data & add to your CRMEinstein will use a global model until enough data is available for a local model. For a local model: ≥ 200 closed won and ≥ 200 closed lost opportunities in the last 2 years, each with a lifespan of at least 2 days≥ 30 accounts, contacts, or leads; Requires Gmail, Microsoft Exchange 2019, 2016, or 2013 Einstein Relationship Insights Speed prospecting with AI that researches for you. No data requirements. Einstein Next Best Action Deliver optimal recommendations at the point of maximumimpactNo data requirements. User specific requirements like browser and device apply Sales AIGenerate emails, prioritize leads & opportunities most likely to convert, uncover pipeline trends, predict sales forecasts, automate data capture, and more with Einstein for Sales. Generative AIPrompt BuilderEinstein Lead ScoringEinstein Opportunity ScoringEinstein Activity CaptureEinstein Automated ContactsEinstein Recommended ConnectionsEinstein ForecastingEinstein Email InsightsEinstein Activity Metrics (Activity 360)Sales AnalyticsEinstein Conversation InsightsBuyer Assistant Sales AIGenerative AI: 
Feature Why is it so Great? What do I need? Automate common questions and business processes to solve customer requests fasterBoost productivity by auto-generating service replies, summarizing conversations during escalations andtransfers or closed interactions, drafting knowledge articles, and surfacing relevant answers grounded inknowledge for agents’ and customers’ commonly asked questions. Deliver optimal recommendations at the point of maximum impactEliminate the guesswork with AI-powered recommendations for everyoneDecrease time spent on manual data entry for incoming cases and improve case field accuracy and completionAutomate case triage and solve customer requests fasterDecrease time spent selecting field values needed to close a case with chat conversations and improved field accuracySurface the best articles in real time to solve any customer’s questionEliminate time spent typing responses to the most common customer questionsGet insights into contact center operations, understand customers, and deliver enhanced customerexperiencesChat or Messaging channels, minimum of 20 examples for most languagesNo data requirements. User specific requirements like browser and device apply Make sure that your dataset has the minimum records to build a successful recommendation. Recipient Records need a minimum of 100 records,Recommended Item Records need a minimum of 10 records, andPositive Interaction Examples need a minimum of 400 records

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April 13, 2024in Generative AI, Salesforce Einstein, Snowflake
Government CRM System
12Apr

Government CRM System

Explore How Governments Can Modernize Services for Citizens with Government CRM System What is CRM in Government? CRM (Customer Relationship Management) systems in government streamline administrative tasks, allowing public servants to concentrate on enhancing citizens’ daily lives. Does the US Government Use Salesforce? Salesforce is valuable to the US federal government due to its highly customizable nature, catering to diverse agency needs and projects. Understanding AI in Government: Reshaping Public Sector Services Enhancing Workforce Skills for Better Constituent Experiences and Efficient Agency Operations The AI revolution presents opportunities for governments to enhance efficiency and service delivery. AI technologies can significantly improve data processing, cybersecurity, public planning, and other critical areas. Government agencies must raise awareness about the benefits of AI and upskill employees to bridge the AI skills gap. This transformation enables workers to better serve the public and foster trust between sectors. However, the rapid adoption of AI also raises concerns about a potential skills crisis, as highlighted by a survey revealing insufficient high-quality AI and machine learning resources. While AI promises to create new jobs, it may also displace certain roles. Organizations must prepare employees for this shift, ensuring they transition to higher-value work and acquire the necessary AI skills. Data Modernization: Paving the Way for an AI-Optimized Future Modernizing data infrastructure is essential for leveraging AI effectively. Employees can upskill in data science and AI, facilitating this transition from traditional workflows to AI-driven processes. Applications of AI in Government AI offers transformative potential across various government functions, such as traffic management, healthcare delivery, and administrative tasks like paperwork processing. Government agencies can enhance operations through AI-driven insights, improving efficiency, and service delivery for citizens. Challenges and Opportunities in AI Adoption Despite the promise of AI, many public agencies lack sufficient AI and data management capabilities among their workforce. Effective Education and Training for AI Implementation Organizations must prioritize AI education and responsible usage to better serve the public while upholding stringent security standards. Understanding Government Cloud Salesforce Salesforce Government Cloud and Government Cloud Plus provide dedicated instances of Salesforce’s Customer 360 suite, tailored to meet government requirements. Enhancing Government Efficiency with Modern CRM Solutions Explore How CRM Software Can Revolutionize Citizen Engagement and Government Operations CRM systems empower local governments to establish meaningful connections with citizens, improving service delivery and operational efficiency. Key Features of Local Government CRM Software Discover essential CRM features for local government agencies, including workflow automations, communication tools, data security, citizen contact management, real-time analytics, and business intelligence reporting. Evaluating CRM Data-Quality Solutions Evaluate CRM solutions based on security, flexibility, scalability, interoperability, ease of use, and customization capabilities to enhance government operations effectively. Strategies for Implementing CRM Workflows in Government Implement CRM systems strategically to improve service delivery and constituent engagement, focusing on data integration and minimizing the need for complex coding during deployment. By embracing modern CRM technologies and AI solutions, governments can enhance efficiency, transparency, and citizen satisfaction, ushering in a new era of effective public service delivery. Government CRM System. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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April 12, 2024in Mulesoft, Salesforce Experience Cloud, Salesforce Public Sector Solutions
Which Industries Use Salesforce
11Apr

A charity auction to have lunch with Salesforce CEO Marc Benioff to launch in early May

A charity auction to have lunch with Salesforce CEO Marc Benioff launches in May carrying on a long tradition. The lunch featured Berkshire Hathaway’s Warren Buffett for many years. Glide will field bids for the “Power of One Charity Lunch Auction” with Benioff from May 5-10, according to a Tuesday press release put out by eBay on the nonprofit’s behalf. It is working with the online marketplace to conduct the auction, like it has for many years. Charity auction to have lunch with Salesforce CEO Marc Benioff “I’m humbled to continue my friend @WarrenBuffett’s legacy w/ the Power of One Charity Lunch,” the billionaire Salesforce CEO said on X. “Thank you to @GLIDEsf for their great work to support our amazing city of SF. And I’ll always be grateful for the passion of Susie Buffett and leadership of Warren & Reverend Cecil Williams.” EBay and the nonprofit have set bidding at $25,000 to start. Whoever winds up winning will get a “memorable lunch for eight” with Benioff, the press release said. Not the first charitable thing Benioff has made a large donation to in 2024. Earlier this year Fox Business reported a large donation to hospitals in Hawaii. He and his wife have promised to give away at least 50% of their wealth to charitable causes as signatories of the Giving Pledge. They signed onto that initiative, which Bill Gates, Melinda French Gates and Warren Buffett set up about eight years ago. Glide had collaborated with Warren Buffett, the longtime CEO of Berkshire Hathaway and a billionaire investor, on the auctions for over two decades. The origins of the lunch trace back to his late wife, Susie. $25,000 charity auction to have lunch with Salesforce CEO Marc Benioff launches in May. The more than two decades of lunches with Buffett helped the non-profit bring in $53 million worth of funds for its “transformative programs and services that lift people out of poverty, hunger, and homelessness, and advance equity through systems change,” according to the press release. The total includes the “record-breaking” $19 million from 2022’s lunch auction. That year marked the last one Buffett planned to do, as FOX Business reported at the time. Buffett, who turned 93 in late August, said the lunch “is in the right hands” with Benioff.  “He is going to do a wonderful job improving on what I did over the years,” the “Oracle of Omaha” said. “With Marc’s enthusiasm and commitment, along with GLIDE’s leadership and volunteers, GLIDE will be able to continue providing its vital services for San Francisco.” Both men have been active when it comes to philanthropy-related efforts over the years. Buffett has disbursed tens of billions in charitable contributions to the Bill & Melinda Gates Foundation and a few nonprofits linked to Buffett’s family. Meanwhile, Benioff has made donations for various causes, including the environment, homelessness and health care. Salesforce has provided more than $10 million in philanthropic giving to refugee-serving organizations worldwide. The Salesforce Foundation also makes considerable contributions. What does the Salesforce Foundation look for in a potential grantee partner? The Salesforce Foundation looks at the following factors when sourcing and shaping new strategic grant partnerships: alignment with Salesforce’s Education grantmaking strategy; nonprofit status, demonstrated program impact or strong evidence-informed models; commitment to measure, learn, and adapt; and strong, diverse leadership team reflective of the community and connected to the issues they are addressing. So, if you’d like to chat with Mark Benioff of Salesforce, like to see your money going somewhere meaningful, and are intrigued, mark your calendars for the May auction. Charity auction to have lunch with Salesforce CEO Marc Benioff Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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April 11, 2024in Salesforce
Predictive Lead Scoring
11Apr

Predictive Lead Scoring

Traditional lead scoring relies on predefined criteria and subjective assumptions, whereas predictive lead scoring (PLS) harnesses machine learning algorithms to analyze extensive data and identify key predictors of lead quality.  Traditional lead scoring only learns from data if you revise your scoring methodology for it.  Predictive lead scoring constantly reworks the machine learning model based on more and newer data. Traditional lead scoring can be impacted by human error and bias. PLS analyzes from historical data eliminating bias and error. PLS employs a machine learning model to assign scores to open leads based on historical data, enabling sales teams to prioritize effectively and improve lead qualification rates while reducing the time spent on lead qualification. Discover how AI can elevate PLS to new heights and transform various organizational functions amidst shrinking budgets and heightened performance expectations across sales and marketing teams. Key Benefits of Predictive Lead Scoring: PLS leverages data science and machine learning to analyze and predict future outcomes based on historical and current data, guiding businesses in identifying high-potential leads and optimizing resource allocation. Implementing Predictive Lead Scoring: AI CRM and PLS: AI-enabled CRM platforms like Salesforce’s Einstein Lead Scoring automate lead scoring processes, leveraging extensive data to predict lead quality and prioritize effectively for sales and marketing teams. Benefits of Predictive Lead Scoring: AI and Machine Learning in Lead Scoring: AI and machine learning enhance lead scoring by analyzing vast data sets, identifying patterns, and predicting behaviors for more accurate lead qualification and prioritization.  A data-driven enterprise is a smarter enterprise acting on data and insights. Salesforce’s Intelligent Lead Scoring: Salesforce’s Einstein Lead Scoring automates lead scoring processes within Sales Cloud and Marketing Cloud, providing tailored metrics and insights for informed decision-making. Generative AI and Predictive Lead Scoring: Generative AI streamlines processes like email personalization and content creation, enhancing marketing effectiveness and productivity. Good PLS with AI and machine learning transforms lead management by leveraging data insights for efficient and accurate lead qualification, ultimately driving improved sales and marketing 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 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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April 11, 2024in Einstein Insights
Salesforce Summer '24 Sandbox Preview
10Apr

Salesforce Summer ’24 Sandbox Preview Announced

The Salesforce Summer ’24 release is coming soon. Salesforce Summer ’24 Sandbox Preview announced this week. Use your sandbox to get early access to new features and test your configurations before the production upgrade. Here’s how: If you still have questions about your sandbox options for Summer ’24, open a support case via Salesforce Help. Salesforce Summer ’24 Sandbox If you have questions about how Salesforce Summer ’24 Sandbox Preview may impact your Salesforce org, stay tuned to Tectonic’s Insights or contact Tectonic today. Summer ’24 is coming with lots of new features. Use your sandbox to get early access to new features and test your configurations before the production upgrade.  The sandbox preview window for Summer ’24 begins May 10, 2024. You must have an active sandbox on a preview instance by May 9, 2024 (the day before sandbox preview) to take advantage of the preview.  Here’s how… First, decide whether you want to: After you decide:  Usually, no action is required because your sandbox is already on a preview instance.   How Does Sandbox Preview Work? The sandbox preview is a 6-week window ahead of a major Salesforce release when all preview sandboxes are upgraded to the next major Salesforce release. It’s your first chance to test your configurations on the new release.  Sandboxes are updated in groups based on the instance where they’re located. All sandboxes on a preview instance get upgraded together, and all sandboxes on non-preview instances remain on the current release. The only way to change the upgrade status of a sandbox is to move it to another instance by refreshing it. If you want to move it to a preview instance, you must do so before the cutoff date.  Between now and May 9, 2024, we route all sandbox requests to preview instances. If your sandbox request is complete in time, we upgrade it to Summer ’24. On May 10, 2024, we reset all incomplete sandbox requests to a non-preview instance.  All pending and new sandbox requests are routed to non-preview instances until your production instance is upgraded to Summer ’24. We can create sandboxes only on the same major release version of Salesforce as production.   For the Summer ’24 Release, we upgrade the preview instances to Summer ’24 on May 10 and May 11, 2024, and non-preview instances to Summer ’24 on June 14 and June 15, 2024. Follow the basic process below if you want your sandbox to participate in the Summer ’24 preview.  1. Find your sandbox details.In production, from Setup, in the Quick Find box, enter Sandbox, and then select Sandbox. Use the Release Type column to determine if your sandbox is on a preview or non-preview instance. You can also use the Sandbox Preview Guide to determine if each of your sandboxes is on a preview or non-preview instance. Enter your sandbox instance name(s). The guide tells you if it’s preview or non-preview and what to do to either move to the next release (Summer ’24) or stay on the current release (Spring ’24). You can also check multiple sandboxes and view the full list of preview and non-preview instances. 2. Understand the cutoff date. Every release, we give you a cutoff date for sandbox refresh completions, and usually, it’s the day before the preview starts. For Summer ’24, the cutoff date is May 9, 2024. Any sandbox created after the cutoff isn’t a preview sandbox. If you have a sandbox on a preview instance and refresh it after the cutoff, your sandbox is routed to a non-preview instance and reverts to Spring ’24. Resolution Should I Refresh my Sandbox? Here are the most common scenarios: It’s important to plan ahead. The popularity of the sandbox preview program often means backlogs in sandbox create and refresh requests, especially closer to the cutoff date.  Remember, if it’s a full sandbox it can take some time to copy. All incomplete sandboxes are reset to a non-preview instance before the sandbox preview starts on May 10, 2024, so we recommend that you request your sandbox at least a week before the deadline to improve your chances of getting a completed sandbox on a preview instance.  We aim to process each request on time, but we can’t guarantee the completion of your sandbox before the deadline.  I Want to Create a New Sandbox — What Are My Options? Create a new sandbox that goes to a preview instance and gets the Summer ’24 preview Create a new sandbox that goes to a non-preview instance and stays on Spring ’24 Submit your sandbox request well ahead of the 6:00 PM PT cutoff on May 9, 2024 (01:00 UTC on May 10, 2024) so that the sandbox copy is completed before the sandbox release.  Plan ahead. If you submit your request too close to the deadline and the copy isn’t completed, or if your request is after the deadline, your Sandbox is built on a non-preview instance. Wait to submit your request to create a sandbox until after 6:00 PM PT on May 9, 2024 (01:00 UTC on May 10, 2024). If you submit your request before then, your Sandbox can be built on a Preview Instance. I Have a Sandbox — What Are My Options? If the Release Type of your sandbox is: You want to try Summer ’24 in your sandbox You want the sandbox on Spring ’24 Preview No action needed. Your sandbox is already on a preview instance and gets upgraded to Summer ’24 on May 10, 2024. If you must refresh your sandbox for other reasons, do so well in advance of 6:00 PM PT on May 9, 2024 (01:00 UTC on May 10, 2024) to make sure that your sandbox is completed before the cutoff date.If you refresh your sandbox after the deadline or your sandbox isn’t completed in time, it is built on a non-preview instance. Refresh to a non-preview instance after the cutoff date to keep your sandbox on Spring ’24.To do so, wait to refresh after 6:00 PM PT on May 9, 2024 (01:00

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April 10, 2024in Salesforce

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