PHAs Archives - gettectonic.com - Page 24
Ethical and Responsible AI
02Apr

Ethical and Responsible AI

Responsible AI and ethical AI are closely connected, with each offering complementary yet distinct principles for the development and use of AI systems. Organizations that aim for success must integrate both frameworks, as they are mutually reinforcing. Responsible AI emphasizes accountability, transparency, and adherence to regulations. Ethical AI—sometimes called AI ethics—focuses on broader moral values like fairness, privacy, and societal impact. In recent discussions, the significance of both has come to the forefront, encouraging organizations to explore the unique advantages of integrating these frameworks. While Responsible AI provides the practical tools for implementation, ethical AI offers the guiding principles. Without clear ethical grounding, responsible AI initiatives can lack purpose, while ethical aspirations cannot be realized without concrete actions. Moreover, ethical AI concerns often shape the regulatory frameworks responsible AI must comply with, showing how deeply interwoven they are. By combining ethical and responsible AI, organizations can build systems that are not only compliant with legal requirements but also aligned with human values, minimizing potential harm. The Need for Ethical AI Ethical AI is about ensuring that AI systems adhere to values and moral expectations. These principles evolve over time and can vary by culture or region. Nonetheless, core principles—like fairness, transparency, and harm reduction—remain consistent across geographies. Many organizations have recognized the importance of ethical AI and have taken initial steps to create ethical frameworks. This is essential, as AI technologies have the potential to disrupt societal norms, potentially necessitating an updated social contract—the implicit understanding of how society functions. Ethical AI helps drive discussions about this evolving social contract, establishing boundaries for acceptable AI use. In fact, many ethical AI frameworks have influenced regulatory efforts, though some regulations are being developed alongside or ahead of these ethical standards. Shaping this landscape requires collaboration among diverse stakeholders: consumers, activists, researchers, lawmakers, and technologists. Power dynamics also play a role, with certain groups exerting more influence over how ethical AI takes shape. Ethical AI vs. Responsible AI Ethical AI is aspirational, considering AI’s long-term impact on society. Many ethical issues have emerged, especially with the rise of generative AI. For instance, machine learning bias—when AI outputs are skewed due to flawed or biased training data—can perpetuate inequalities in high-stakes areas like loan approvals or law enforcement. Other concerns, like AI hallucinations and deepfakes, further underscore the potential risks to human values like safety and equality. Responsible AI, on the other hand, bridges ethical concerns with business realities. It addresses issues like data security, transparency, and regulatory compliance. Responsible AI offers practical methods to embed ethical aspirations into each phase of the AI lifecycle—from development to deployment and beyond. The relationship between the two is akin to a company’s vision versus its operational strategy. Ethical AI defines the high-level values, while responsible AI offers the actionable steps needed to implement those values. Challenges in Practice For modern organizations, efficiency and consistency are key, and standardized processes are the norm. This applies to AI development as well. Ethical AI, while often discussed in the context of broader societal impacts, must be integrated into existing business processes through responsible AI frameworks. These frameworks often include user-friendly checklists, evaluation guides, and templates to help operationalize ethical principles across the organization. Implementing Responsible AI To fully embed ethical AI within responsible AI frameworks, organizations should focus on the following areas: By effectively combining ethical and responsible AI, organizations can create AI systems that are not only technically and legally sound but also morally aligned and socially responsible. Content edited October 2024. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

Read More
April 2, 2024in AI Tools, Data, Generative AI
Einstein Copilot - A Valued Team Member
27Mar

Einstein Copilot – A Valued Team Member

What Can Salesforce Einstein Copilot AI Really Do? Einstein Copilot – A Valued Team Member To find out, let’s virtually attend a live demo of the service March 2024. The short answer to the question is “whatever your business needs,” but with a common caveat in AI demos: beware of hallucinations. Keeping Data SafeLet’s rewind a bit. Back in September, Salesforce unveiled Einstein Copilot at Dreamforce, emphasizing customer data safety as a key selling point. Salesforce CEO Marc Benioff stated, “Your data isn’t our product.” Then, in February, the product entered public beta. Salesforce re-emphasized that the Einstein Trust Layer, designed to protect customer data, was a critical reason why customers could trust the responses and actions of Salesforce Einstein Copilot. At the demo safety was again a primary focus. Salesforce Product Management leads Gary Brandeleer and Jaswinder Rattanpal highlighted that Einstein is designed to differentiate between sensitive and non-sensitive data and to verify if the end-user has appropriate access rights for their query. These measures prevent leaks of confidential information and also minimize the impact of any potential “hallucinations” by compartmentalizing data. Rattanpal offered a word of caution: “While we have these amazing tools, be careful because we are not at a stage when they can be 100% trusted. Always have a human in the loop, especially when dealing with information that may become public.” Maximizing EfficiencySalesforce’s emphasis on data safety is wise, and its more than 150,000 customers worldwide will appreciate it. However, the real appeal of Einstein Copilot lies in the efficiency it offers. This efficiency stems from two key principles that drive Salesforce’s approach to AI. The first principle is that AI copilots fundamentally change how humans interact with software. Instead of navigating through clicks and menus, users can ask questions and receive answers directly, making software interaction more conversational. This shift can potentially transform software development and reduce the time required to complete tasks, particularly in sales, marketing, and customer service. Users can access Einstein across Salesforce’s interface. One click launches the assistant, which can execute tasks while the user attends to other duties. This reduces the time spent sifting through information to find answers. During the demo, Rattanpal showcased how Einstein could summarize an account’s financial history and populate different fields with data from a single prompt. Customization and AvailabilityThe second principle is the mix of customization and availability. Salesforce aims to allow users to deploy Einstein Copilot across any desired modules and to customize these deployments to suit each customer’s specific needs. Recognizing that its vast customer base has diverse requirements, Salesforce makes Einstein flexible yet grounded in a safety-first approach. Admins can customize Einstein using Copilot Builder, Prompt Builder, and Model Builder, each offering different levels of customization. Standard actions, like “write an email,” require minimal development, while custom actions typically involve more intricate setups. More Than a Copilot: A CoworkerThese capabilities often make Einstein feel more like a valued team member than a mere copilot. During the demo, Brandeleer showed how Einstein could determine whether a sales opportunity was worth pursuing—a subjective query that Einstein backed with a dozen data-driven reasons. This level of analysis, which would take a human hours or days to compile, underscores Einstein’s potential to exceed human efficiency and objectivity. When an AI can provide better answers to subjective questions than a human, it transcends being a simple tool. If it can effectively manage hallucinations, the question becomes: what can’t Einstein do? Salesforce Einstein Copilot stands out not only for its robust data safety measures but also for the significant efficiency and customization it offers. With its advanced capabilities, Einstein has the potential to revolutionize how businesses handle routine and complex tasks, making it an invaluable asset for any organization. Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

Read More
March 27, 2024in Einstein 1 Platform, Salesforce
Gov Agencies AI Workforce Challenges
24Mar

Gov Agencies AI Workforce Challenges

Federal agencies are placing a higher priority on providing AI training to their workforces with a focus on principles of transparency and accountability, officials announced at ATARC’s GITEC conference in Charlottesville, Virginia earlier. Gov Agencies AI Workforce Challenges. Alexis Bonnell, Air Force Research Laboratory CIO and Director of the Digital Capabilities Directorate, emphasized the importance of upholding existing ethics standards rather than creating new ones. She stressed that agencies need to exercise the ethical principles they have always been expected to follow. President Biden’s October 2023 executive order on artificial intelligence mandated that agencies develop ethical AI and establish AI offices, among other directives. While agencies like the Defense Department and the Department of Homeland Security are optimistic about AI’s potential, leaders remain cautious about its ethical implications and stress the importance of safe technology development. It’s not just technologists who require AI training. To ensure all employees understand AI’s risks and benefits, government leaders are prioritizing education and upskilling efforts. Steven Brand, Energy Deputy CIO of Resource Management, highlighted the initiative to provide foundational AI training across his department, emphasizing that the goal is not to make employees experts. Tammy Hornsby-Fink, Executive Vice President and System CISO at the Federal Reserve Bank of Richmond, emphasized the need for accessible learning opportunities for all department members, from data scientists to executive assistants, to grasp AI concepts in manageable increments. Hornsby-Fink also emphasized the importance of providing sandboxes for employees to experiment with new technologies safely, stressing that experimentation is key to understanding how these technologies can create business value. According to Tony Boese, Department of Veterans Affairs Interagency Programs Manager, consistent education is essential to combat misinformation about AI. He mentioned the agency’s ASPIRE data-literacy program, which leverages AI to identify skills gaps and tailor educational pathways for individuals. Karen Howard, IRS Office of Online Services Executive Director, highlighted the need to modernize recruitment strategies and change management principles to attract top talent and leverage digital transformation and AI effectively. Jamie Holcombe, U.S. Patent and Trademark Office CIO, emphasized the importance of diversifying agency workforces by bringing in new perspectives from industry, such as those from Silicon Valley, to move away from outdated organizational playbooks. Gov Agencies AI Workforce Challenges Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

Read More
March 24, 2024in Salesforce Einstein
End of Support for Workflow Rules and Process Builder
20Mar

End of Support for Workflow Rules and Process Builder

Every month Salesforce announces retirement and sundowning of products and features. For long-time Salesforce power users, this one feels like the end of an era. Salesforce has announced the end of support for Workflow Rules and Process Builder in 2025. Scheduled Paths are a replacement for Scheduled Actions in Process Builder and Time-Based Workflow Rules. And they even have some new and improved features like support for minutes! Flows can now call other sub-flows, a much-longed-for replacement for Process Builder’s ability to call Flows. End of Support for Workflow Rules and Process Builder Salesforce will no longer be supporting Workflow Rules and Process Builder on December 31, 2025, and we recommend that you migrate your automation to Flow Builder by that time. Have you taken advantage of all the powerful features of Flow? Not yet? Have no fear, and go with the Flow! We’ve curated a comprehensive guide of resources to take you from zero to hero on your journey with us to migrate your organization from Workflow Rules and/or Process Builder onto the newer and more powerful engine of Flow! Why move to Flow? What’s happening with Workflow Rules and Process Builder? Salesforce is starting the process of moving away from Workflow Rules and Process Builder, and transitioning to the more feature-rich functionality of Flow. You’re also probably wondering why Salesforce is retiring Workflow Rules and Process Builder. Salesforce wants to focus development on a modern, extensible, low-code automation solution in Flow Builder, which led to retire the previous features. What does this change mean for me?  If you have active Workflow Rules or Process Builder processes running after 2025, they will no longer receive customer support or bug fixes. What action can I take? We recommend implementing a plan to migrate any active rules or processes to Flow Builder before the deadline. Depending on the complexity of your org, this migration may take a significant amount of time and testing, so we recommend starting now. To assist in the migration process, we have a Migrate to Flow tool and extensive support resources available. What happens if I don’t take action? After December 31, 2025, Workflow Rules and Process Builder may continue to function and execute existing automation, but customer support will not be available, and bugs will not be fixed. How do I identify affected users? You can identify whether you have active workflow rules by going to Setup | Process Automation | Workflow Rules and sorting the Active column for checkmarks. You can identify whether you have active Process Builder processes by going to Setup | Process Automation | Process Builder and sorting the Status column for Active. If you have more questions, open a case with support via Salesforce Help. To view all current and past retirements, see Salesforce Product & Feature Retirements. What does the transition to Flows entail? The transition is set to take place in multiple phases. The first phase began with the Winter ’22 release, wherein the ability to create net-new Workflow Rules was turned off. In Summer ‘23 release the ability to create net-new processes in Process Builder will be disabled. In the last phase, Workflow Rules and Process Builder will go away entirely, and any platform automation will be leveraging Flow. This phased approach will allow administrators ample time to transition to Flow with as minimal effort as possible. What changed in the Winter ’22 release? As of Winter ’22, we’ve blocked the creation of Workflow Rules. You can still activate, deactivate, and edit any existing Workflow Rules. To test and create Workflow Rules for use in managed packages, developer orgs still allow you to create Workflow Rules. Process Builder has remained unaffected during this period. For new automation, use Flow. Link to Release Notes What changed in the Summer ’23 release? Starting in Summer ’23, Salesforce began blocking the creation of Process Builder Processes. Much like workflow rules above, you can still activate, deactivate, and edit any existing Processes. For new automation, use Flow. How can existing Workflow Rules & Processes be transitioned to Flow? A tool called “Migrate to Flow” allows you to covert existing Workflow Rules & Processes to Flow in an org, at the click of a button. More information about this tool can be found here. There are a few considerations to keep in mind as we roll out this tool. If your organization relies heavily on existing Workflow Rules or Process Builder, please consider starting the migration process today using the Migrate to Flow tool, rather than when the deadline approaches. This will also allow you to test the migrated Flows in small batches to ensure your organization’s needs are adequately met. How can I get started in Flow? For those with limited exposure to Flows, tinkering in a trailhead playground, demo org, or other non-production org is a great way to explore the capabilities of Flows. Once ready, admins can try creating any new automation needed in their organization in Flows rather than in Process Builder. Trailhead – The best place to start! Build Flows with Flow BuilderRecord-Triggered FlowsAutomate Your Business Processes with Salesforce Flow Automation Home Page – Watch videos, read blog posts, or explore other Trailhead offerings related to Flows. Trailhead Community Topics – Join the discussion!#Flow#Automation What about feature parity between Workflows/Processes and Flows? During the last few releases, many of the biggest parity gaps between Workflow Rules/Process Builder and Flow have been addressed already, including: Additionally, more and more gaps are being addressed with each new release. What about Approval Processes, Email Alerts, and other functionality operating on the workflow engine? There are no plans to retire any of these auxiliary features; however, each of them can be leveraged through flow (or Flow Orchestrator, in the case of approval processes), without the need to use processes or workflow rules at all. Flow Trailhead – Click Here Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read

Read More
March 20, 2024in Salesforce
Acceptable AI Use Policies
19Mar

AI Data Readiness

Data Readiness: The Key to AI Success As organizations rapidly integrate AI into their CRM systems, data readiness has become a critical factor in successful AI implementation. A study by Forrester found that companies prioritizing data readiness achieve better outcomes when deploying AI-powered CRM features. Additionally, organizations can leverage vendors to enhance their data readiness and AI capabilities, though trust remains a serious consideration. The Impact of Data Readiness The Growing Importance of AI-Ready Data AI adoption has surged across industries, with a McKinsey survey revealing that AI adoption has more than doubled in recent years. Furthermore, IBM’s 2023 CEO study found that 75% of CEOs believe competitive advantage hinges on advanced generative AI capabilities. However, AI is only as effective as the data supporting it. Chad Sanderson, CEO of Gable, likens data readiness to a healthy diet: “If garbage goes in, garbage comes out.” AI-ready data must be well-governed, secure, bias-free, enriched, accurate, and high quality. What is Data Readiness for AI? Data readiness for AI involves preparing data to ensure its effectiveness in AI-driven applications. Key characteristics of AI-ready data include: Defining AI-Ready Data: 4 Key Factors Different industry experts provide varied definitions of AI-ready data: In our interpretation, AI-ready data exists in a unified platform, not in silos. It is enriched with metadata, ensuring transparency in data lineage, enabling AI systems to deliver meaningful insights. Additionally, AI-ready data requires a governance framework to maintain security, compliance, and ethical use. 1. Metadata Management Metadata management provides essential context for AI systems. Key elements include: 2. Data and Metadata Quality Management High-quality AI output depends on high-quality data. Organizations should continuously evaluate data based on relevance, reliability, and accuracy. Equally important is metadata quality, which ensures that AI models interpret and apply data correctly. 3. Data Lineage Management Understanding data origins and transformations is crucial for AI readiness. Harvard Business Review emphasizes the importance of ontology, a structured representation of data relationships, in AI system development. 4. Data Governance Data governance establishes AI standards, ensuring ethical and compliant AI practices. Steve Lohr of The New York Times highlights data provenance as a key governance element, akin to food safety regulations ensuring transparency in data origins and processing. Is Your Data AI-Ready? According to IBM’s 2023 CEO survey, the top barriers to AI adoption include concerns around data lineage, security, and compliance. Harvard Business Review also cites siloed data as a major challenge, emphasizing that AI success requires well-prepared data systems. Gartner underscores the necessity of lighthouse principles—guiding frameworks for ethical AI usage. Organizations lacking structured governance, transparency, and documentation are not yet AI-ready. How to Ensure Data Readiness for AI A Gartner survey found that only 9% of CIOs and data leaders have an AI vision statement, with over a third lacking plans to create one. The first step to AI readiness is defining an AI vision statement and setting lighthouse principles to guide AI initiatives. Key strategies for AI readiness include: Wrapping Up Achieving data readiness for AI involves ensuring data is secure, accurate, bias-free, enriched, and accessible. Key components include metadata management, quality assurance, data lineage tracking, and governance frameworks. Ultimately, AI readiness extends beyond technology—it requires a cultural shift within organizations. Aligning strategy, preparing data, and fostering AI-driven decision-making will enable organizations to harness AI’s full potential. By following these best practices, businesses can ensure their AI investments deliver meaningful, data-driven insights. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

Read More
March 19, 2024in Data, Generative AI, Salesforce, Technology
Channel Sales Management Salesforce
12Mar

Channel Sales Management Salesforce

Salesforce provides tools for channel sales management, primarily through its Partner Relationship Management (PRM) solutions, which are integrated within Sales Cloud. This allows companies to manage and optimize their indirect sales channels, including partners, distributors, and resellers. Salesforce PRM offers features like partner portals, dashboards, and automation to streamline communication, collaboration, and deal management.  Key aspects of Salesforce Channel Sales Management: Benefits of Salesforce Channel Management: You could say, Salesforce provides a robust platform for managing and optimizing channel sales, enabling businesses to expand their reach, drive revenue growth, and build stronger relationships with their partners.  Content updated June 2025. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

Read More
March 12, 2024in Data, Salesforce
UX Design Trends 2024
08Mar

UX Design Trends 2024

Navigating Design Trends: AI, Discovery, Accessibility, and Collaboration. Salesforce UX Design Trends 2024. As we reflect on the past year and look ahead, design trends are emerging, signaling a pivotal moment in the intersection of creativity, usability, and AI. For developers, admins, architects, and business leaders, understanding these trends is crucial in shaping the future. Here are the four design trends steering this transformative journey: As we move forward, these design trends signify a paradigm shift, emphasizing the significance of AI, streamlined discovery, accessibility, and the growing collaboration between designers and developers. Navigating this transformative landscape requires an adaptable mindset and a commitment to ethical, inclusive design practices from the outset. When you work with Tectonic we take all these considerations to mind as we design or re-design your Salesforce org. Contact Tectonic today. UX Design Trends 2024 Like2 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

Read More
March 8, 2024in Salesforce
Data Management and Data Maturity
07Mar

Data Management and Data Maturity

Data Management and Data Maturity: Generative AI Raises Concerns About Data Ethics and Equity Harnessing the capabilities of generative AI is contingent on having comprehensive, unified, and accurate data, as indicated by more than half of IT leaders. However, several obstacles hinder progress. A recent survey unveils that a majority of IT leaders lack a unified data strategy, impeding the seamless integration of generative AI into their existing technology stack. Beyond technical challenges, generative AI also brings to the forefront serious ethical considerations. Key findings from the survey reveal: AI Illuminates Data Management While generative AI garners attention, more established AI applications, such as predictive analytics and chatbots, have long been advantageous for organizations. Technical leaders leveraging AI report significantly faster decision-making and operations. The benefits extend beyond speed, with analytics and IT leaders highlighting more time to address strategic challenges rather than being immersed in mundane tasks. Customers also reap the rewards, with technical leaders noting substantial improvements in customer satisfaction due to AI. Given the pivotal role of quality data in AI outcomes, it is unsurprising that nearly nine out of ten analytics and IT leaders consider new developments in AI to prioritize data management. Realized Benefits of AI Adoption Analytics and IT leaders cite several top benefits realized from AI adoption: Data Maturity Signals AI Preparedness Data maturity emerges as a foundational element for successful AI adoption, with high-maturity organizations boasting superior infrastructure, strategy, and alignment compared to their low-data-maturity counterparts. The disparities are particularly evident in terms of data quality, with high-maturity respondents being twice as likely as low-maturity respondents to possess the high-quality data required for effective AI utilization. Like2 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

Read More
March 7, 2024in Generative AI, Salesforce Data Cloud, Salesforce Einstein
AI and the Role of Healthcare CIOs
07Mar

AI and the Role of Healthcare CIOs

Healthcare leaders see significant potential in data analytics and AI technology to transform the industry over the next five years, according to a new market research report from Arcadia and The Harris Poll. AI and the Role of Healthcare CIOs The report, titled “The Healthcare CIO’s Role in the Age of AI,” examines AI’s impact on the healthcare sector and how decision-makers are preparing to leverage the technology. Notably, 96% of healthcare leaders surveyed believe that adopting AI effectively will provide a competitive edge both now and in the future. While only a third see AI as essential today, 73% expect it to become critical within five years. How Health Systems Are Using AI Around 63% of respondents revealed that their organizations use AI to analyze large patient data sets to identify trends and guide population health management efforts. Another 58% are using AI to analyze individual patient data to identify opportunities for improving health outcomes. Close to half of the leaders indicated that AI is being used to optimize electronic health records (EHR) management and analysis. These trends align with the findings of the recent “Top of Mind for Top Health Systems” survey, conducted by the University of Pittsburgh Medical Center’s Center for Connected Medicine (CCM) in collaboration with KLAS, which identified AI as the most exciting emerging technology in healthcare with transformative potential for both administration and care delivery. The excitement surrounding healthcare AI largely stems from its ability to break down data silos and tap into the wealth of clinical data that healthcare organizations already collect. “Healthcare leaders are thoughtfully preparing to harness the full value of AI in care delivery reform,” said Aneesh Chopra, Arcadia’s chief strategy officer. “As safe, secure data sharing scales, technology leaders prioritize data platforms that organize fragmented patient records into clinically relevant insights at every stage of the patient journey.” A quest for a 360 degree patient view abounds. Using AI to Support Strategic Priorities The Arcadia survey emphasized the importance of using analytics to improve patient care, with 83% of leaders believing that harnessing data will help healthcare organizations remain competitive and resilient while overcoming digital transformation and financial challenges. Eighty-four percent of respondents cited technology as a current priority, with 44% focusing on an enterprise-wide approach to data analytics, 41% prioritizing AI-driven decision-making, and 32% working to simplify technical ecosystems. These efforts are viewed as crucial to advancing other strategic goals, with 40% of leaders prioritizing the patient experience, 35% aiming to improve outcomes, and 29% focusing on patient engagement. Although healthcare leaders view AI adoption positively for strategic advancements, hurdles remain. While 96% of respondents are confident in adopting AI, many feel pressured to move quickly. When asked about the sources of this pressure, 82% cited data and analytics teams, 78% pointed to IT and tech teams, and 73% mentioned executives. However, successfully implementing AI requires talent and resources that some organizations lack. About 40% of leaders identified a lack of talent as a significant barrier to AI adoption, signaling the need for IT and analytics teams to acquire new skill sets. Seventy-one percent of IT leaders reported a growing demand for data-driven decision-making skills, while two-thirds pointed to a rising need for expertise in data analysis, machine learning, and systems integration. Additionally, nearly 60% mentioned the need for roles that focus on training and support for healthcare staff. The Evolving Role of CIOs CIOs and other healthcare leaders are seeing their roles evolve as AI and data become more integrated into healthcare operations. Eighty-seven percent of respondents see themselves as strategy influencers, actively involved in setting and executing AI strategies, while only 13% view themselves as purely focused on implementation. Despite these evolving roles, many CIOs feel constrained by daily operations. Fifty-eight percent reported being primarily focused on tactical execution rather than developing long-term AI strategies, although they believe they should spend 75% of their time on strategic planning to be most effective. Part of these strategies will likely focus on improving communication and workforce readiness. Three out of four leaders cited a lack of effective communication between IT teams and clinical staff as a barrier to leveraging new technologies, and two out of five noted that clinical staff are not fully equipped to make the best use of data analytics. “CIOs and their teams are setting the stage for an AI-powered revolution in patient care and healthcare operations,” said Michael Meucci, Arcadia’s president and CEO. “Our findings highlight a strong consensus that a solid data foundation is necessary to realize the future of AI in healthcare. At the same time, the human workforce, with evolving talent and skills, will shape the real-world impact of AI in healthcare.“ Content updated August 2024. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

Read More
March 7, 2024in Generative AI, Salesforce Development Services, Salesforce Health and Life Sciences Solutions
demand generation web use cases for personalization
03Mar

Demand Generation Web Use Cases for Personalization

Utilize effective personalization techniques adopted by businesses in online campaigns to stimulate demand generation. The term “demand generation” has somewhat faded from the marketing lexicon due to the emphasis on analytics, AI, and metrics for lead conversion. However, where does personalization fit into the broader scope of demand generation? Demand generation web use cases for personalization. Personalization plays a pivotal role in various aspects of demand generation: In lead nurturing, personalization is equally vital: Moreover, personalization is instrumental in lead acquisition efforts by delivering relevant experiences to all of your prospects. To effectively implement personalization, real-time insights into individual behaviors and interactions are essential. A comprehensive personalization solution should unify data from various channels and systems, enabling seamless cross-channel personalization. This includes “stitching” together anonymous and known user profiles, integrating data with complementary systems like CRMs and marketing platforms, and facilitating real-time omni-channel personalization. The key to successful personalization lies in understanding and addressing each individual’s unique needs and preferences. By adopting a customer-centric approach and setting clear objectives aligned with business goals, organizations can leverage personalization to enhance customer experiences, boost conversion rates, and drive measurable business growth. To execute a successful personalization strategy, organizations must: By following these steps and continuously optimizing personalization efforts, organizations can build stronger customer relationships, drive business growth, and maximize marketing ROI. Website personalization serves as the starting point for many companies embarking on their personalization journey. This entails ensuring that returning visitors encounter pages tailored to their previous experiences or recent purchases. It can also involve presenting new customers with product recommendations based on their current browsing session. The return on this initial investment can be substantial, with many companies witnessing a significant increase in conversion rates, sometimes by as much as 50% or more. For instance, a site converting 2% of visitors might see that figure rise to 3%, a dream scenario for digital marketers. Moreover, this boost in conversion rates can have far-reaching effects across marketing programs, leading to a reduction in overall customer acquisition costs. Tectonic now offers Personalization Implementation Solutions. The next stage in personalization maturity involves integrating a customer’s web and email experiences. This seamless connection between two major channels for customer engagement brings organizations closer to achieving an omni-channel personalization experience. Timely and relevant follow-up messages after a customer’s website visit or purchase can deepen relationships and enhance lifetime value without significant additional marketing expenditure. Finally, the ultimate goal is to extend personalization across all channels, ensuring consistent and tailored experiences wherever customers interact with your brand. However, achieving this can be challenging due to fragmented customer data across multiple channels, teams, and systems. An effective personalization solution should consolidate and synthesize this cross-channel information by maintaining unified customer profiles and enabling real-time omni-channel personalization. Testing is a crucial aspect of successful personalization efforts, allowing organizations to optimize campaigns and maximize engagement, conversions, and revenue. A robust personalization solution should facilitate A/B testing, measuring lift over control, evaluating impacts against specific goals, and filtering results by segment. Effective website personalization lays the foundation for broader personalization efforts across channels. By seamlessly integrating web and email experiences and extending personalization to all touchpoints, organizations can deliver tailored experiences that drive engagement, loyalty, and ultimately, business growth. By Tectonic’s Salesforce Marketing Platform Architect Shannan Hearne Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

Read More
March 3, 2024in Data, Einstein Insights, Generative AI, Salesforce, Salesforce Einstein, Salesforce Marketing Cloud, Salesforce Optimization, Salesforce Sales Cloud
AI Adoption Not Even Across the Board
07Feb

AI Adoption Not Even Across the Board

Reflecting on AI’s potential and its challenges, McElheran calls for a balanced approach: “To fully harness AI’s benefits, we need a realistic, evidence-based approach that accounts for both the advantages and the societal costs associated with adoption.”

Read More
February 7, 2024in Data, Technology
AI Capability Maturity Model
30Jan

AI Capability Maturity Model

The AI Capability Maturity Model (AI CMM), devised by the Artificial Intelligence Center of Excellence within the GSA IT Modernization Centers of Excellence (CoE), functions as a standardized framework for federal agencies to evaluate their organizational and operational maturity levels. It is equally useful for private organizations in aligning them with predefined objectives. Instead of imposing normative capability assessments, the AI CMM concentrates on illuminating significant milestones indicative of maturity levels along the AI journey. The AI Capability Maturity Model focuses primarily on the development of AI capabilities within an organization. It evaluates an organization’s maturity across four main areas: data, algorithms, technology, and people. Serving as a valuable tool, the AI CMM assists organizations in shaping their unique AI roadmap and investment strategy. The outcomes derived from AI CMM analysis empower decision-makers to identify investment areas that address immediate goals for rapid AI adoption while aligning with broader enterprise objectives in the long run. Maturity vs capability models A maturity model tends to measure activities, such as whether a certain tool or process has been implemented. In contrast, capability models are outcome-based, which means you need to use measurements of key outcomes to confirm that changes result in improvements. AI development rooted in sound software practices underpins much of the content discussed in this and other chapters. Though not explicitly delving into agile development methodology, Dev(Sec)Ops, or cloud and infrastructure strategies, these elements are fundamental to the successful development of AI solutions. The AI CMM elaborates on how a robust IT infrastructure leads to the most successful development of an organization’s AI practice. What are the maturity levels of AI? What are the maturity levels of Artificial Intelligence? Or it can be measured this way. AI Maturity Model Why is AI maturity important? The AI Maturity Assessment is a process designed to help organizations evaluate their current AI capabilities, identify gaps and areas for improvement, and develop a roadmap to build a more effective AI program. Organizational Maturity Areas Organizational maturity areas represent the capacity to embed AI capabilities across the organization. Two approaches, top-down and user-centric, offer distinct perspectives on organizational maturity. Top-Down, Organizational View Bottom-Up, User-centric View Operational Maturity Areas Operational maturity areas represent organizational functions impacting the implementation of AI capabilities. Each area is treated as a discrete capability for maturity evaluation, yet they generally depend on one another. PeopleOps CloudOps DevOps SecOps DataOps MLOps AIOps AI Capability Maturity Model This comprehensive overview of organizational and operational maturity areas underlines the multifaceted nature of AI implementation and the critical role played by diverse elements in ensuring success across different layers of an organization. How AI is transforming the world? AI-powered technologies such as natural language processing, image and audio recognition, and computer vision have revolutionized the way we interact with and consume media. With AI, we are able to process and analyze vast amounts of data quickly, making it easier to find and access the information we need. Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

Read More
January 30, 2024in Salesforce Einstein
Unfolding AI Revolution
19Jan

Unfolding AI Revolution

Ways the AI Revolution is Unfolding The transformative potential of artificial intelligence (AI) is being explored by James Manyika, Senior VP of Research, Technology, and Society at Google, and Michael Spence, Nobel laureate in economics and professor at NYU Stern School of Business, in their recent article, “The Coming AI Economic Revolution: Can Artificial Intelligence Reverse the Productivity Slowdown?” Published in Foreign Affairs, the article outlines the conditions necessary for an AI-powered economy to thrive, including policies that augment human capabilities, promote widespread adoption, and foster organizational innovation. Manyika and Spence highlight AI’s potential to reverse stagnating productivity growth in advanced economies, stating, “By the beginning of the next decade, the shift to AI could become a leading driver of global prosperity.” However, the authors caution that this economic revolution will require robust policy frameworks to prevent harm and unlock AI’s full potential. Here are the key insights from their analysis: 1. The Great Slowdown The rapid advancements in AI arrive at a critical juncture for the global economy. While technological innovations have surged, productivity growth has stagnated. For instance, total factor productivity (TFP), a key contributor to GDP growth, grew by 1.7% in the U.S. between 1997 and 2005 but has since slowed to just 0.4%. This slowdown is exacerbated by aging populations and shrinking labor forces in major economies like China, Japan, and Italy. Without a transformative force like AI, economic growth could remain stifled, characterized by higher inflation, reduced labor supply, and elevated capital costs. 2. A Different Digital Revolution Unlike the rule-based automation of the 1990s digital revolution, AI has shattered previous technological constraints. Advances in AI now enable tasks that were previously unprogrammable, such as pattern recognition and decision-making. AI systems have surpassed human performance in areas like image recognition, cancer detection, and even strategic games like Go. This shift extends the impact of technology to domains previously thought to require exclusively human intuition and creativity. 3. Quick Studies Generative AI, particularly large language models (LLMs), offers exceptional versatility, multimodality, and accessibility, making its economic impact potentially transformative: Applications range from digital assistants drafting documents to ambient intelligence systems that automate homes or generate health records based on patient-clinician interactions. 4. Creative Instruction Despite its promise, AI has drawn criticism for issues like bias, misinformation, and the potential for job displacement. Critics highlight that AI systems may amplify societal inequities or produce unreliable outputs. However, research suggests that AI will primarily augment work rather than eliminate it. While about 10% of jobs may decline, two-thirds of occupations will likely see AI enhancing specific tasks. This shift emphasizes collaboration between humans and intelligent machines, requiring workers to develop new skills. Studies, such as MIT’s Work of the Future task force, reinforce that automation will not lead to a jobless future but rather to evolving roles and opportunities. 5. With Us, Not Against Us The full benefits of AI will not materialize if its deployment is left solely to market forces. Proactive measures are necessary to maximize AI’s positive impact while mitigating risks. This includes fostering widespread adoption of AI in ways that empower workers, enhance productivity, and address societal challenges. Policies should prioritize accessibility and equitable diffusion to ensure AI serves as a force for inclusive economic growth. 6. The Real AI Challenge Generative AI has the potential to spark a productivity renaissance at a time when the global economy urgently needs it. Yet, Manyika and Spence caution that AI could exacerbate existing economic disparities if not guided effectively. They argue that focusing solely on existential threats overlooks the broader risks posed by inequitable AI deployment. Instead, a positive vision is needed—one that prioritizes AI as a tool for global economic progress, equitable growth, and generational prosperity. “Harnessing the power of AI for good will require more than simply focusing on potential damage,” the authors conclude. “It will demand effective measures to turn that vision into reality.” The unfolding AI revolution offers immense opportunities, but realizing its full potential requires thoughtful action. By addressing risks and fostering innovation, AI could reshape the global economy for the better. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

Read More
January 19, 2024in Artificial Intelligence, Generative AI, Google, Technology
gettectonic.com