Generative AI - gettectonic.com - Page 23
Digital Transformation for Life Sciences

Digital Transformation for Life Sciences

In hindsight, one remarkable aspect of the COVID crisis was the speed with which vaccines passed through regulatory approval processes to address the pandemic emergency. Approvals that would typically take years were expedited to mere months, a pace not usually seen in the life sciences industry. It was an extraordinary situation, as Paul Shawah, Senior Vice President of Commercial Strategy at Veeva Systems, notes: “There were things that were unnaturally fast during COVID. There was a shifting of priorities, a shifting of focus. In some cases, you had the emergency approvals or the expedited approvals of the vaccines that you saw in the early days, so there was faster growth. Everything was kind of different in the COVID environment.” Today, the industry is not operating at that same rapid pace, but the impact of this acceleration remains significant: “What it did do is it challenged companies to think about why can’t we operate faster at a steady state? There was an old steady state, then there was COVID speed. The industry is trying to get to a new steady state. It won’t be as fast as during COVID because of unique circumstances, but expectations are now much higher. This drives a need to modernize systems, embrace the cloud, become more digital, and improve efficiency.” Companies like Veeva, alongside enterprise giants such as Salesforce, SAP, and Oracle, specialize in this market and play crucial roles in life sciences digitization. According to a McKinsey study, about 45% of tech spending in life sciences goes to three key technologies: applied Artificial Intelligence, industrialized Machine Learning, and Cloud Computing. Over 80% of the top 20 global pharma and medtech companies are operating in the cloud to some extent. However, a study by Accenture found that life sciences firms are among the lowest in achieving benefits from cloud investments, with only 43% satisfied with their results and less than a quarter confident that cloud migration initiatives will deliver the promised value within expected time frames. This presents both a challenge and an opportunity. Frank Defesche, SVP & GM of Life Sciences at Salesforce, sees it as the latter, stating: “The life sciences industry faces increased competition, evolving patient expectations, and ongoing pressure to bring devices and drugs to market faster. With rising drug costs, frustrated doctors, and varying regulatory scrutiny, life sciences organizations must find ways to do more with less.” The industry also contends with an unprecedented influx of data and disparate systems, making it difficult to move quickly. Addressing changes one by one is too slow and costly. Defesche believes that a systemic solution, fueled by connected data and Artificial Intelligence (AI), is key to overcoming these challenges. Paul Shawah of Veeva emphasizes the unique challenges of the life sciences sector: “Life sciences firms primarily do two things: discover and develop medicines, and commercialize them by educating doctors and getting the right drugs to patients. The drug development cycle includes clinical trials, managing everything related to drug safety, the manufacturing process, and ensuring quality. They also manage regulatory registrations. On the commercial side, it’s about reaching out to doctors and healthcare professionals.” Veeva’s Vault platform is designed for life sciences, with customers like Merck, Eli Lilly, and Boehringer Ingelheim. Shawah acknowledges it’s “still relatively early days” for cloud computing adoption but notes successes in areas like CRM, where Veeva achieved over 80% market share by standardizing processes and reducing technical debt. Other areas, like parts of the clinical trials process, remain largely untapped by cloud computing. Shawah sees opportunities to improve patient experiences and make the process more efficient. AI represents a significant area of opportunity. Shawah explains Veeva’s approach: “I’ll break AI into two categories: traditional AI, Machine Learning, and data science, which we’ve been doing for a long time, and generative AI, which is new. We’re focusing on finding use cases that create sustainable, repeatable value. We’re building capabilities into our Vault platform to support AI.” Joe Ferraro, VP of Product, Life Sciences at Salesforce, emphasizes AI’s critical role: “We are born out of the data and AI era, and we’re taking that philosophy into everything we do from a product standpoint. We aim to move from creating a system of record to a system of insight, using data and AI to transform how users interact with software.” Ferraro highlights the need for change: “Organizations told us, ‘Please don’t build the same thing we have now. We are mired in fragmented experiences. Our sales and marketing teams aren’t talking, and our medical and commercial teams don’t understand each other.’ Life Sciences Cloud aims to move the industry from these fragmented experiences to an end-to-end, AI-powered experience engine.” The COVID crisis highlighted the critical role of the life sciences industry. There’s a massive opportunity for digital transformation, whether through specialists like Veeva or enterprise players like Salesforce, Oracle, and SAP. Data must be the foundation of any solution, especially amidst the current AI hype cycle. Ensuring this data is well-managed is a crucial starting point for industry-wide change. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
Order Management Enhancements Commerce Cloud

Order Management Enhancements Commerce Cloud

Salesforce Order Management Enhancements stands out for its scalability, catering to both small startups and large enterprises by offering customization to meet specific needs. Its adaptability facilitates seamless integration with other systems, presenting a comprehensive solution for e-commerce requirements. Explore the latest features in Salesforce Order Management and Lightning B2B Commerce, including process exception management, return merchandise authorizations, and various enhancements. Salesforce Order Management now provides tools to identify and address process exceptions during order processing interruptions. The addition of a new return order data model aids in managing return merchandise authorizations. Further updates include improved order summary flows, customization of payment methods, and the creation of order summaries with custom numbers and statuses. Enhancements to Lightning B2B Commerce introduce new components for deliveries and order summaries, improved searchability, an integration dashboard, and the Price Book Workspace. The integration dashboard consolidates management of integrations, providing a centralized overview. Order Management serves as the central hub for handling the entire order lifecycle, covering order capture, fulfillment, shipping, payment processing, and service. Customers can submit orders through any commerce channel, and merchants can efficiently manage fulfillment, shipping, invoicing, and service using integrated and customizable workflows. Salesforce Mobile App allows easy access to data on the go, although certain console features are not available. Order Management offers various resources, including preconfigured permission sets, Salesforce Payments integration for seamless payment processing, and extensive documentation for setup, administration, and extension. Salesforce Order Management utilizes Einstein Generative AI to enhance the overall experience, providing smarter service to customers. High-scale orders are supported on Hyperforce, offering increased capacity for order processing. For post-implementation monitoring and optimization, tracking key performance indicators (KPIs) such as order processing time, customer satisfaction, and inventory levels is crucial. Salesforce analytics aid in making data-driven improvements, and troubleshooting tools are available for resolving common issues like order discrepancies and payment failures. The Salesforce Order Management Implementation Guide outlines steps from initial setup and data migration to workflow customization, payment, and shipping integrations. Real-world case studies demonstrate successful implementations, showcasing the platform’s benefits, scalability, and advanced features. The retirement of Commerce Cloud Order Management is announced, with Salesforce Order Management positioned as the new product built within the Salesforce core platform. For further details or assistance, users are advised to contact their account manager or refer to Salesforce Order Management Help documentation. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
Salesforce SOQL Tools and AI

Salesforce SOQL Tools and AI

Salesforce Object Query Language (SOQL) is a robust instrument empowering users to retrieve data from Salesforce efficiently. With SOQL, you can interrogate any Salesforce object, spanning from custom objects to user permissions like profile and permission set perms. Salesforce SOQL Tools and AI. As a seasoned Salesforce Admin, crafting SOQL queries can often prove cumbersome. Not only must you master the syntax, but you must also ensure the queries are both effective and precise. Salesforce SOQL Tools and AI Fortunately, the advent of generative artificial intelligence (AI) tools offers a solution to this challenge. Generative AI tools, capable of producing text, code, and creative content, have emerged as invaluable aids in generating SOQL queries. Among these, Bard and ChatGPT have emerged as particularly helpful tools for crafting SOQL queries. In this discourse, I’ll illustrate how to leverage prompts to generate SOQL queries using these generative AI tools, providing query examples and resources to kickstart your journey with SOQL in your organization. Initiating SOQL Queries with Generative AI Tools To start, it’s essential to furnish the generative AI tool with pertinent information about the intended query. For instance, one might instruct the tool to generate a query retrieving all open opportunities. Subsequently, upon prompting: “Write SOQL that returns all open opportunities in Salesforce.” ChatGPT furnishes the following response: “SELECT Id, Name, StageName, CloseDate, Amount FROM Opportunity WHERE IsClosed = false” This query selects Opportunity records and retrieves their pertinent attributes, filtering the results to include only open opportunities based on the IsClosed field. Bard echoes a similar sentiment with its rendition of the query: “SELECT Id, Name, StageName, Amount, CloseDate FROM Opportunity WHERE IsClosed = false” However, the essence of prompt writing and critical thinking lies in refining the generated response. For instance, by slightly altering the prompt to include additional criteria: “Write SOQL that returns all open opportunities in Salesforce, including all standard fields and filtering results by StageName.” ChatGPT furnishes an adapted query: “SELECT Id, Name, AccountId, Amount, CloseDate, CreatedBy.Name, CreatedDate, LastModifiedBy.Name, LastModifiedDate, StageName FROM Opportunity WHERE StageName != ‘Closed Won’ AND StageName != ‘Closed Lost’” It’s worth noting that ChatGPT lacks insight into the specifics of your organization’s structure, necessitating occasional guidance to accommodate custom fields. However, through conversational cues, you can educate it accordingly: “My Salesforce org includes a custom field with the API name of NonProfit__c for Opportunity. It’s a boolean field. Please include it in all future SOQL queries on Opportunity.” To which ChatGPT responds: “Certainly! To include the custom field NonProfit__c in all future SOQL queries on Opportunity, you can modify the query as follows: SELECT Id, Name, AccountId, Amount, CloseDate, CreatedBy.Name, CreatedDate, LastModifiedBy.Name, LastModifiedDate, StageName, NonProfit__c FROM Opportunity WHERE StageName != ‘Closed Won’ AND StageName != ‘Closed Lost’” By incorporating NonProfit__c into the SELECT statement, the custom field is seamlessly integrated into the query results. Leveraging SOQL for User Permissions Reporting One particularly advantageous application of SOQL for Salesforce Admins is reporting on user permissions. By utilizing the SOQL Query tool in Code Builder or the Developer Console, admins can scrutinize permissions assigned to users via permission sets, profiles, objects, and fields. Here are a few illustrative examples of user permission SOQL queries, collaboratively devised with the assistance of ChatGPT: Check user permissions on an object: SELECT PermissionsRead, PermissionsCreate, PermissionsEdit, PermissionsDelete FROM ObjectPermissions WHERE ParentId IN (SELECT Id FROM PermissionSet WHERE PermissionSet.Name = ‘Your_Permission_Set_Name’) AND SObjectType = ‘Your_Object_Name’ AND PermissionsRead = true Check user permissions on a field: SELECT PermissionsRead, PermissionsEdit FROM FieldPermissions WHERE ParentId IN (SELECT Id FROM PermissionSet WHERE PermissionSet.Name = ‘Your_Permission_Set_Name’) AND SObjectType = ‘Your_Object_Name’ AND Field = ‘Your_Field_Name’ AND PermissionsRead = true Determine which permission sets grant Edit access for a specific field: SELECT ParentId, Parent.Name, Parent.Type, Field, PermissionsEdit, PermissionsRead, SobjectType FROM FieldPermissions WHERE Parent.IsOwnedByProfile = true AND Field = ‘ADM_Work__c.Subject__c’ AND PermissionsEdit = True Identify users assigned managed packages: SELECT Id, UserId, PackageLicense.NamespacePrefix FROM UserPackageLicense WHERE PackageLicense.NamespacePrefix = ‘YOUR_PREFIX_HERE’ Embark on Efficient SOQL Query Generation Today With generative AI tools, initiating sample SOQL queries becomes a n easier process, alleviating the need to grapple with syntax intricacies. For admins who occasionally require SOQL queries and find themselves toggling between documentation and references to commence, leveraging generative AI represents a compelling alternative. Here are some additional pointers for harnessing generative AI tools to craft SOQL queries effectively: Be precise: Furnish the tool with specific instructions to ensure accuracy and efficiency in query generation. Provide examples: Supplying the tool with query examples aids in generating more tailored queries. Test rigorously: Following query generation, ensure thorough testing to verify the returned results align with expectations. Ultimately, by harnessing the power of generative AI, admins can streamline the process of crafting SOQL queries, thereby enhancing productivity and efficiency in Salesforce data management endeavors. Data analysis serves as a cornerstone of business strategy, yet crafting custom SOQL queries to import specific Salesforce data can prove complex and time-consuming, particularly for those without coding expertise. When you add the necessity to amalgamate data from various systems, the process becomes even more cumbersome and inefficient. Coefficient Salesforce SOQL Tools and AI In this insight, we’ll demonstrate how Coefficient’s Formula Builder, powered by GPT, streamlines the creation of custom SOQL functions, and how Coefficient facilitates direct data imports from Salesforce within Google Sheets, all seamlessly integrated into your workflow without ever leaving your spreadsheet. To get started, launch the Coefficient add-on directly within your Google Sheets. If you haven’t already installed Coefficient, simply navigate to the Google Workspace Marketplace to acquire it. Here’s how to install Coefficient: Now that you have Coefficient installed, you can effortlessly import your live Salesforce data. In the Coefficient sidebar within Google Sheets, follow these steps: To do this, follow these steps: The Formula Builder will promptly generate a custom SOQL query based on your specifications. Simply copy this query, and you’re ready to go. With Coefficient, data analysis becomes more efficient and accessible, empowering users of all skill levels to harness the power of Salesforce data seamlessly within Google

Read More
Roles in AI

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 Elm furniture store on University Avenue in Palo Alto. A group of artificial intelligence enthusiasts within Salesforce, seeking to enhance the impact of machine learning models, embarked on this mission two years ago. While shoppers checked out furniture above, they developed a system to automate the creation of machine learning models. Thus Salesforce’s Quest for AI for the Masses started. Despite being initially named after the Transformers leader, the tie-in was abandoned, and Salesforce named its AI program Einstein. This move reflects the ambitious yet practical approach Salesforce takes in the AI domain. In March, a significant portion of Einstein became available to all Salesforce users, aligning with the company’s tradition of making advanced software accessible via the cloud. Salesforce, although now an industry giant, retains its scrappy upstart identity. When the AI trend gained momentum, the company aimed to create “AI for everyone,” focusing on making machine learning affordable and accessible to businesses. This populist mission emphasizes practical applications over revolutionary or apocalyptic visions. Einstein’s first widely available tool is the Einstein Intelligence module, designed to assist salespeople in managing leads effectively. It ranks opportunities based on factors like the likelihood to close, offering a practical application of artificial intelligence. While other tech giants boast significant research muscle, Salesforce focuses on providing immediate market advantages to its customers. Einstein Intelligence The Einstein Intelligence module employs machine learning to study historical data, identifying factors that predict future outcomes and adjusting its model over time. This dynamic approach allows for subtler and more powerful answers, making use of various data sources beyond basic Salesforce columns. Salesforce’s AI team strives to democratize AI by offering ready-made tools, ensuring businesses can benefit from machine learning without the need for extensive customization by data scientists. The company’s multi-tenant approach, serving 150,000 customers, keeps each company’s data separate and secure. Salesforce’s Quest for AI for the Masses To scale AI implementation across its vast customer base, Salesforce developed Optimus Prime. This system automates the creation of machine learning models for each customer, eliminating the need for extensive manual involvement. Optimus Prime, the AI that builds AIs, streamlines the process and accelerates model creation from weeks to just a couple of hours. Salesforce plans to expand Einstein’s capabilities, allowing users to apply it to more customized data and enabling non-programmers to build custom apps. The company’s long-term vision includes exposing more of its machine learning system to external developers, competing directly with AI heavyweights like Google and Microsoft in the business market. Originally published in WIRED magazine on August 2, 2017 and rewritten for this insight. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
gettectonic.com