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Jobs AI Could Take
08Jan

Jobs AI Could Take

AI’s Impact on Creative Professions AI is poised to replace designers, writers, illustrators, filmmakers, and many other professional roles within the creative services industry. This transition may occur rapidly or gradually, but the inevitability remains. Disregard the notion that AI cannot perform deep-feeling work due to its lack of emotions. Similarly, dismiss the idea that the value of human-crafted work will surge as AI becomes more prevalent. Jobs AI Could Take and jobs it can’t. There are a few artisans, like hand-dipped candle makers, who still charge good prices for a handmade product. And there are many companies that mass produce candles far cheaper. However, the artisians are a small fraction compared to the thousands of craftsmen who existed before the advent of factories. Once AI has full access to publisher archives and the gamut of human emotions documented therein, it will be capable of replicating feelings just as easily. Historically, artisans displaced by machines have faced similar fears. Earning a living as a creative has always been challenging, and this transition will not be any different. Let’s face it. Each of the four Industrial Revolutions have caused fears, displaced workers, and created new jobs. Interestingly, the first three were each about a century apart. And the fourth only 4 short decades. Adapting to AI in Creative Roles Creatives will still possess their passions and skills, but the key question is how to make a living. Embracing AI and integrating it into your workflow is crucial. AI will transform the creative process. Your painting or drawing or creating may not continue to be your full time income, but it will become more valuable as less and less people are doing it. For instance, clients are using AI to expedite writing and strategy development, create packaging designs, and generate variations. In filmmaking, AI assists with storyboarding and editing. Even illustrators are using AI for initial sketches, with a foreseeable shift to using it for final art. Combining roles like writing and design with AI support is becoming more common. AI could enable writers to become proficient in design and illustration, and vice versa. Filmmakers may find new ways to expand their roles, potentially making existing categories less distinct. Current AI Developments in Creative Fields AI is already driving business decisions, with corporations reducing costs by replacing human roles. Here are some current trends: Emphasizing Human Connections Despite AI’s rise, human interaction remains irreplaceable. Profits depend on people making purchases, and corporate employees need trusted individuals to help them sell products and services. Maintaining personal relationships is crucial for success in a world increasingly influenced by AI. Focusing on real, person-to-person connections is essential. Building and nurturing relationships with fellow professionals and clients can provide lasting benefits. Engage in genuine interactions, listen actively, and develop strong interpersonal skills. Sharing stories and demonstrating vulnerability and empathy can strengthen these connections. Leveraging Personal Strengths and Jobs AI Could Take Continue honing your craft and keeping your community informed about your work. Focus on clients who value your expertise and are likely to invest in your services. Opportunities may lie with corporations or wealthy individuals who require your unique skills. Exploring alternative income sources beyond the corporate world can also be beneficial. Some creatives are transitioning to selling their work to communities with shared interests. Ultimately, your unique talents and personal touch are irreplaceable. By embracing AI while maintaining strong human connections, you can navigate the changing landscape and continue to thrive in your creative career. Jobs AI Could Take and why that might not be a terrible thing. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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January 8, 2024in Salesforce Einstein
Salesforce Einstein AI Trust Layer
08Jan

Einstein AI Trust Layer Explained

The Einstein Trust Layer is a secure AI architecture. It is natively built into the Salesforce Platform. Designed for enterprise security standards the Einstein Trust Layer continues to allow teams to benefit from generative AI. Without compromising their customer data, while at the same time letting companies use their trusted data to improve generative AI responses: Trusted AI starts with securely grounded prompts. A prompt is a canvas to provide detailed context and instructions to Large Language Models. The Einstein Trust layer allows you to responsibly ground all of your prompts in customer data and mask that data when the prompt is shared with Large Language Models*. With our Zero Retention architecture, none of your data is stored outside of Salesforce. Salesforce gives customers control over the use of their data for AI. Whether using our own Salesforce-hosted models or external models that are part of our Shared Trust Boundary, like OpenAI, no context is stored. The large language model forgets both the prompt and the output as soon as the output is processed. Like Related Posts Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more Public Sector Salesforce Solutions Public Sector Solutions revolutionize public service delivery through flexible and secure e-government tools supporting both service providers and constituents. Designing Read more

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January 8, 2024in Salesforce Einstein
AI's Impact on the Workforce
07Jan

AI’s Impact on the Workforce

According to McKinsey, generative AI has the potential to contribute between $2.6 trillion and $4.4 trillion in value to the global economy across various industries, spanning banking, retail, high tech, healthcare, and life sciences. Its impact is expected to reach diverse professions, including customer operations, marketing and sales, software engineering, and research and development. The influence of AI on the workforce is significant. A report by Goldman Sachs suggests that AI could replace the equivalent of 300 million full-time jobs, affecting a quarter of work tasks in the US and Europe. However, it also brings forth new job opportunities and a productivity boom. Despite concerns about job displacement, AI is anticipated to generate numerous new opportunities. Roles like prompt engineer and AI product manager are emerging, with a Salesforce-sponsored IDC white paper predicting a surge in demand for positions such as data architects, AI ethicists, and AI solutions architects over the next 12 months. The report also forecasts the creation of 11.6 million new jobs within the Salesforce ecosystem alone over the next six years. Recent advancements in generative AI, exemplified by products like ChatGPT with 100 million monthly active users in two months, have reignited discussions about automation’s impact on jobs. While the extent of disruption remains unknown, developers, users, and policymakers should consider its effects on workers. To address challenges and opportunities, Majority Leader Chuck Schumer has launched a SAFE Innovation Framework, emphasizing worker security. The Biden administration is developing a National AI Strategy to address economic and job impacts. For individuals in the workforce, there’s an opportunity to cultivate existing skills and acquire new ones through platforms like Salesforce’s Trailhead, Coursera, and LinkedIn. AI’s impact on jobs involves eliminating repetitive tasks, allowing individuals to focus on more strategic and creative aspects of their roles. In fields like sales, customer service, marketing, healthcare, finance, and graphic design, AI will transform roles and create new opportunities. Chris Poole, AI Technical Consulting Lead in Salesforce’s global AI practice, envisions AI becoming ingrained in every aspect of our lives, contributing to fascinating evolution across various fields. The scale of AI adoption’s impact on workers, especially with generative AI tools, remains uncertain. Potential effects include replacing, complementing, or freeing workers for more productive tasks, or creating new jobs. A Goldman Sachs estimate suggests that about two-thirds of current jobs are exposed to some degree of AI automation, with generative AI potentially substituting up to one-fourth of current work. McKinsey Global Institute estimates that 29.5 percent of all hours worked could be automated by 2030. Regarding job impact, professional occupations associated with clerical work in finance, law, and business management are most exposed to AI. However, AI is also concurrently creating many new jobs. Like Related Posts Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more Salesforce Data Studio Data Studio Overview Salesforce Data Studio is Salesforce’s premier solution for audience discovery, data acquisition, and data provisioning, offering access Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more

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January 7, 2024in Salesforce Einstein
Salesforce Einstein Commerce
03Jan

Salesforce Einstein Commerce

Elevate Your Commerce Store Experience with Commerce Einstein Transform your commerce store with the advanced capabilities of Commerce Einstein, which includes features like Goals and Recommendations, the Commerce Concierge bot, Smart Promotions, and SEO-optimized meta tags. Salesforce Einstein Commerce, Enhance Store Performance with Goals and Recommendations Achieve key performance objectives for your store such as increased site conversion, higher site traffic, and greater average order value. Utilize an intuitive framework powered by AI recommendations to implement intelligent actions quickly and efficiently, facilitating the setup and growth of your store. Track progress with insights from Data Cloud. This feature is available for B2B Commerce and D2C Commerce in the Enterprise, Unlimited, and Developer editions. Access it through the Goals and Recommendations option in the Commerce App Navigation menu. Optimize Shopping Experience with the Commerce Concierge Bot Enhance the shopping experience by providing conversational product recommendations and reordering capabilities with the Commerce Concierge bot. Build a new bot using the Commerce Concierge template to connect your store to a new Einstein bot, or upgrade an existing bot with new Commerce Concierge bot blocks. This allows customers to authenticate, manage multiple accounts, reorder, and utilize Einstein’s generative AI features. Craft Intelligent Promotions with Einstein Create promotions effortlessly using Einstein and reliable data from Commerce Cloud. Employ natural language instructions and generative AI to quickly generate both simple and advanced promotions, making your promotional efforts smarter and more effective. Enhance SEO with Einstein Meta Tags Boost your SEO performance by using Einstein’s generative AI to create Page Title Tags and Page Meta Descriptions for products. This enriches search engines with relevant information, improving your store’s visibility. Alternatively, you can manually create and manage page meta tags through the SEO tab on a product record. Reduce Return Rates with Einstein Return Insights Analyze return reasons to refine product listings and minimize return rates. Einstein helps you identify up to 20 high return-rate products and provides analyzed and categorized return reasons, enabling you to make strategic improvements. Facilitate Product Discovery with AI-Powered Search (Generally Available) Improve your customers’ product discovery experience with Einstein semantic search. Using natural language processing, this feature interprets queries to deliver relevant results, accommodating synonyms, alternative spellings, typos, and more. For example, it matches terms like “couch” and “sofa” or “jumper” and “sweater,” aligning with the searcher’s intent. Enhance your commerce store with these innovative features from Commerce Einstein to drive growth, improve customer experience, and optimize operational efficiency. 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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January 3, 2024in Generative AI, Salesforce, Salesforce Einstein
Cloud Based Business Solutions
02Jan

What Can’t Generative AI Do?

There is a tremendous amount of discussion about all the capabilities of generative AI, but once in a while it doesn’t hurt to look at the other side of the AI coin. The USC Library published a piece in October of 2023 that did just that. In addition to the identified limitations discussed below, generative AI may be susceptible to issues that have yet to be uncovered or fully grasped. Clearly we learn new things about it every day. What Can’t Generative AI do? Large language models (LLMs) are susceptible to “hallucinations,” producing fictional information presented as factual or accurate. This includes citations, publications, biographical details, and other data commonly used in research and academic papers. Furthermore, answers generated by LLMs may be incorrect, often presented as correct or authoritative. ChatGPT has been known to “make things up” when it’s last data load didn’t cover the time frame asked to generate content about. The fundamental structure of generative AI models, coupled with frequent updates, makes content reproduction challenging. This poses a significant challenge in research and academia, where reproducibility is crucial for establishing credibility. Generative AI models don’t function as databases of knowledge. Instead, they attempt to synthesize and replicate the information they were trained on. This complexity makes it exceptionally difficult to validate and properly attribute the sources of their content. Generative AI models have no reason to believe any information it has is inaccurate. When asked to tell me how the sky was purple, ChatGPT explained both why the daytime sky is normally blue and reasons from volcanic ash to pollution that it might “appear” purple. But when asked who owns Twitter, ChatGPT refers to it as a publicly traded entity as it’s last data load was prior to Elon Musk purchasing and renaming Twitter to X. Is the Data Up to Date? Many common generative AI tools lack internet connectivity and cannot update or verify the content they generate. Additionally, the nature of generative AI models, especially when provided with simple prompts, can lead to content that is overly simplistic, of low quality, or overly generic. When asked for the weather forecast, Chat GPT replied, “I’m sorry, but I don’t have the capability to provide real-time information, including current weather forecasts. Weather conditions can change rapidly, and it’s important to get the most up-to-date information from a reliable source.” Several generative AI models, including ChatGPT, are trained on data with cutoff dates. Thus resulting in outdated information or an inability to provide answers about current events. In some instances, the data cutoff date may not be explicitly communicated to the user. The capabilities of generative AI are obviously limited by outdated data. Data Privacy Precautions: Exercise extra caution when dealing with private, sensitive, or identifiable information, whether directly or indirectly, regardless of using a generative AI service or hosting your own model. While some generative AI tools permit users to set their data retention policies, many collect user prompts and data, presumably for training purposes. USC researchers, staff, and faculty should particularly avoid sharing student information (a potential FERPA violation), proprietary data, or other controlled/regulated information. Salesforce recognizes this. According to their news and insights page, companies are actively embracing generative AI to power business growth. Building trustworthy generative AI requires a firm foundation at the inception of AI development. Salesforce published an overview of their five guidelines for the ethical development of generative AI that builds on their Trusted AI Principles and AI Acceptable Use Policy. The guidelines focus on accuracy, safety, transparency, empowerment, and sustainability – helping Salesforce AI engineers create ethical generative AI from the start. Additional Considerations: Apart from providing direct access to generative AI tools, many companies are integrating generative AI functionality into existing products and application. Tools such as Google Workspace, Microsoft Office, Notion, and Adobe Photoshop to name a few. Extra care should be taken when using these tools for research and academic work. Be careful especially to avoid the use of auto-completion for sentences or generating text without explicit permission. When working with images or videos, clearly communicate and attribute the use of generative AI assistance. Detecting Generative AI: In an effort to counter undisclosed and inappropriate uses of generative AI content, many organizations are developing generative AI detectors. These tools use AI to flag content created by generative AI. However, these tools can be unreliable and have erroneously flagged student content as AI-generated when it was created by a human. Relying solely on these tools to identify the origin of an assignment or work is not advisable. I played with one such tool using content solely written by generative AI. Amazingly it received a 99% human generated score. I rewrote the content in my own words and the score dropped by 20%. In April 2023, Turnitin introduced a preview of their AI detection tool, available to USC instructors via the Turnitin Feedback Studio. When in doubt, professors should engage with their students to better understand if and how generative AI tools were used. This interaction provides an essential opportunity for both parties to discuss the nuances of the technology. Thereby they can address any questions or concerns. Determining how and when the capabilities of generative AI is useful for you, is not ever going to be a cut and dry process. By Shannan Hearne, Tectonic Salesforce Marketing Consultant Like1 Related Posts Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more Salesforce Data Studio Data Studio Overview Salesforce Data Studio is Salesforce’s premier solution for audience discovery, data acquisition, and data provisioning, offering access Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of

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January 2, 2024in Salesforce Einstein
Jan '24 Einstein Data Cloud Updates
30Dec

January ’24 Einstein Data Cloud Updates

Utilize Generative AI to Target Audiences Effectively Harness the power of generative AI with Einstein Segment Creation in Data Cloud to create precise audience segments. Describe your target audience, and Einstein Segment Creation swiftly produces a segment using trusted customer data available in Data Cloud. This segment can be easily edited and fine-tuned as necessary. Jan ’24 Einstein Data Cloud Updates. Where: This enhancement is applicable to Data Cloud in Developer, Enterprise, Performance, and Unlimited editions. Einstein generative AI is accessible in Lightning Experience. When: This functionality is rolling out gradually, starting in Spring ’24. How: In Data Cloud, create a new segment and choose Einstein Segment Creation. In the Einstein panel, input a description of your segment using simple text, review the draft, and make adjustments as needed. Gain Insights into Segment Performance with Segment Intelligence Analyze segment data efficiently with Segment Intelligence, an in-platform intelligence tool for Data Cloud for Marketing. Offering a straightforward setup process, out-of-the-box data connectors, and pre-built visualizations, Segment Intelligence aids in optimizing segments and activations across various channels, including Marketing Cloud Engagement, Google Ads, Meta Ads, and Commerce Cloud. Where: This update applies to Data Cloud in Developer, Enterprise, Performance, and Unlimited editions. Utilizing Segment Intelligence requires a Data Cloud Starter license. When: For details regarding timing and eligibility, contact your Salesforce account executive. How: To configure Segment Intelligence, navigate to Salesforce Setup. To view Segment Intelligence dashboards, go to Data Cloud and select the Segment Intelligence tab. Activate Audiences on Google DV360 and LinkedIn Effortlessly activate audiences on Google DV360 and LinkedIn as native activation destinations in Data Cloud. Directly use segments for targeted advertising campaigns and insights reporting. Where: This change is applicable to Data Cloud in Developer, Enterprise, Performance, and Unlimited editions. Requires an Ad Audiences license. When: This functionality is available starting in March 2024. Enhance Identity Resolution with More Frequent Ruleset Processing Experience more timely ruleset processing as rulesets now run automatically whenever your data changes. This improvement eliminates the need to wait for a daily ruleset run, ensuring efficient and cost-effective processing. Where: This update applies to Data Cloud in Developer, Enterprise, Performance, and Unlimited editions. Refine Identity Resolution Match Rules with Fuzzy Matching Extend the use of fuzzy matching to more fields, allowing fuzzy matching on any text field in your identity resolution match rules. Up to two fuzzy match fields, other than first name, can be used in a match rule, with a total of six fuzzy match fields in any ruleset. Enhance match rules by updating to the “Fuzzy Precision – High” method for fields like last name, city, and account. Where: This enhancement applies to Data Cloud in Developer, Enterprise, Performance, and Unlimited editions. Salesforce Einstein’s AI Capabilities Salesforce Einstein stands out as a comprehensive AI solution for CRM. Notable features include being data-ready, eliminating the need for data preparation or model management. Simply input data into Salesforce, and Einstein seamlessly operates. Additionally, Salesforce introduces the Data Cloud, formerly known as Genie, as a significant AI-powered product. This platform, combining Data Cloud and AI in Einstein 1, empowers users to manage unstructured data efficiently. The introduction of the Data Cloud Vector Database allows for the storage and retrieval of unstructured data, enabling Einstein Copilot to search and interpret vast amounts of information. Salesforce also unveils Einstein Copilot Search, currently in closed beta, enhancing AI search capabilities to respond to complex queries from users. Jan ’24 Einstein Data Cloud Updates This groundbreaking offering addresses the challenge of managing unstructured data, a substantial portion of business data, and complements it with the capability to use familiar automation tools such as Flow and Apex to monitor and trigger workflows based on changes in this data. Overall, Salesforce aims to revolutionize how organizations handle unstructured data with these innovative additions to the Data Cloud. Like2 Related Posts Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more CRM Cloud Salesforce What is a CRM Cloud Salesforce? Salesforce Service Cloud is a customer relationship management (CRM) platform for Salesforce clients to Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more

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December 30, 2023in Einstein 1 Platform, Einstein Insights, Salesforce Data Cloud, Salesforce Einstein
Salesforce Einstein Copilot
28Dec

What’s Included in Einstein Copilot Studio?

Christmas came early this year with Salesforce’s announcement of Einstein Copilot Studio. Einstein Copilot Studio will encompass the following features: By Tectonic’s Salesforce Marketing Consultant, Shannan Hearne Like1 Related Posts Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more Sales Cloud Einstein Forecasting Salesforce, the global leader in CRM, recently unveiled the next generation of Sales Cloud Einstein, Sales Cloud Einstein Forecasting, incorporating Read more

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December 28, 2023in Einstein 1 Platform, Salesforce Einstein
Salesforce Flow
24Dec

Einstein Flow and Generative AI

By Shannan Hearne, Tectonic Marketing Consultant Based on Nov 2023 Salesforce post from Cesar Castro As part of the ongoing evolution of Salesforce’s Einstein AI, Salesforce provided an update on how Einstein is impacting one of your favorite tools in your Salesforce admin toolkit: Salesforce Flow. Flow, with its numerous powerful capabilities, has been instrumental in helping Salesforce Admins streamline and automate their work, delivering value to all end users in their organizations.  Often without the need for any additional Salesforce development assistance! Einstein Flow and Generative AI are powering a whole new brand of Salesforce actions. Salesforce Flow is a blanket term for everything in the Salesforce ecosystem that allows you to create, manage, and run automation with clicks not code.  Those of you who are using Flow on the regular?  You are known as Flownatics! Working With Einstein Flow and Generative AI In preparation for Einstein for Flow, content was collaborated on with Ajaay Ravi, Einstein for Flow Product Manager, Cesar Castro, Product Manager for Einstein Flow, and Vera Vetter, Director of Product Management, AI Research. Together, as product managers, they are actively working on enhancing Einstein for Flow. As Tectonic’s Salesforce Marketing Consultant I am very excited to share the news with you! Generative AI Einstein for Flow is a generative artificial intelligence (AI) tool that utilizes large language models (LLMs). To drive process automation across multiple Salesforce products. Whether you are a new Flow user or an experienced admin, Einstein for Flow aims to assist in learning and adopting this capability. By aiding in the creation of more complex flows and time saving automations.  Many of Salesforce Flow’s capabilities allow you to do what used to require Apex developers. The powerhouse behind Einstein for Flow is CodeGen, Salesforce’s in-house LLM released by Salesforce AI Research in 2022 to transform software development. CodeGen is the driving force behind the tailored AI solution for the needs of Salesforce users. So, how does Einstein for Flow operate? It’s as straightforward as describing your flow requirement in plain language through a natural language prompt. Einstein then invokes code to interpret and generate the natural flow data. To handle intricate flows, a concept called a “chain of thought” is introduced. By breaking down the flow into manageable steps, sequentially creating each part, and merging them to produce the final flow. In tis way Einstein is ensuring accuracy in meeting your business automation needs. Looking ahead, Salesforce’s roadmap for Einstein for Flow includes exciting features. One notable feature is the ability to use Einstein to edit an existing flow. This is irrespective of whether Einstein created it. Salesforce’s goal is to make flow building more accessible by enabling editing of both new and existing flows. In upcoming releases, an Einstein assistive interface will be integrated. Thereby allowing you to open and edit flows by conversing with Einstein in natural language. For all you Star Trek fans out there, the age of the Enterprise doing your verbal bidding is upon us! Einstein Suggestions Einstein will suggest changes and present a list of interconnected updates resulting from the proposed modifications. Designed to be user-friendly yet advanced, you can inspect recommended changes. Then review step by step, and view individual modifications. Additionally, a history of all changes suggested and made by Einstein will be available for those interested in maintaining an audit trail of AI-driven alterations. This is just the beginning for Einstein for Flow. As Salesforce launched a pilot program with diverse customers, they plan to go general availability (GA) in Spring ’24, with capabilities to generate flows for both standard and custom objects. Customer feedback will play a crucial role in shaping their future roadmap as they continue to explore more use cases and capabilities for Einstein for Flow. Flows are such a powerful tool, they are like visual coding created with clicks. Unlike code requiring only an understanding of programming concepts and logic. Examples of Flow Automation Use Cases: Stay tuned for updates as we ride the Einstein for Flow wave. It’s a game-changer! Tectonic will be watching and implementing with Einstein Flow and look forward to helping you do the same. Like2 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 Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more

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December 24, 2023in Salesforce Einstein, Salesforce Implementation Services, Salesforce Managed Services
The Promise of AI in Health Outcomes
16Dec

The Promise of AI in Health Outcomes

As President Biden has highlighted, artificial intelligence (AI) holds tremendous promise and potential peril. This is especially true in healthcare. On October 30, the President underscored his commitment by signing a landmark Executive Order aimed at governing AI development and use to improve health outcomes for Americans while safeguarding their security and privacy. The Biden-Harris Administration is leveraging every tool at its disposal to advance responsible AI in healthcare. However, U.S. government action alone cannot achieve the bold vision laid out by the President. By integrating AI into their platform, Salesforce aims to empower public health organizations with actionable insights and predictive analytics. From disease surveillance to population health management, AI-driven solutions have the potential to revolutionize how we approach public health initiatives. Therefore, policy priorities include managing and measuring the environmental impacts of AI by requiring emissions disclosures, adding environmental impact as a risk factor, and establishing efficiency standards for high-risk AI systems. In response to the Administration’s leadership, leading healthcare providers and payers have announced voluntary commitments to the safe, secure, and trustworthy use of AI in healthcare. These commitments build on ongoing efforts by the Department of Health and Human Services (HHS), the AI Executive Order, and earlier commitments from 15 leading AI companies to develop models responsibly. Today, 28 providers and payers have joined these commitments, including Allina Health, Bassett Healthcare Network, Boston Children’s Hospital, Curai Health, CVS Health, Devoted Health, Duke Health, Emory Healthcare, Endeavor Health, Fairview Health Systems, Geisinger, Hackensack Meridian, HealthFirst (Florida), Houston Methodist, John Muir Health, Keck Medicine, Main Line Health, Mass General Brigham, Medical University of South Carolina Health, Oscar, OSF HealthCare, Premera Blue Cross, Rush University System for Health, Sanford Health, Tufts Medicine, UC San Diego Health, UC Davis Health, and WellSpan Health. The commitments align with the “FAVES” principles—Fair, Appropriate, Valid, Effective, and Safe. Under these principles, companies commit to informing users when they receive content that is largely AI-generated and not reviewed by humans. They will adhere to a risk management framework to monitor and address potential harms of AI applications. Additionally, they pledge to develop AI solutions responsibly, advancing health equity, expanding access to care, making care affordable, improving care coordination, reducing clinician burnout, and enhancing patient experiences. Healthcare is an essential service, and quality care can be a matter of life and death. AI-enabled tools used for clinical decisions must undergo appropriate testing, risk mitigations, and human oversight to avoid costly or dangerous errors. AI diagnoses can be biased if not trained on diverse data, and AI’s data-collection capabilities could create privacy risks. Addressing these risks is crucial. Despite these risks, AI holds enormous potential to benefit patients, doctors, and hospital staff. AI can help doctors deliver higher-quality, more empathetic care and cut healthcare costs by hundreds of billions of dollars annually. It can also help patients make more informed health choices by better understanding their conditions and needs. Consider some examples: Each year, hospitals produce 3.6 billion medical images worldwide. AI helps doctors analyze images more quickly and effectively, detecting signs of breast cancer, lung nodules, and other conditions earlier than ever before. AI is also streamlining drug development, matching drug targets with new molecules faster and cheaper, translating to better care for patients. Additionally, new generative AI applications can alleviate clinician burnout by automating data extraction, form population, note recording, and patient communications. The Promise of AI in Health Outcomes To understand AI applications and the necessary risk-mitigation measures, the Biden-Harris Administration has engaged with healthcare providers, payers, academia, civil society, and other stakeholders. These engagements have informed the Administration’s approach, including the President’s October AI Executive Order, which tasks HHS with a wide range of actions to advance safe, secure, and trustworthy AI. These actions include developing frameworks, policies, and potential regulations for responsible AI deployment, documenting AI-related safety incidents, prioritizing grants for innovation in underserved communities, and ensuring compliance with nondiscrimination laws in AI deployment in healthcare. The private-sector commitments announced today are a critical step in our whole-of-society effort to advance AI for the health and well-being of Americans. These 28 providers and payers have stepped up, and we hope more will join these commitments in the coming weeks. The Promise of AI in Health Outcomes has been addressed by governments everywhere. In March 2024, Salesforce strengthened its AI commitment to healthcare. Salesforce’s Einstein 1 Platform powers Einstein Copilot with your healthcare organization’s unique data and metadata from Data Cloud to capture and summarize patient details, quickly update patient and member information, and automate manual processes Assessment Generation digitizes paper assessments and surveys to capture and track patient data Customers like Baptist Health South Florida and HarmonyCares are using Salesforce to personalize patient interactions and create a single, unified view of each patient Today, Salesforce announced AI and data innovations for CRM to help make healthcare operations more efficient and personalized. Einstein Copilot: Health Actions, a conversational AI assistant that will deliver trusted AI responses grounded with your healthcare organization’s own trusted and private data, Assessment Generation, and Data Cloud for Health help automate and streamline clinical summaries, deliver more personalized communication, and help compile tailored patient assessments faster for care teams, all from a single platform. These new innovations are powered by Salesforce’s Einstein 1 Platform, which helps organizations safely unlock their data to create better patient experiences and augment employee productivity. Why it matters: Nearly a quarter of U.S. healthcare spending is wasted on administrative costs, presenting a potential cost savings of up to $320 billion for healthcare organizations, according to McKinsey and Co. AI could be the solution, with recent Forrester data revealing that 82% of healthcare data leaders say AI is a top focus area that will drive operational efficiency.  Content updated April 2024. 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

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December 16, 2023in Generative AI, Salesforce Einstein
Retrieval Augmented Generation Techniques
13Dec

Retrieval Augmented Generation Techniques

A comprehensive study has been conducted on advanced retrieval augmented generation techniques and algorithms, systematically organizing various approaches. This insight includes a collection of links referencing various implementations and studies mentioned in the author’s knowledge base. If you’re familiar with the RAG concept, skip to the Advanced RAG section. Retrieval Augmented Generation, known as RAG, equips Large Language Models (LLMs) with retrieved information from a data source to ground their generated answers. Essentially, RAG combines Search with LLM prompting, where the model is asked to answer a query provided with information retrieved by a search algorithm as context. Both the query and the retrieved context are injected into the prompt sent to the LLM. RAG emerged as the most popular architecture for LLM-based systems in 2023, with numerous products built almost exclusively on RAG. These range from Question Answering services that combine web search engines with LLMs to hundreds of apps allowing users to interact with their data. Even the vector search domain experienced a surge in interest, despite embedding-based search engines being developed as early as 2019. Vector database startups such as Chroma, Weavaite.io, and Pinecone have leveraged existing open-source search indices, mainly Faiss and Nmslib, and added extra storage for input texts and other tooling. Two prominent open-source libraries for LLM-based pipelines and applications are LangChain and LlamaIndex, both founded within a month of each other in October and November 2022, respectively. These were inspired by the launch of ChatGPT and gained massive adoption in 2023. The purpose of this Tectonic insight is to systemize key advanced RAG techniques with references to their implementations, mostly in LlamaIndex, to facilitate other developers’ exploration of the technology. The problem addressed is that most tutorials focus on individual techniques, explaining in detail how to implement them, rather than providing an overview of the available tools. Naive RAG The starting point of the RAG pipeline described in this article is a corpus of text documents. The process begins with splitting the texts into chunks, followed by embedding these chunks into vectors using a Transformer Encoder model. These vectors are then indexed, and a prompt is created for an LLM to answer the user’s query given the context retrieved during the search step. In runtime, the user’s query is vectorized with the same Encoder model, and a search is executed against the index. The top-k results are retrieved, corresponding text chunks are fetched from the database, and they are fed into the LLM prompt as context. An overview of advanced RAG techniques, illustrated with core steps and algorithms. 1.1 Chunking Texts are split into chunks of a certain size without losing their meaning. Various text splitter implementations capable of this task exist. 1.2 Vectorization A model is chosen to embed the chunks, with options including search-optimized models like bge-large or E5 embeddings family. 2.1 Vector Store Index Various indices are supported, including flat indices and vector indices like Faiss, Nmslib, or Annoy. 2.2 Hierarchical Indices Efficient search within large databases is facilitated by creating two indices: one composed of summaries and another composed of document chunks. 2.3 Hypothetical Questions and HyDE An alternative approach involves asking an LLM to generate a question for each chunk, embedding these questions in vectors, and performing query search against this index of question vectors. 2.4 Context Enrichment Smaller chunks are retrieved for better search quality, with surrounding context added for the LLM to reason upon. 2.4.1 Sentence Window Retrieval Each sentence in a document is embedded separately to provide accurate search results. 2.4.2 Auto-merging Retriever Documents are split into smaller child chunks referring to larger parent chunks to enhance context retrieval. 2.5 Fusion Retrieval or Hybrid Search Keyword-based old school search algorithms are combined with modern semantic or vector search to improve retrieval results. Encoder and LLM Fine-tuning Fine-tuning of Transformer Encoders or LLMs can further enhance the RAG pipeline’s performance, improving context retrieval quality or answer relevance. Evaluation Various frameworks exist for evaluating RAG systems, with metrics focusing on retrieved context relevance, answer groundedness, and overall answer relevance. The next big thing about building a nice RAG system that can work more than once for a single query is the chat logic, taking into account the dialogue context, same as in the classic chat bots in the pre-LLM era.This is needed to support follow up questions, anaphora, or arbitrary user commands relating to the previous dialogue context. It is solved by query compression technique, taking chat context into account along with the user query. Query routing is the step of LLM-powered decision making upon what to do next given the user query — the options usually are to summarise, to perform search against some data index or to try a number of different routes and then to synthesise their output in a single answer. Query routers are also used to select an index, or, broader, data store, where to send user query — either you have multiple sources of data, for example, a classic vector store and a graph database or a relational DB, or you have an hierarchy of indices — for a multi-document storage a pretty classic case would be an index of summaries and another index of document chunks vectors for example. This insight aims to provide an overview of core algorithmic approaches to RAG, offering insights into techniques and technologies developed in 2023. It emphasizes the importance of speed in RAG systems and suggests potential future directions, including exploration of web search-based RAG and advancements in agentic architectures. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the

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December 13, 2023in Salesforce Einstein
Tectonic at a Glance
06Dec

Will You Get a Return on Your AI Investment?

To get a return on AI investment, you must understand what you are investing in with Artificial Intelligence. “You need to be able to be clear-eyed about the potential but be able then to come down to the use cases. What are the functions? What are the tasks? What are the jobs to be done?” CHITRANG DAVE DATA LEADERSHIP COLLABORATIVE The essence of digital transformation, whether it involves cloud migration or implementing enterprise CRM, revolves around achieving results and enhancing productivity. Artificial Intelligence (AI) is no exception to this paradigm. A survey conducted among analytics and IT leaders reveals the manifold benefits of AI adoption, including expedited business decision-making, operational streamlining, increased focus on valuable tasks, automated workflows, and an enhanced customer experience. However, to fully realize these advantages and get a return on AI investment, it is imperative to pinpoint projects that promise the highest returns. Not every use case aligns with an AI solution, and the process of determining where to deploy AI involves both an art and a science. It commences with a profound understanding of the goals at hand and an assessment of potential challenges. Starting from this foundation, the following questions should guide your approach to get a return on AI investment: By addressing these questions systematically, organizations can navigate the complexities of AI deployment, ensuring that the chosen projects align with their objectives and provide tangible benefits in terms of efficiency, innovation, and customer satisfaction. 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 Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more CRM Cloud Salesforce What is a CRM Cloud Salesforce? Salesforce Service Cloud is a customer relationship management (CRM) platform for Salesforce clients to Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more

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December 6, 2023in Salesforce Einstein, Salesforce Implementation Services
Cool New AI Tools
27Nov

Cool New AI Tools

In the rapidly growing world of artificial intelligence, staying abreast of the latest tools is not merely advantageous but imperative. As AI technology advances, so do the instruments that revolutionize problem-solving, innovation, and business growth. Whether you are an experienced developer, an aspiring entrepreneur, or simply interested in the expansive potential of AI, this insight offers a comprehensive guide to the newest and most impactful AI tools available. Additionally, startups and developers can now register their AI projects at no cost by visiting genai.works. Let us dig into this exciting wave of innovation. AI Tools Overview AI for Content & Voiceovers Parlandi AI: Accessible at parlandi.com, this tool enables the generation of various text content such as articles, blogs, advertisements, and media in 53 languages. Additionally, users can create AI-generated images by simply describing them, leveraging solutions like OpenAI DALL-E-2, DALL-E-3, DALL-E-3 HD, and Stable Diffusion by Stability.ai. AI for Clip Generation 10LevelUp: Available at 10levelup.com, this platform automates the creation of viral clips from YouTube videos, facilitating channel growth with minimal user input by generating engaging clips within minutes. AI for In-Depth Qualitative Research ResearchGOAT: Found at researchgoat.com, ResearchGOAT harnesses the burgeoning capabilities of generative AI to design, field, and analyze custom research studies across various vertical markets, geographical regions, and consumer cohorts. AI for Customer Support ChatFly: Accessible via chatfly.co, ChatFly is a robust platform for developing AI-driven chatbots. It empowers businesses to create intelligent bots using their data, which can be seamlessly integrated into existing systems to enhance customer support. AI to Automate Document Processes Base64.ai Document AI: Available at base64.ai, this leading no-code AI solution comprehends documents, photos, and videos, facilitating the automation of document-related processes. AI for Job & CV Management Xtramile: Accessible through lnkd.in, Xtramile offers an Office Add-in that allows the dissemination of job offers across job boards with a single click, streamlining the recruitment process. Conclusion Empower your operations and innovate with these cutting-edge AI tools, tailored to meet a variety of business needs from content creation and customer support to qualitative research and job management. Embrace the future of AI and unlock new potentials for growth and efficiency. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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November 27, 2023in Generative AI, Salesforce Einstein
Salesforce Einstein 1 Platform
22Nov

Salesforce Einstein 1 Platform

Salesforce unveils the groundbreaking Einstein 1 Platform, a transformative force in enterprise AI designed to enhance productivity and cultivate trusted customer experiences by seamlessly integrating data, AI, and CRM. This advanced platform meets the demands of a new AI era, adeptly managing extensive disconnected data, offering flexibility in AI model selection, and seamlessly integrating with workflow processes while prioritizing customer trust. Salesforce Einstein 1 Platform is a game changer from Salesforce. What is the Salesforce Einstein 1 platform? Einstein 1 has a mixture of artificial intelligence tools on the platform, and it kind of mirrors the way the core Salesforce platform is built, standardized and custom. We have out of the box AI features such as sales email generation in Sales Cloud, and service replies in Service Cloud. The Einstein 1 Platform consolidates data, AI, CRM, development, and security into a unified, comprehensive platform, empowering IT professionals, administrators, and developers with an extensible AI platform for rapid app development and automation. Streamlining change and release management, the DevOps Center allows centralized oversight of project work at every stage of the application lifecycle management process, ensuring secure data testing and AI app deployment. Salesforce customizes security and privacy add-on solutions, including data monitoring and masking, backup implementation, and compliance with evolving privacy and encryption regulations. Grounded in the Einstein 1 Platform, Salesforce AI delivers trusted and customizable experiences by leveraging customer data to create predictive and generative interactions tailored to diverse business needs. What are the Einstein platform products? Commerce Cloud Einstein is a generative AI tool that can be used to provide personalized commerce experiences throughout the entire buyer’s journey. It can be used to generate auto-generated recommendations, content, and communications that are based on real-time data from the Data Cloud. Einstein 1 serves as a comprehensive solution for organizations seeking a unified 360-degree view of their customers, integrating Silverline expertise to maximize AI potential and scalability. The introduction of Einstein 1 Data Cloud addresses data integration challenges, enabling users to connect any data source for a unified customer profile enriched with AI, automation, and analytics. Salesforce Data Cloud unifies and harmonizes customer data, enterprise content, telemetry data, Slack conversations, and other structured and unstructured data to create a single view of the customer. Einstein 1 Data Cloud is natively integrated with the Einstein 1 Platform and allows companies to unlock siloed data and scale in entirely new ways, including: Supporting thousands of metadata-enabled objects per customer, the platform ensures scalability, while re-engineering Marketing Cloud and Commerce Cloud onto the Einstein 1 Platform enables seamless incorporation of massive data volumes. Salesforce offers Data Cloud at no cost for Enterprise Edition or above customers, underscoring its commitment to supporting businesses at various stages of maturity. Einstein Copilot Search and the Data Cloud Vector Database further enhance Einstein 1 capabilities, providing improved AI search and unifying structured and unstructured data for informed workflows and automation. Einstein 1 introduces generative AI-powered conversational assistants, operating within the secure Einstein Trust Layer to enhance productivity while ensuring data privacy. Businesses are encouraged to embrace Einstein 1 as a strategic move toward becoming AI-centric, leveraging its unified data approach to effectively train AI models for informed decision-making. Salesforce’s Einstein 1 Platform introduces the Data Cloud Vector Database, seamlessly unifying structured and unstructured business data to enhance AI prompts and streamline workflows. Generative AI impacts businesses differently, augmenting processes to improve efficiency and productivity across sales, service, and field service teams. Einstein 1 Platform addresses challenges of fragmented customer data, offering a unified view for effective AI model training and decision-making. Salesforce’s continuous evolution ensures businesses have access to cutting-edge AI technologies, positioning Einstein 1 as a crucial tool for staying ahead in the AI-centric landscape. Ready to explore Data Cloud for Einstein 1? Limited access is available for $0, offering businesses an exclusive opportunity to leverage this transformative solution. Salesforce’s Einstein 1 Platform introduces advancements in AI search capabilities and unification of structured and unstructured business data, empowering informed workflows and automation. Einstein GPT expands conversational AI across Marketing and Commerce clouds, with the Data Cloud Vector Database playing a pivotal role in unifying data for Einstein 1 users. Einstein now has a generative AI-powered conversational AI assistant that includes Einstein Copilot and Einstein Copilot Studio. These two capabilities operate within the Einstein Trust Layer – a secure AI architecture built natively into the Einstein 1 Platform that allows you to leverage generative AI while preserving data privacy and security standards. Einstein Copilot is an out-of-the-box conversational AI assistant built into the user experience of every Salesforce application. Einstein Copilot drives productivity by assisting users within their flow of work, enabling them to ask questions in natural language and receive relevant and trustworthy answers grounded in secure proprietary company data from Data Cloud. Data Cloud Vector Database simplifies data integration, enhancing AI prompts without costly updates to specific business models. Data Cloud, integrated with the Einstein Trust Layer, provides secure data access and visualization, enabling businesses to fully harness generative AI. Einstein 1, with Data Cloud, offers a solution for organizations seeking comprehensive customer insights, guided by Silverline expertise for AI maximization. Salesforce’s Einstein 1 Platform securely integrates data, connecting various products to empower customer-centric businesses with AI-driven applications. Data Cloud for Einstein 1 supports AI assistants and enhances customer experiences, driving productivity and reducing operational costs. Einstein 1’s impact is evident in increased productivity and enhanced customer experiences, with ongoing evolution ensuring businesses stay at the forefront of AI technology. Generative AI augments existing processes, particularly in sales, service, and customer support, with Einstein 1 providing tools for streamlined operations. Salesforce’s Einstein 1 Platform introduces AI search enhancements and unified data capabilities, empowering businesses with informed decision-making and automation. Ready to embrace AI-driven productivity? Explore Data Cloud for Einstein 1 and revolutionize your business operations today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce

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November 22, 2023in Einstein 1 Platform, Salesforce, Salesforce Einstein

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