Governance Archives - gettectonic.com - Page 4
26Nov

Salesforce Agents are Transforming Internal Workflows

How Salesforce Agents are Transforming Internal Workflows Salesforce CIO and Executive Vice President Juan Perez, with three decades of IT leadership experience, is leading the charge in deploying generative AI solutions like Agentforce within Salesforce. Perez’s approach reflects lessons learned during his tenure at UPS, where he oversaw IT operations for a global enterprise. His strategies emphasize scalability, data strategy, and modernization to support growth, with AI now playing a pivotal role. UPS Lessons Applied to Salesforce Perez draws on his UPS experience in managing IT at scale to navigate Salesforce’s needs as a growing enterprise. At UPS, he managed a complex, global IT organization supporting diverse operations, from running an airline to ensuring timely package delivery. Similarly, Salesforce’s IT strategy prioritizes scalable solutions, robust data strategies, and AI integration. “Salesforce intelligently realized the importance of leveraging its own technologies, including AI, to modernize and support growth,” Perez explains. Generative AI’s Transformative Potential Perez views generative AI (GenAI) as a transformative force on par with the internet’s emergence in the 1990s. By reducing the time spent on data analysis and decision-making, AI enables teams to focus on actions that improve productivity and customer service. While GenAI isn’t a solution in itself, Perez sees it as an enabler that amplifies human efforts. Evaluating and Integrating AI in Salesforce’s Stack Salesforce adopts a rigorous, multi-step approach to evaluate new technologies, including large language models (LLMs) and generative AI tools. Perez outlines a “filtering mechanism” for implementation: This structured approach ensures AI investments are both impactful and sustainable. Measuring AI’s ROI To quantify the impact of AI, Salesforce evaluates metrics like lines of code generated using AI tools and time saved through automation. In one example, approximately 26% of production-ready code in a recent deployment was AI-generated. This efficiency is factored into planning and budgeting, allowing resources to be reallocated to other initiatives. Mitigating “Shadow AI” Risks Perez warns against “shadow AI,” where decentralized or unmanaged AI implementations can lead to security, data privacy, and investment inefficiencies. He stresses the need for visibility and governance to prevent these risks. To address this, Salesforce has established an AI Council that is evolving into an Agentforce Center of Excellence. This body ensures responsible development, aligns projects with organizational goals, and maintains oversight of AI implementations across the enterprise. Responsible and Scalable AI Adoption Salesforce’s commitment to using its own products extends to Agentforce, a generative AI suite designed to streamline internal workflows. With a focus on governance, scalability, and measurable impact, Salesforce sets a benchmark for AI adoption. As Perez explains, “We ensure our AI solutions are safe, effective, and capable of driving significant value while remaining aligned with our strategic goals.” By combining rigorous evaluation, measurable outcomes, and proactive governance, Salesforce demonstrates how AI can transform workflows while mitigating risks. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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November 26, 2024in AI Tools, Data, Enterprise, Generative AI, Salesforce, Technology
healthcare Can prioritize ai governance
22Nov

Salesforce Data Governance

Salesforce Data Governance Best Practices Salesforce provides a centralized platform for managing customer relationships, but without proper data governance, the system can quickly become unmanageable. Data governance ensures the accuracy, security, and usability of the vast amounts of information collected, helping teams make better decisions and maximizing the value of Salesforce investments. By establishing robust processes and policies, organizations can maintain clean, compliant, and reliable data. Here’s an overview of data governance in Salesforce, its importance, and strategies to implement it effectively. What Is Data Governance in Salesforce? Data governance in Salesforce refers to the practices that monitor and manage data accuracy, security, and compliance. Proper governance ensures your Salesforce data remains trustworthy and actionable, avoiding issues like errors, duplicates, and regulatory violations. Key Components of Salesforce Data Governance: Strong governance enables organizations to make informed decisions and unlock Salesforce’s full potential. The Impact of Data Governance on Decision-Making Accurate and well-governed data empowers leaders to make strategic, data-driven decisions. With clean and current records, organizations can: Good governance ensures data integrity, leading to smarter decisions and improved business performance. Principles of Effective Salesforce Data Governance Building a strong data governance framework starts with these core principles: 1. Data Ownership Assign clear ownership of datasets to specific individuals, teams, or departments. Owners are accountable for maintaining data quality, ensuring compliance, and resolving issues efficiently. Benefits include: 2. Monitoring and Compliance Conduct regular audits to ensure data accuracy, detect unauthorized access, and maintain compliance with regulations. Tools like Salesforce’s built-in monitoring features or third-party solutions (e.g., Validity DemandTools) can streamline this process. Audit checks should include: Consistent monitoring safeguards sensitive data and avoids costly fines, particularly in heavily regulated industries like healthcare and finance. Steps to Develop a Data Governance Strategy Techniques for Maintaining High-Quality Data High-quality data is the backbone of Salesforce governance. Apply these techniques to ensure your data meets quality standards: Standardizing Data for Better Governance Data standardization ensures consistency across Salesforce records, improving analysis and operational efficiency. Examples include: Leveraging Data Management Tools Data management tools are essential for maintaining data integrity and enhancing governance. Benefits include: By integrating these tools into your Salesforce processes, you can establish a solid foundation for data governance while boosting operational efficiency. Final Thoughts Effective data governance in Salesforce is critical for maintaining data quality, ensuring compliance, and empowering teams to make strategic decisions. By following best practices and leveraging the right tools, organizations can maximize the value of their Salesforce investment and drive long-term success. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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November 22, 2024in Data, Salesforce, Technology
Collaborative Business Intelligence
21Nov

Collaborative Business Intelligence

Collaborative BI combines BI tools with collaboration platforms, enabling users to connect data insights directly within their existing workflows. This integration enhances decision-making by reducing misunderstandings and fostering teamwork through real-time or asynchronous discussions about data. In traditional BI, data analysis was handled by data scientists and statisticians who translated insights for business users. However, the rise of self-service BI tools has democratized data access, allowing users of varying technical skills to create and share visualizations. Collaborative BI takes this a step further by embedding BI functions into collaboration platforms like Slack and Microsoft Teams. This setup allows users to ask questions, clarify context, and share reports within the same applications they already use, enhancing data-driven decisions across the organization. One real-life time saver in my experience is being able as a marketer to dig in to our BI and generate lists myself, without depending upon a team of data scientists. Benefits of Collaborative BI Leading Collaborative BI Platforms Several vendors offer collaborative BI solutions, each with unique integrations for communication and data sharing: Collaborative BI bridges data analysis with organizational collaboration, creating an agile environment for informed decision-making and effective knowledge sharing across all levels. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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November 21, 2024in Data, Salesforce
Alation Strengthens Data Cloud
20Nov

Alation Anywhere

Alation Anywhere for Google Chrome: A Streamlined Approach to Analysis Alation’s browser-based extension, Alation Anywhere for Google Chrome, integrates its tools directly into the user’s browser to reduce the inefficiencies caused by toggling between applications. This innovation enhances workflow efficiency and accelerates access to data-driven insights, bringing the full power of Alation’s Data Intelligence Platform into the daily flow of work. Tackling Context-Switching with Alation Anywhere A 2022 Harvard Business Review study revealed that Fortune 500 employees toggle between applications 1,200 times per day, resulting in four hours of weekly lost productivity and five weeks of annual time wasted. By embedding Alation’s capabilities into Chrome, users can seamlessly interact with curated and governed data without leaving their current workflows. According to Matt Aslett, analyst at Ventana Research, “The primary goal of capabilities like Alation Anywhere is to accelerate data-driven decision-making. By embedding data catalog functionality within productivity tools, it reduces the delays caused by switching contexts and facilitates quicker access to insights.” Enhanced Data Discovery in the Browser The popularity of web-based applications makes Chrome the ideal platform for extending Alation’s tools. Stewart Bond, analyst at IDC, emphasizes that “most modern applications are accessed via the browser. Alation Anywhere for Chrome eliminates the need to toggle between browser tabs, saving users time and boosting productivity.” Beyond efficiency, embedding analytics into frequently used tools increases the likelihood of data-driven decision-making. By integrating Alation’s insights directly into the browser, users can make informed decisions in real-time, with minimal disruptions. Key Features of Alation Anywhere for Google Chrome The Chrome extension introduces several tools to streamline data discovery and management: These features reflect Alation’s strategy of extending data catalog capabilities to the tools users rely on most, facilitating quicker and more accurate insights. Meeting Modern Data Demands As enterprises increasingly develop AI models and applications, the need for accessible, real-time data is growing. Alation’s vice president of product management, Diby Malakar, highlighted the role of user feedback in driving innovations like the Chrome extension. “Embedding analytics and data management directly into everyday tools is essential for streamlining workflows and reducing friction,” said Malakar. “Our goal is to ensure data intelligence is always available where it drives the most value.” Differentiation and Future Focus While Alation’s browser-based extension addresses a critical need, competitors such as Collibra, Informatica, and Qlik also offer similar capabilities. To stand out, Alation must build on unique initiatives like its new AI governance suite, which addresses emerging challenges in enterprise AI adoption. According to Malakar, Alation will continue developing features that enable both technical and non-technical users to discover and curate trusted data. This aligns with growing enterprise priorities, including real-time analytics and AI model development. “As AI and data initiatives become central to enterprises, the demand for accessible, reliable data will only increase,” Malakar noted. “Alation is committed to evolving our platform to meet these needs … backed by the data they trust.” Alation Anywhere for Google Chrome exemplifies the company’s commitment to improving access to governed data while staying attuned to user workflows and the evolving data landscape. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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November 20, 2024in Data, Enterprise, Google, Salesforce, Snowflake
2025: The Rise of AI Agents and Industry-Focused Innovation
13Nov

2025: The Rise of AI Agents and Industry-Focused Innovation

Over the past few years, CX vendors have rapidly integrated generative AI (GenAI) across the customer experience landscape. This wave of innovation has brought advancements like auto-summarization, customer response recommendations, and intent analysis, especially within Contact Center as a Service (CCaaS) solutions. However, as these capabilities become standard, differentiation now hinges on more advanced AI solutions, orchestration of cross-platform workflows, and collection of industry-specific datasets. AI Agents and Industry-Focused Innovation. Agentic AI, where bots autonomously handle tasks without human intervention, is emerging as a critical differentiator. This shift is reshaping sector-specific processes. Take network providers, for instance; they can leverage agentic AI to detect service outages, create affected customer segments, and proactively send alerts. Salesforce exemplifies this trend with its Agentforce platform, which debuted at Dreamforce 2024, introducing 100 pre-configured, autonomous bots designed for specific industries. By 2025, such bots will likely proliferate, expanding across ecosystems like Workday to facilitate cross-functional automation. Toward a More Autonomous Enterprise As autonomous AI agents advance, they are poised to manage complex, multi-step workflows collaboratively. This move will help organizations move closer to an autonomous enterprise model, where human oversight drives the deployment, testing, and optimization of AI agents. In this model, collaboration platforms such as Microsoft Teams, Slack, and Zoom will serve as operational hubs for managing and refining AI-driven processes. While this full vision may take longer to achieve, 2025 promises substantial advancements in sector-specific efficiencies through AI agents. Not all industries, however, are equally poised to benefit; while healthcare, financial services, and retail lead in AI-enabled CX solutions, other sectors such as hospitality, travel, and education still lag. The Need for Sector-Specific Use Case Libraries CX vendors could empower businesses by providing industry-specific AI use case libraries, building confidence in AI-agent-driven experiences. For example, bots in the finance sector could streamline billing, invoice processing, and ledger management, while spotting and correcting errors. Other industries would benefit from AI innovations tailored to their unique challenges, but such solutions will require co-innovation across CX platforms. 2025 Strategic Technology Trends Gartner’s top technology trends for 2025 provide a framework for CIOs aiming to future-proof their organizations. These trends fall into three themes: AI imperatives, new computing frontiers, and human-machine synergy. These trends will push organizations to adopt cloud, AI, and sustainability-focused architectures, despite challenges. As AI capabilities evolve, so will the risks, emphasizing the need for robust security and ethical frameworks. Salesforce charges up its game with its Agentforce platform, which debuted at Dreamforce 2024, introducing 100 pre-configured, autonomous bots designed for specific industries. By 2025, such bots will likely proliferate, expanding across ecosystems like Workday to facilitate cross-functional automation. Preparing for 2025: Upskilling for the Future As organizations embrace these transformative trends, they must also address a persistent skill gap. Pluralsight’s recent survey reveals that 20% of organizations have deployed AI, while 55% are planning to. However, without strategic business alignment, technology adoption won’t necessarily translate to customer value. For organizations, a focus on responsible innovation and proactive skills development in AI, cloud security, and sustainability will be vital. By preparing for these 2025 trends, businesses can navigate the complexities of the tech landscape and position themselves for long-term success. AI Agents and Industry-Focused Innovation As you prepare for 2025. Tectonic can help you align your goals with your road map. Contact us today! Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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November 13, 2024in Agentforce Platform, Data, Enterprise, Generative AI, Salesforce, Technology
AI Agents as Tools of Trust
13Nov

AI Agents as Tools of Trust

Salesforce Report Highlights AI Agents as Tools to Rebuild Consumer Trust For businesses of any size, the to-do list never ends. Monitoring customers, understanding their needs, and delivering products and services that align with their expectations are critical. Salesforce’s latest research, however, points to a troubling trend: consumer trust is at an all-time low. Yet, the report, State of the AI Connected Customer, also suggests that AI—particularly agentic AI—could help reverse this decline. Trust in Decline The key finding of the Salesforce report is stark: consumer trust in companies has taken a significant hit. Among 15,015 surveyed consumers, 72% say they trust companies less today than they did a year ago. Compounding this is the rapid advancement of AI; 60% of respondents believe that the rise of AI increases the importance of businesses being trustworthy. One major culprit behind eroding trust is the perceived mishandling of customer data. A staggering 65% of respondents feel companies are careless with data, adding to the skepticism. While high prices remain the top reason customers abandon brands, 43% pointed to poor customer service as a major deterrent. Can AI Agents Fill the Gap? The Salesforce report suggests that AI agents—when deployed transparently—could address many of the factors driving distrust and disengagement. Younger consumers, particularly Gen Z and millennials, appear more open to interacting with AI agents. Notable insights from the research include: However, trust is non-negotiable. Transparency is a critical factor for AI adoption: As Michael Affronti, SVP and General Manager of Salesforce Commerce Cloud, explains: “AI agents can help brands deliver consistent, personalized experiences for shoppers across every channel — deepening customer loyalty and ultimately driving more sales.” Building Trust Through Transparency The research underscores the potential for AI to transform customer interactions, but it also highlights the challenges. Transparency and accountability are essential for AI systems to inspire confidence and loyalty. Salesforce’s AI solutions are designed to prioritize transparency and foster reliable consumer experiences. Features such as clear agent identification and robust escalation paths are steps in the right direction. However, companies must double down on governance frameworks and safeguards to ensure AI agents handle data responsibly. Final Thoughts While the idea of using AI to rebuild consumer trust is promising, it’s not without its challenges. Establishing trust in AI itself remains a work in progress. Consumers expect companies to prioritize not only innovation but also ethics, security, and accountability. The Salesforce report demonstrates that younger consumers are already embracing AI as a way to address today’s service expectations. For Salesforce and other companies leveraging agentic AI, the key to success will lie in balancing cutting-edge technology with meaningful protections for customer data and experiences. The future of AI-driven customer engagement isn’t just about meeting expectations—it’s about exceeding them in a way that inspires confidence and loyalty. With the right approach, AI agents could be a vital tool for restoring consumer trust in an era where skepticism runs high. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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November 13, 2024in Data, Salesforce, Salesforce Commerce Cloud, Technology
Enterprises are Adopting AI-powered Automation Platforms
12Nov

Enterprises are Adopting AI-powered Automation Platforms

The rapid pace of AI technological advancement is placing immense pressure on teams, often leading to disagreements due to the unrealistic expectations businesses have for the speed and agility of new technology implementation. A staggering 88% of IT professionals report that they are unable to keep up with the flood of AI-related requests within their organizations. Executives from UiPath, Salesforce, ServiceNow, and ManageEngine offer insights into how enterprises can navigate these challenges. Leading enterprises are adopting AI-powered automation platforms that understand, automate, and manage end-to-end processes. These platforms integrate seamlessly with existing enterprise technologies, using AI to reduce friction, eliminate inefficiencies, and enable teams to achieve business goals faster, with greater accuracy and efficiency. This year’s innovation drivers include tools such as Intelligent Document Processing, Communications Mining, Process and Task Mining, and Automated Testing. “Automation is the best path to deliver on AI’s potential, seamlessly integrating intelligence into daily operations, automating backend processes, upskilling employees, and revolutionizing industries,” says Mark Gibbs, EMEA President, UiPath. Jessica Constantinidis, Innovation Officer EMEA at ServiceNow, explains, “Intelligent Automation blends Robotic Process Automation (RPA), Artificial Intelligence (AI), and Machine Learning (ML) with well-defined processes to automate decision-making outcomes.” “Hyperautomation provides a business-driven, disciplined approach that enterprises can use to make informed decisions quickly by analyzing process and data feedback within the organization,” adds Constantinidis. Thierry Nicault, AVP and General Manager at Salesforce Middle East, emphasizes that while companies are eager to embrace AI, the pace of change often leads to confusion and stifles innovation. He notes, “By deploying AI and Hyperintelligent Automation tools, organizations can enhance productivity, visibility, and operational transformation.” Automation is driving growth and innovation across industries. AI-powered tools are simplifying processes, improving business revenues, and contributing to economic diversification. Ramprakash Ramamoorthy, Director of AI Research at ManageEngine, highlights how Hyperintelligent Automation, powered by AI, uses tools like Natural Language Processing (NLP) and Intelligent Document Processing to detect anomalies, forecast business trends, and empower decision-making. The IT Pushback Despite enthusiasm for AI, IT professionals are raising concerns. A Salesforce survey revealed that 88% of IT professionals feel overwhelmed by the influx of AI-related requests, with many citing resource constraints, data security concerns, and data quality issues. Business stakeholders often have unrealistic expectations about how quickly new technologies can be implemented, creating friction. According to Constantinidis of ServiceNow, many organizations lack transparency across their business units, making it difficult to fully understand their processes. As a result, automating processes becomes challenging. She adds, “Before full hyperautomation is possible, issues like data validation, classification, and privacy must be prioritized.” Automation platforms need accurate data, and governance is crucial in managing what data is used for AI models. “You need AI skills to teach and feed the data, and you also need a data specialist to clean up your data lake,” Constantinidis explains. Gibbs from UiPath stresses that automation must be designed in collaboration with the business users who understand the processes and systems. Once deployed, a feedback loop ensures continuous improvement and refinement of automated workflows. Ramamoorthy from ManageEngine notes that adopting Hyperintelligent Automation alongside existing workflows poses challenges. Enterprises must evaluate their technology stack, considering the costs, skills required, and the potential benefits. Strategic Integration of AI and Automation To successfully implement Hyperintelligent Automation tools, enterprises need a blend of IT and business skills. Mark Gibbs of UiPath points out, “These skills ensure organizations can effectively implement, manage, and optimize hyperintelligent technologies, aligning them with organizational goals.” Salesforce’s Nicault adds, “Enterprises must empower both IT and business teams to embrace AI, fostering innovation while ensuring the technology delivers real value.” Business skills are equally crucial, including strategic planning, process analysis, and change management. Ramamoorthy emphasizes that these competencies help identify automation opportunities and align them with business goals. According to Bassel Khachfeh, Digital Solutions Manager at Omnix, automation must be implemented with a focus on regulatory and compliance needs specific to the industry. This approach ensures the technology supports future growth and innovation. Transforming Customer Experiences and Business Operations As automation evolves, it’s transforming not only back-end processes but also customer experiences and decision-making at every level. Constantinidis from ServiceNow explains that hyperintelligence enables enterprises to predict outcomes and avert crises by trusting AI’s data accuracy. Gibbs from UiPath adds that automation allows enterprises to unlock untapped opportunities, speeding up the transformation of manual processes and enhancing business efficiency. AI is already making an impact in areas like supply chain management, regulatory compliance, and customer-facing processes. Ramamoorthy of ManageEngine notes that AI-powered NLP is revolutionizing enterprise chatbots and document processing, enabling businesses to automate complex workflows like invoice handling and sentiment analysis. Khachfeh from Omnix highlights how Cognitive Automation platforms elevate RPA by integrating AI-driven capabilities, such as NLP and Optical Character Recognition (OCR), to further streamline operations. Looking Ahead Hyperintelligent Automation, driven by AI, is set to revolutionize industries by enhancing efficiency, driving innovation, and enabling smarter decision-making. Enterprises that strategically adopt these tools—by integrating IT and business expertise, prioritizing data governance, and continuously refining their automated workflows—will be best positioned to navigate the complexities of AI and achieve sustainable growth. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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November 12, 2024in Agentforce Platform, Artificial Intelligence, Data, Enterprise, Salesforce, Technology
Snowflake Security and Development
12Nov

Snowflake Security and Development

Snowflake Unveils AI Development and Enhanced Security Features At its annual Build virtual developer conference, Snowflake introduced a suite of new capabilities focused on AI development and strengthened security measures. These enhancements aim to simplify the creation of conversational AI tools, improve collaboration, and address data security challenges following a significant breach earlier this year. AI Development Updates Snowflake announced updates to its Cortex AI suite to streamline the development of conversational AI applications. These new tools focus on enabling faster, more efficient development while ensuring data integrity and trust. Highlights include: These features address enterprise demands for generative AI tools that boost productivity while maintaining governance over proprietary data. Snowflake aims to eliminate barriers to data-driven decision-making by enabling natural language queries and easy integration of structured and unstructured data into AI models. According to Christian Kleinerman, Snowflake’s EVP of Product, the goal is to reduce the time it takes for developers to build reliable, cost-effective AI applications: “We want to help customers build conversational applications for structured and unstructured data faster and more efficiently.” Security Enhancements Following a breach last May, where hackers accessed customer data via stolen login credentials, Snowflake has implemented new security features: These additions come alongside existing tools like the Horizon Catalog for data governance. Kleinerman noted that while Snowflake’s previous security measures were effective at preventing unauthorized access, the company recognizes the need to improve user adoption of these tools: “It’s on us to ensure our customers can fully leverage the security capabilities we offer. That’s why we’re adding more monitoring, insights, and recommendations.” Collaboration Features Snowflake is also enhancing collaboration through its new Internal Marketplace, which enables organizations to share data, AI tools, and applications across business units. The Native App Framework now integrates with Snowpark Container Services to simplify the distribution and monetization of analytics and AI products. AI Governance and Competitive Position Industry analysts highlight the growing importance of AI governance as enterprises increasingly adopt generative AI tools. David Menninger of ISG’s Ventana Research emphasized that Snowflake’s governance-focused features, such as LLM observability, fill a critical gap in AI tooling: “Trustworthy AI enhancements like model explainability and observability are vital as enterprises scale their use of AI.” With these updates, Snowflake continues to compete with Databricks and other vendors. Its strategy focuses on offering both API-based flexibility for developers and built-in tools for users seeking simpler solutions. By combining innovative AI development tools with robust security and collaboration features, Snowflake aims to meet the evolving needs of enterprises while positioning itself as a leader in the data platform and AI space. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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November 12, 2024in AI Tools, Data, Enterprise, Generative AI, Snowflake
healthcare Can prioritize ai governance
11Nov

Healthcare Can Prioritize AI Governance

As artificial intelligence gains momentum in healthcare, it’s critical for health systems and related stakeholders to develop robust AI governance programs. AI’s potential to address challenges in administration, operations, and clinical care is drawing interest across the sector. As this technology evolves, the range of applications in healthcare will only broaden.

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November 11, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Laws, Life Sciences Cloud, Salesforce, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Technology
user q and a
08Nov

Handling Duplicate Phone Numbers in Salesforce Automations

I’m working with a list of customers in Salesforce, where duplicate detection is enabled for the phone field. When manually creating a new customer with an existing phone number, Salesforce displays a warning prompt asking if I want to proceed. The warning doesn’t block the save; it simply alerts me to the duplicate. However, when attempting to insert a record with the same phone number using MAKE, I encounter the following error: RuntimeError[400]: A duplicate record was found. Are you sure you want to create the record? This indicates that the automation is being blocked due to the duplicate detection rules. Solution Options Here are a few strategies to address this and allow the record to be inserted: Best Practices If you need help setting up any of these solutions, let Tectonic know! Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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November 8, 2024in Data, Salesforce
healthcare Can prioritize ai governance
07Nov

AI Data Privacy and Security

Three Key Generative AI Data Privacy and Security Concerns The rise of generative AI is reshaping the digital landscape, introducing powerful tools like ChatGPT and Microsoft Copilot into the hands of professionals, students, and casual users alike. From creating AI-generated art to summarizing complex texts, generative AI (GenAI) is transforming workflows and sparking innovation. However, for information security and privacy professionals, this rapid proliferation also brings significant challenges in data governance and protection. Below are three critical data privacy and security concerns tied to generative AI: 1. Who Owns the Data? Data ownership is a contentious issue in the age of generative AI. In the European Union, the General Data Protection Regulation (GDPR) asserts that individuals own their personal data. In contrast, data ownership laws in the United States are less clear-cut, with recent state-level regulations echoing GDPR’s principles but failing to resolve ambiguity. Generative AI often ingests vast amounts of data, much of which may not belong to the person uploading it. This creates legal risks for both users and AI model providers, especially when third-party data is involved. Cases surrounding intellectual property, such as controversies involving Slack, Reddit, and LinkedIn, highlight public resistance to having personal data used for AI training. As lawsuits in this arena emerge, prior intellectual property rulings could shape the legal landscape for generative AI. 2. What Data Can Be Derived from LLM Output? Generative AI models are designed to be helpful, but they can inadvertently expose sensitive or proprietary information submitted during training. This risk has made many wary of uploading critical data into AI models. Techniques like tokenization, anonymization, and pseudonymization can reduce these risks by obscuring sensitive data before it is fed into AI systems. However, these practices may compromise the model’s performance by limiting the quality and specificity of the training data. Advocates for GenAI stress that high-quality, accurate data is essential to achieving the best results, which adds to the complexity of balancing privacy with performance. 3. Can the Output Be Trusted? The phenomenon of “hallucinations” — when generative AI produces incorrect or fabricated information — poses another significant concern. Whether these errors stem from poor training, flawed data, or malicious intent, they raise questions about the reliability of GenAI outputs. The impact of hallucinations varies depending on the context. While some errors may cause minor inconveniences, others could have serious or even dangerous consequences, particularly in sensitive domains like healthcare or legal advisory. As generative AI continues to evolve, ensuring the accuracy and integrity of its outputs will remain a top priority. The Generative AI Data Governance Imperative Generative AI’s transformative power lies in its ability to leverage vast amounts of information. For information security, data privacy, and governance professionals, this means grappling with key questions, such as: With high stakes and no way to reverse intellectual property violations, the need for robust data governance frameworks is urgent. As society navigates this transformative era, balancing innovation with responsibility will determine whether generative AI becomes a tool for progress or a source of new challenges. While generative AI heralds a bold future, history reminds us that groundbreaking advancements often come with growing pains. It is the responsibility of stakeholders to anticipate and address these challenges to ensure a safer and more equitable AI-powered world. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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November 7, 2024in Data, Generative AI, Laws
Road for AI Regulation
04Nov

Road for AI Regulation

The concept of artificial intelligence, or synthetic minds capable of thinking and reasoning like humans, has been around for centuries. Ancient cultures often expressed ideas and pursued goals similar to AI, and in the early 20th century, science fiction brought these notions to modern audiences. Works like The Wizard of Oz and films such as Metropolis resonated globally, laying the groundwork for contemporary AI discussions.

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November 4, 2024in Generative AI
AI Strategy for Your Business
27Oct

AI Strategy for Your Business

How to Create a Winning AI Strategy for Your Business To maximize the value of AI, organizations must align their AI projects with strategic business objectives. Here’s a 10-step guide to crafting an effective AI strategy, including sample templates to support your planning. While AI adoption is on the rise, many companies still struggle to unlock its full potential. According to the 2024 IDC report Scaling AI Initiatives Responsibly, even organizations with advanced AI practices, termed “AI Masters,” face a 13% failure rate, while those still emerging in AI see a 20% failure rate. Challenges such as poor data quality and cultural resistance often contribute to these failures. To avoid these pitfalls, companies need to adopt a more deliberate and strategic approach to AI implementation. As Nick Kramer from SSA & Company states, “It’s not just about implementing the right technology; a lot of work needs to be done beforehand to succeed with AI.” What is an AI Strategy and Why is it Important? An AI strategy unifies all necessary components—such as data, technology, and talent—required to achieve business goals through AI. This includes: A well-designed AI strategy sets clear directions on how AI should be leveraged to achieve optimal outcomes within the organization. 10 Steps to Craft a Successful AI Strategy Resources for AI Strategy Templates If you’re ready to start building your AI strategy, here are several resources offering templates and guidance: By following these steps and utilizing the right resources, businesses can ensure they capture AI in ways that align with their strategic goals and maximize their competitive edge. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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October 27, 2024in Data, Generative AI, Technology
AI That Forgets
23Oct

AI That Forgets

Salesforce has introduced a generative AI system designed to prioritize data privacy through a unique “forgetting” feature. This innovation allows the AI to process information through large language models (LLMs) without retaining the data, helping companies manage sensitive information more securely. AI That Forgets. As part of the latest wave in generative AI, Salesforce’s solution takes the form of digital “agents”—intelligent systems capable of understanding and responding to customer inquiries autonomously. CEO Marc Benioff has hailed this development as a significant breakthrough for the company, emphasizing its potential to transform customer interactions. AI That Forgets. At a recent event, Patrick Stokes, Salesforce’s EVP of Products and Industries, highlighted how this system supports organizations by reducing the costs and risks associated with building their own AI models. According to Stokes, many companies lack the resources to develop in-house AI sustainably, and Salesforce’s privacy-first approach provides an appealing alternative. Rather than focusing solely on creating the most powerful LLM, Salesforce has built AI agents that connect data and actions securely, addressing privacy concerns that have hindered AI adoption. AI That Forgets Salesforce’s approach integrates privacy-focused safeguards, which Stokes describes as a “trust layer” within the AI system. This feature verifies that data retrieved during an AI query aligns with the user’s access permissions, protecting sensitive information. Stokes notes that unlike traditional AI models that retain data, Salesforce’s LLM processes only the information required for each interaction and then “forgets” it afterward. This zero-retention approach creates a more secure environment, where companies retain governance over data usage and minimize risks associated with long-term data storage. Zahra Bahrololoumi, CEO of Salesforce UK and Ireland, also emphasized that Salesforce’s AI solutions offer users the confidence to adopt generative AI without compromising security. With over 1,000 AI agents already implemented, companies are benefiting from reduced burnout and increased productivity while maintaining data trust and integrity. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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October 23, 2024in Data, Generative AI

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