The introduction of LAMs marks a significant advancement in AI, focusing on actionable intelligence. By enabling robust, dynamic interactions through function calling and structured output generation, LAMs are set to redefine the capabilities of AI agents across industries.
Examples of Data Governance Frameworks Data governance is not a one-size-fits-all approach. Organizations must carefully choose a framework that aligns with their unique goals, structure, and culture. Data is one of an organization’s most valuable assets, and proper governance is key to unlocking its potential. Without a well-designed framework, companies risk poor data quality, privacy breaches, regulatory noncompliance, and missed insights. A data governance framework provides a structured way to manage data throughout its lifecycle, including policies, processes, and standards to ensure data is accurate, accessible, and secure. By putting clear guidelines in place, organizations can increase trust in their data and improve decision-making. Key Pillars of a Data Governance Frameworks A robust data governance framework typically rests on four key pillars: 1. Center-Out Model The center-out model places a centralized team, such as a data governance council, at the core of the governance process. This group establishes policies and oversees data management across the organization, balancing consistency with flexibility for different departments. The Data Governance Institute’s framework is an example of this model. It focuses on creating a Data Governance Office responsible for managing key governance functions such as setting data policies, assigning data stewards, and monitoring compliance. The framework provides a clear structure while allowing business units some leeway in adapting governance practices to their needs. PwC’s model also adopts a center-out approach, with an emphasis on using data governance to monetize data assets. It highlights the importance of maintaining consistency while minimizing the risk of data silos. 2. Top-Down Model In the top-down model, data governance is driven by executive leadership, ensuring alignment with strategic goals. This model provides authority for enforcing governance standards but may face challenges if business units feel disconnected from the central governance team. McKinsey’s framework exemplifies this approach, focusing on integrating data governance with broader business transformation efforts. Executive leadership plays a key role in ensuring that governance initiatives receive the necessary attention and resources. 3. Hybrid Model The hybrid model combines centralized governance with flexibility for individual business units. It establishes an enterprise-wide framework while allowing departments to adapt governance practices to their specific needs. The Eckerson Group’s Modern Data Governance Framework represents a hybrid approach. It emphasizes the importance of people and culture, alongside technology and processes, and encourages organizations to create a roadmap for governance that evolves as needs change. This model provides a balance between centralized control and decentralized flexibility. 4. Bottom-Up Model In the bottom-up model, data governance is driven by subject matter experts and data stakeholders across the organization. This approach promotes collaboration and buy-in from the people closest to the data, ensuring that governance policies are practical and effective. The DAMA-DMBOK framework, developed by the Data Management Association, is a prime example. Although flexible, it often starts as a bottom-up initiative, driven by IT departments and data experts who later gain executive support. 5. Silo-In Model The silo-in model allows individual business units or departments to create their own governance practices. While this approach addresses localized data issues, it often leads to inconsistencies and challenges when the organization needs to integrate data across the enterprise. Though not widely recommended, the silo-in approach may emerge when specific business units take the initiative to establish governance due to regulatory requirements or data management needs within their domains. However, as organizations mature, they often transition to more holistic frameworks to support cross-functional collaboration and data integration. Choosing the Right Framework Selecting the right data governance framework involves evaluating the organization’s needs, structure, and culture. Whether an organization adopts a center-out, top-down, hybrid, bottom-up, or silo-in approach, success depends on involving key stakeholders, securing executive buy-in, and committing to continuous improvement. By treating data as a critical asset and implementing a governance framework that aligns with its business strategy, an organization can ensure that its data management practices support growth, innovation, and regulatory compliance. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
The fundamental capabilities of collaboration platforms have remained largely unchanged since the pandemic began. These platforms typically offer video conferencing, desktop sharing, and text chat, creating a virtual approximation of in-person meetings. While this setup effectively allows teams to collaborate across distances, it raises the question: Is this all there is to the collaboration experience? Enter Copilot. Microsoft is pioneering a new era of collaboration, where AI assistants help users prioritize meetings, manage follow-ups on action items, and integrate meeting outputs into future tasks. This evolution is particularly promising for knowledge workers who are overwhelmed by constant meetings. Copilot aims to redefine the collaboration experience, promising increased productivity and a more strategic approach to meetings. However, OpenAI, Microsoft’s prominent AI partner, is making moves to disrupt the enterprise space as well. OpenAI recently launched ChatGPT Enterprise, which now boasts 600,000 users, including clients from 93% of the Fortune 500. This week, OpenAI also acquired the videoconferencing startup Multi, sparking speculation that the company may integrate collaboration features directly into ChatGPT. Multi’s unique approach to videoconferencing—described as “multiplayer” and drawing parallels to gaming rather than traditional meetings—hints at a potential shift in how meetings are experienced. The Multi tool, set to be discontinued in July following the acquisition, was tailored for software developers, focusing on screen sharing and leveraging Zoom’s video capabilities. Yet, the concept of enhanced document collaboration extends beyond software developers. Integrating document collaboration with AI-driven features like summarization, and linking this to advanced language models, could revolutionize the collaboration experience. This approach promises to streamline the collaborative process, focusing on the work at hand with new functionalities. That said, not all meetings revolve around documents. Many are simply conversations—often the ones people prefer to avoid. Therefore, refining how meetings are managed and integrating them into users’ work lives will remain crucial, even as new technologies enhance screen sharing and video capabilities. So, where does this leave traditional video services? The quest for meeting equity and AI-enhanced directors will likely continue to refine the experience, striving for the “next best thing to being there.” As the collaboration platform evolves, any outdated elements will become more apparent. Ultimately, collaboration is a multifaceted experience, and technology will play a key role in its continued advancement. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
Salesforce has announced several major updates to its Data Cloud at the annual Dreamforce conference, aimed at enhancing data analysis and boosting the capabilities of its new AI agents, Agentforce. These updates include support for unstructured data in audio and video formats, 50 new data connectors, and a low-latency pipeline to ensure faster AI responses. Salesforce describes Data Cloud as a customer data platform designed to unify data from various sources, providing actionable insights for sales, service, and marketing teams. The latest features will further improve this functionality, according to Rahul Auradkar, Salesforce’s General Manager of Unified Data Services and Einstein. Key Updates 1. Unstructured Data Support: Audio and Video Formats Salesforce has introduced the ability to process unstructured audio and video data. This allows enterprises to analyze information from sources like customer calls, training sessions, product demos, and webinars. The unstructured data can enrich customer profiles, provide deeper insights into behavior, and enhance the performance of AI agents within Agentforce by improving response accuracy. IDC’s Hayley Sutherland highlighted that vectorization of this content enables retrieval-augmented generation (RAG), enhancing the use of large language models (LLMs) to generate more effective and context-aware responses. 2. 50 New Data Connectors Salesforce has expanded its connector library with 50 new options, raising the total to 200. This helps businesses integrate diverse data sources, facilitating improved personalization and decision-making. These connectors are also critical for feeding data to AI agents, enabling them to generate more accurate insights. 3. Sub-second Data Layer for Real-time Responses A new sub-second data layer has been rolled out, allowing enterprises to ingest, unify, analyze, and act on data in real time. This layer powers Einstein Personalization, enabling instant AI-driven recommendations and analytics. It’s especially important for the low-latency needs of Agentforce’s autonomous AI agents, ensuring faster, more accurate customer interactions. 4. Governance and Security Enhancements Salesforce has introduced AI tagging and classification features for unstructured data, automating labeling and organization to ensure secure access. Policy-based governance will offer granular control over data access based on tags, metadata, and user attributes. Additionally, customer-managed encryption keys ensure data security, while the newly available private connect feature allows secure data sharing between Data Cloud and public cloud environments. 5. Data Cloud One and Hybrid Search Data Cloud One, a new feature, will allow enterprises to connect multiple Salesforce accounts across different departments or regions into a single instance, simplifying data management. The hybrid search feature, which combines vector and keyword search, is designed to improve the speed and accuracy of finding industry-specific information. Other updates include Tableau Semantics, which helps organize data based on its meaning, and a new data community platform to foster collaboration among data professionals. These updates reflect Salesforce’s commitment to enhancing data-driven decision-making and delivering powerful AI capabilities for enterprises. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
Salesforce and IBM are advancing their longstanding partnership by focusing on transforming sales and service processes with AI, particularly for organizations in regulated industries that seek to leverage enterprise data for automation. The collaboration aims to deliver pre-built AI agents and tools that integrate seamlessly within customers’ IT environments, enabling them to use their proprietary data while maintaining full control over their systems. By merging Salesforce’s Agentforce, a suite of autonomous agents, with IBM’s watsonx capabilities, the partnership will empower businesses to utilize AI agents within their daily applications. IBM’s watsonx Orchestrate will enhance Agentforce with autonomous agents that improve productivity, security, and regulatory compliance. Additionally, IBM customers will have the ability to interact with these agents via Slack, facilitating dynamic conversational experiences. Planned integrations between Salesforce Data Cloud and IBM Data Gate for watsonx will enable access to business data from IBM Z mainframes and Db2 databases, supporting AI workflows across the Agentforce platform. This integration will enhance data analysis and fuel AI-driven processes. Customers will also benefit from a broader range of AI model and deployment options through integration with IBM watsonx.ai. This will include access to IBM’s Granite foundation models, designed for enterprise applications. Enhancing Business Automation with Tailored Autonomous Agents Through the Agentforce Partner Network, businesses can develop and customize AI agents to interact with various enterprise tools and platforms. These agents are designed to perform multi-step tasks, make decisions based on triggers or interactions, and seek user approval for actions beyond their scope. They will help automate routine tasks, increase efficiency, streamline operations, and enhance customer service. IBM’s watsonx Orchestrate will integrate with Salesforce Agentforce to develop new pre-built agents for specific business challenges. These agents will leverage data and AI from both Salesforce and IBM to address various needs: Expanding Data Integration for AI Salesforce and IBM are also advancing data integration strategies through the Zero Copy integration between Salesforce Data Cloud and watsonx.data. This allows data to remain in place while being utilized for AI use cases, without duplication. Joint customers, particularly in financial services, insurance, manufacturing, and telecommunications, will leverage this integration to access and use mainframe datasets from IBM Z and Db2 databases on Salesforce’s platform. IBM will be the first Zero Copy partner to facilitate data flow between IBM Z and Salesforce Cloud, offering secure access to critical enterprise data and enhancing AI agent functionality. With IBM Z handling over 70% of global transaction value, this partnership ensures high standards of security, privacy, and compliance. Improving Efficiency with Slack and IBM watsonx Orchestrate IBM customers will now engage with watsonx Orchestrate agents directly within Slack, supporting AI app experiences with a new interface. This integration allows for seamless interaction with AI agents, automating tasks and enhancing collaboration across systems without leaving Slack. Expanding AI Model and Deployment Options with watsonx.ai A new integration with watsonx.ai will enable customers to deploy customized large language models (LLMs) within Salesforce Model Builder. This includes access to a range of third-party models and IBM’s Granite foundation models, which offer transparency and compliance with regulatory requirements. IBM Granite models are expected to be available within the Salesforce ecosystem by October. Partnering with IBM Consulting for Tailored AI Solutions IBM Consulting will leverage its expertise in Salesforce and AI to help joint customers accelerate the implementation of Agentforce. Through IBM Consulting Advantage, the AI-powered delivery platform, businesses will receive support in selecting, customizing, deploying, and scaling AI agents to meet specific industry needs. Customer Perspective Tectonic is transforming its service stations into preferred journey stops with the help of Salesforce and IBM. The collaboration offers unprecedented flexibility in AI utilization, enabling Tectonic to deliver hyper-personalized services through Agentforce and IBM’s watsonx AI, enhancing customer engagement and satisfaction. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
Healthcare leaders are increasingly optimistic about the transformative potential of AI and data analytics in the industry, according to a new market research report by Arcadia and The Harris Poll. The report, titled “The Healthcare CIO’s Role in the Age of AI,” reveals that 96% of healthcare executives believe AI adoption can provide a competitive edge, both now and in the future. While one-third of respondents see AI as essential today, 73% believe it will become critical within the next five years. How AI is Being Used in Healthcare The survey found that 63% of healthcare organizations are using AI to analyze large patient data sets, identifying trends and informing population health management. Additionally, 58% use AI to examine individual patient data to uncover opportunities for improving health outcomes. Nearly half of the respondents also reported using AI to optimize the management of electronic health records (EHRs). These findings align with a similar survey conducted by the University of Pittsburgh Medical Center’s Center for Connected Medicine (CCM), which highlighted AI as the most promising emerging technology in healthcare. The focus on AI stems from its ability to break down data silos and make use of the vast amount of clinical data healthcare organizations collect. “Healthcare leaders are preparing to harness AI’s full potential to reform care delivery,” said Aneesh Chopra, Arcadia’s chief strategy officer. “With secure data sharing scaling across the industry, technology leaders are focusing on platforms that can organize fragmented patient records into actionable insights throughout the patient journey.” Supporting Strategic Priorities with AI AI and data analytics are also seen as critical for maintaining competitiveness and resilience, particularly as organizations face digital transformation and financial challenges. In fact, 83% of respondents indicated that data-driven tools could help them stay ahead in these areas. Technology-related priorities, such as adopting an enterprise-wide approach to data analytics (44%) and enhancing decision-making through AI (41%), were top of mind for many healthcare leaders. Improving patient experience (40%), health outcomes (35%), and patient engagement (29%) were also highlighted as key strategic goals that AI could help achieve. Challenges in AI Adoption While most healthcare leaders are confident about adopting AI (96%), they also feel pressure to do so quickly, with the push primarily coming from data and analytics teams (82%), IT teams (78%), and executives (73%). One major obstacle is the lack of talent. Approximately 40% of respondents identified the shortage of skilled professionals as a top barrier to AI adoption. To address this, organizations are seeing increased demand for skills related to data analysis, machine learning, and systems integration. Additionally, 71% of IT leaders emphasized the growing need for data-driven decision-making skills. The Evolving Role of CIOs The rise of AI is reshaping the role of CIOs in healthcare. Nearly 87% of survey respondents see themselves as strategic influencers in setting and refining AI-related strategies, rather than just implementers. However, many CIOs feel constrained by the demands of day-to-day operations, with 58% reporting that tactical execution takes precedence over long-term AI strategy development. Leaders agree that to be effective, CIOs and their teams should focus more on strategic planning, dedicating around 75% of their time to developing and implementing AI strategies. Communication and workforce readiness are also crucial, with 75% of respondents citing poor communication between IT teams and clinical staff as a barrier to AI success, and 40% noting that clinical staff need more support to utilize data analytics effectively. “CIOs and their teams are setting the stage for an AI-driven transformation in healthcare,” said Michael Meucci, president and CEO of Arcadia. “The findings show that a robust data foundation and an evolving workforce are key to realizing AI’s full potential in patient care and healthcare operations.” Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
OpenAI has released its latest model, GPT-5, also known as Project Strawberry or GPT-o1, positioning it as a significant advancement in AI with PhD-level reasoning capabilities. This new series, OpenAI-o1, is designed to enhance problem-solving in fields such as science, coding, and mathematics, and the initial results indicate that it lives up to the anticipation. Key Features of OpenAI-o1 Enhanced Reasoning Capabilities Safety and Alignment Targeted Applications Model Variants Access and Availability The o1 models are available to ChatGPT Plus and Team users, with broader access expected soon for ChatGPT Enterprise users. Developers can access the models through the API, although certain features like function calling are still in development. Free access to o1-mini is expected to be provided in the near future. Reinforcement Learning at the Core The o1 models utilize reinforcement learning to improve their reasoning abilities. This approach focuses on training the models to think more effectively, improving their performance with additional time spent on tasks. OpenAI continues to explore how to scale this approach, though details remain limited. Major Milestones The o1 model has achieved impressive results in several competitive benchmarks: Chain of Thought Reasoning OpenAI’s o1 models employ the “Chain of Thought” prompt engineering technique, which allows the model to think through problems step by step. This method helps the model approach complex problems in a structured way, similar to human reasoning. Key aspects include: While the o1 models show immense promise, there are still some limitations, which have been covered in detail elsewhere. However, based on early tests, the model is performing impressively, and users are hopeful that these capabilities are as robust as advertised, rather than overhyped like previous projects such as SORA or SearchGPT by OpenAI. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more
Conversational AI for Salesforce Supercharge your Salesforce workflows with the power of AI. Whether you’re tracking deals, reviewing pipeline performance, or uncovering insights, Qatalog’s AI assistant simplifies it all with natural language queries. Designed to understand the intent behind your questions, it delivers accurate, context-rich answers—no manual reporting required. Whether you’re a Salesforce novice or a seasoned pro, Salesforce and Qatalog redefine how you engage with your CRM data. Key Features Salesforce and Qatalog Conversational Search Say goodbye to navigating complex dashboards and reports. Just ask straightforward questions like: Get instant, actionable answers powered by AI, saving time and effort. No Technical Expertise Needed Qatalog’s intuitive AI chat interface is designed for everyone. Non-technical users can quickly access insights without needing Salesforce expertise, freeing up technical teams to focus on higher-value tasks. Seamless Integrations Connect Salesforce with your favorite business tools, including Outlook, Google Drive, Slack, and more. Access Salesforce CRM data in context across your apps, streamlining workflows and collaboration. Enterprise-Grade Data Security Your data’s privacy is paramount. Qatalog processes Salesforce data securely in real-time and discards it immediately after use, ensuring sensitive information stays protected. Transform the way you work with Salesforce—ask, explore, and act with confidence using Qatalog’s Conversational AI. Salesforce and Qatalog. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
Data breaches reached an all-time high in 2023, affecting more than 234 million individuals, and there’s no sign of the trend slowing down. At the center of this challenge is how organizations allocate resources to safeguard customer data. One of the most critical systems for managing this data is CRM platforms like Salesforce, used by over 150,000 U.S. businesses. However, security blind spots within Salesforce continue to pose significant risks. To address these concerns, the National Institute of Standards and Technology (NIST) offers a strategic framework for Salesforce security teams. In February 2024, NIST released Version 2.0 of its Cybersecurity Framework (CSF), marking the first major update in a decade. Key improvements include the introduction of a new “Govern” function, streamlining of categories to simplify usability, and updates to the “Respond” function to enhance incident management. This framework now applies across all industries, not just critical infrastructure. For Salesforce security leaders, these changes will significantly affect how they manage security, from aligning Salesforce practices with enterprise risk strategies to strengthening oversight of third-party apps. Here’s how these updates will influence Salesforce security going forward. What is the NIST Cybersecurity Framework 2.0? The NIST Cybersecurity Framework, first launched in 2014, was developed after an executive order by President Obama, aiming to provide a standardized set of guidelines to improve cybersecurity across critical infrastructure. The framework’s objectives include: The newly updated NIST CSF 2.0, released in 2024, expands on the original framework, providing organizations with structured, yet flexible, guidance for managing cybersecurity risks. It revolves around three core components: the CSF Core, CSF Profiles, and CSF Tiers. Key Components of NIST Cybersecurity Framework 2.0 These components help organizations understand, assess, and improve their cybersecurity posture, forming the basis for risk-informed strategies that align with organizational needs and the evolving threat landscape. Key Updates in the NIST Cybersecurity Framework 2.0 and Their Impact on Salesforce Security The 2024 updates to NIST CSF offer insights that Salesforce security leaders can use to align their strategies with evolving cybersecurity risks. Implementation Strategies for Salesforce Security Leaders To incorporate CSF 2.0 into Salesforce security operations, leaders should: Conclusion: Embracing NIST CSF 2.0 to Strengthen Salesforce Security The 2024 NIST Cybersecurity Framework updates offer crucial insights for Salesforce security leaders. By adopting these practices, organizations can enhance data protection, strengthen incident response capabilities, and ensure business continuity—critical for those relying on Salesforce for managing sensitive customer data. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
Data quality has never been more critical, and it’s only set to grow in importance with each passing year. The reason? The rise of AI—particularly generative AI. Generative AI offers transformative benefits, from vastly improved efficiency to the broader application of data in decision-making. But these advllucantages hinge on the quality of data feeding the AI. For enterprises to fully capitalize on generative AI, the data driving models and applications must be accurate. If the data is flawed, so are the AI’s outputs. Generative AI models require vast amounts of data to produce accurate responses. Their outputs aren’t based on isolated data points but on aggregated data. Even if the data is high-quality, an insufficient volume could result in an incorrect output, known as an AI hallucination. With so much data needed, automating data pipelines is essential. However, with automation comes the challenge: humans can’t monitor every data point along the pipeline. That makes it imperative to ensure data quality from the outset and to implement output checks along the way, as noted by David Menninger, an analyst at ISG’s Ventana Research. Ignoring data quality when deploying generative AI can lead to not just inaccuracies but biased or even offensive outcomes. “As we’re deploying more and more generative AI, if you’re not paying attention to data quality, you run the risks of toxicity, of bias,” Menninger warns. “You’ve got to curate your data before training the models and do some post-processing to ensure the quality of the results.” Enterprises are increasingly recognizing this, with leaders like Saurabh Abhyankar, chief product officer at MicroStrategy, and Madhukar Kumar, chief marketing officer at SingleStore, noting the heightened emphasis on data quality, not just in terms of accuracy but also security and transparency. The rise of generative AI is driving this urgency. Generative AI’s potential to lower barriers to analytics and broaden access to data has made it a game-changer. Traditional analytics tools have been difficult to master, often requiring coding skills and data literacy training. Despite efforts to simplify these tools, widespread adoption has been limited. Generative AI, however, changes the game by enabling natural language interactions, making it easier for employees to engage with data and derive insights. With AI-powered tools, the efficiency gains are undeniable. Generative AI can take on repetitive tasks, generate code, create data pipelines, and even document processes, allowing human workers to focus on higher-level tasks. Abhyankar notes that this could be as transformational for knowledge workers as the industrial revolution was for manual labor. However, this potential is only achievable with high-quality data. Without it, AI-driven decision-making at scale could lead to ethical issues, misinformed actions, and significant consequences, especially when it comes to individual-level decisions like credit approvals or healthcare outcomes. Ensuring data quality is challenging, but necessary. Organizations can use AI-powered tools to monitor data quality, detect irregularities, and alert users to potential issues. However, as advanced as AI becomes, human oversight remains critical. A hybrid approach, where technology augments human expertise, is essential for ensuring that AI models and applications deliver reliable outputs. As Kumar of SingleStore emphasizes, “Hybrid means human plus AI. There are things AI is really good at, like repetition and automation, but when it comes to quality, humans are still better because they have more context.” Ultimately, while AI offers unprecedented opportunities, it’s clear that data quality is the foundation. Without it, the risks are too great, and the potential benefits could turn into unintended consequences. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
Over the past decade, enterprises have accumulated vast amounts of data, capturing everything from business processes to inventory statistics. This surge in data marked the onset of the big data revolution. However, merely storing and managing big data is no longer sufficient to extract its full value. As organizations become adept at handling big data, forward-thinking companies are now leveraging advanced analytics and the latest AI and machine learning techniques to unlock even greater insights. These technologies can identify patterns and provide cognitive capabilities across vast datasets, enabling organizations to elevate their data analytics to new levels. Additionally, the adoption of generative AI systems is on the rise, offering more conversational approaches to data analysis and enhancement. This allows organizations to extract significant insights from information that would otherwise remain untapped in data stores. How Are AI and Big Data Related? Applying machine learning algorithms to big data is a logical progression for companies aiming to maximize the potential of their data. Unlike traditional rules-based approaches that follow explicit instructions, machine learning systems use data-driven algorithms and statistical models to analyze and detect patterns in data. Big data serves as the raw material for these systems, which derive valuable insights from it. Organizations are increasingly recognizing the benefits of integrating big data with machine learning. However, to fully harness the power of both, it’s crucial to understand their individual capabilities. Understanding Big Data Big data involves extracting and analyzing information from large quantities of data, but volume is just one aspect. Other critical “Vs” of big data that enterprises must manage include velocity, variety, veracity, validity, visualization, and value. Understanding Machine Learning Machine learning, the backbone of modern AI, adds significant value to big data applications by deriving deeper insights. These systems learn and adapt over time without the need for explicit programming, using statistical models to analyze and infer patterns from data. Historically, companies relied on complex, rules-based systems for reporting, which often proved inflexible and unable to cope with constant changes. Today, machine learning and deep learning enable systems to learn from big data, enhancing decision-making, business intelligence, and predictive analysis. The strength of machine learning lies in its ability to discover patterns in data. The more data available, the more these algorithms can identify patterns and apply them to future data. Applications range from recommendation systems and anomaly detection to image recognition and natural language processing (NLP). Categories of Machine Learning Algorithms Machine learning algorithms generally fall into three categories: The most powerful large language models (LLMs), which underpin today’s widely used generative AI systems, utilize a combination of these methods, learning from massive datasets. Understanding Generative AI Generative AI models are among the most powerful and popular AI applications, creating new data based on patterns learned from extensive training datasets. These models, which interact with users through conversational interfaces, are trained on vast amounts of internet data, including conversations, interviews, and social media posts. With pre-trained LLMs, users can generate new text, images, audio, and other outputs using natural language prompts, without the need for coding or specialized models. How Does AI Benefit Big Data? AI, combined with big data, is transforming businesses across various sectors. Key benefits include: Big Data and Machine Learning: A Synergistic Relationship Big data and machine learning are not competing concepts; when combined, they deliver remarkable results. Emerging big data techniques offer powerful ways to manage and analyze data, while machine learning models extract valuable insights from it. Successfully handling the various “Vs” of big data enhances the accuracy and power of machine learning models, leading to better business outcomes. The volume of data is expected to grow exponentially, with predictions of over 660 zettabytes of data worldwide by 2030. As data continues to amass, machine learning will become increasingly reliant on big data, and companies that fail to leverage this combination will struggle to keep up. Examples of AI and Big Data in Action Many organizations are already harnessing the power of machine learning-enhanced big data analytics: Conclusion The integration of AI and big data is crucial for organizations seeking to drive digital transformation and gain a competitive edge. As companies continue to combine these technologies, they will unlock new opportunities for personalization, efficiency, and innovation, ensuring they remain at the forefront of their industries. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
Salesforce Enhances Nonprofit Cloud with Generative AI Capabilities On August 6, 2024, Salesforce announced that its Nonprofit Cloud is now equipped with generative AI capabilities powered by the Einstein 1 Platform. This integration represents the first time Salesforce’s Industry Cloud portfolio has incorporated the Einstein 1 Platform, signaling a broader commitment to embedding AI tools across its product offerings. The update aims to revolutionize nonprofit operations by providing AI-powered tools for personalized donor engagement, operational efficiency, and funding discovery. Key features include AI-generated fundraising proposals and program summaries, which provide concise insights into grant details, donor histories, and program outcomes. Transforming Nonprofit Operations with AI The integration of generative AI into Nonprofit Cloud aligns with Salesforce’s strategy to empower nonprofits to navigate challenges such as donor fatigue, increased operational costs, and rising service demands. Notable enhancements include: Additionally, Salesforce launched Data Cloud for Nonprofits, enabling a unified, real-time view of donor, volunteer, and program data. This innovation breaks down data silos, empowering nonprofits to create tailored outreach strategies and assess program performance effectively. Four Pillars of AI Success Salesforce’s enhancements to Nonprofit Cloud embody its “four-pillar” approach to enterprise AI success: Key Innovations in Nonprofit Cloud Salesforce introduced three groundbreaking innovations to address nonprofit-specific challenges: These features, coupled with Nonprofit Cloud Einstein 1 Edition (which bundles Nonprofit Cloud, Data Cloud, Einstein, Experience Cloud, and Slack), provide nonprofits with comprehensive tools to drive impact. Nonprofit Adoption and Impact Nonprofits are already experiencing the transformative potential of AI. According to Salesforce’s Nonprofit Trends Report, organizations leveraging these AI tools have seen: Julie Fleshman, CEO of the Pancreatic Cancer Action Network, shared her organization’s success with Nonprofit Cloud: “Salesforce has been instrumental in helping us connect patients with specialized healthcare providers and clinical trials, advancing our mission and saving valuable time.” Nonprofit Cloud vs. NPSP While Nonprofit Cloud offers a unified, scalable platform with AI-driven insights and advanced donor management tools, the Nonprofit Success Pack (NPSP) serves as a free, open-source solution for smaller organizations. Here’s a quick comparison: Feature Nonprofit Cloud NPSP Functionality Comprehensive CRM with advanced tools Free app with basic CRM functionality Integration Seamless with other Salesforce products Requires additional configuration Ease of Use User-friendly and designed for nonprofits May require technical expertise Cost Subscription-based Free with optional paid add-ons Scalability Built for growing organizations Requires customization for growth Ideal Users Large and mid-sized nonprofits Small nonprofits Maximizing Fundraising with Nonprofit Cloud Nonprofit Cloud offers nonprofits flexibility and efficiency in managing their fundraising efforts, helping them overcome challenges like donor fatigue and retention. Its advanced features include: By leveraging these tools, nonprofits can improve engagement, strengthen donor relationships, and make data-driven decisions, ultimately amplifying their impact. The Tectonic Role Tectonic has been instrumental in implementing Salesforce Nonprofit Cloud for multiple organizations, ensuring they harness its full potential to optimize operations, engage donors, and achieve their missions. With Salesforce’s AI-driven enhancements and Tectonic’s expertise, nonprofits are poised to navigate challenges, unlock new opportunities, and amplify their societal impact. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
Editor’s Note: AI Cloud, Einstein GPT, and other cloud GPT products are now Einstein. For the latest on Salesforce Einstein The Rise of Generative AI: What It Means for Business and CRM Generative artificial intelligence (AI) made headlines in late 2022, sparking widespread curiosity and questions about its potential impact on various industries. What is Generative AI? Generative AI is a technology that creates new content—such as poetry, emails, images, or music—based on a set of input data. Unlike traditional AI, which focuses on classifying or predicting, generative AI can produce novel content with a human-like understanding of language, as noted by Salesforce Chief Scientist Silvio Savarese. However, successful generative AI depends on the quality of the input data. “AI is only as good as the data you give it, and you must ensure that datasets are representative,” emphasizes Paula Goldman, Salesforce’s Chief Ethical and Humane Use Officer. How Does Generative AI Work? Generative AI can be developed using several deep learning approaches, including: Other methods include Variational Autoencoders (VAEs) and Neural Radiance Fields (NeRFs), which generate new data or create 2D and 3D images based on sample data. Generative AI and Business Generative AI has captured the attention of global business leaders. A recent Salesforce survey found that 67% of IT leaders are focusing on generative AI in the next 18 months, with 33% considering it a top priority. Salesforce has long been exploring generative AI applications. For instance, CodeGen helps transform simple English prompts into executable code, and LAVIS makes language-vision AI accessible to researchers. More recently, Salesforce’s ProGen project demonstrated the creation of novel proteins using AI, potentially advancing medicine and treatment development. Ketan Karkhanis, Salesforce’s Executive VP and GM of Sales Cloud, highlights that generative AI benefits not just large enterprises but also small and medium-sized businesses (SMBs) by automating proposals, customer communications, and predictive sales modeling. Challenges and Ethical Considerations Despite its potential, generative AI poses risks, as noted by Paula Goldman and Kathy Baxter of Salesforce’s Ethical AI practice. They stress the importance of responsible innovation to ensure that generative AI is used safely and ethically. Accuracy in AI recommendations is crucial, and the authoritative tone of models like ChatGPT can sometimes lead to misleading results. Salesforce is committed to building trusted AI with embedded guardrails to prevent misuse. As generative AI evolves, it’s vital to balance its capabilities with ethical considerations, including its environmental impact. Generative AI can increase IT energy use, which 71% of IT leaders acknowledge. Generative AI at Salesforce Salesforce has integrated AI into its platform for years, with Einstein AI providing billions of daily predictions to enhance sales, service, and customer understanding. The recent launch of Einstein GPT, the world’s first generative AI for CRM, aims to transform how businesses interact with customers by automating content creation across various functions. Salesforce Ventures is also expanding its Generative AI Fund to $500 million, supporting AI startups and fostering responsible AI development. This expansion includes investments in companies like Anthropic and Cohere. As Salesforce continues to lead in AI innovation, the focus remains on creating technology that is inclusive, responsible, and sustainable, paving the way for the future of CRM and business. The Future of Business: AI-Powered Leadership and Decision-Making Tomorrow’s business landscape will be transformed by specialized, autonomous AI agents that will significantly change how companies are run. Future leaders will depend on these AI agents to support and enhance their teams, with AI chiefs of staff overseeing these agents and harnessing their capabilities. New AI-powered tools will bring businesses closer to their customers and enable faster, more informed decision-making. This shift is not just a trend—it’s backed by significant evidence. The Slack Workforce Index reveals a sevenfold increase in leaders seeking to integrate AI tools since September 2023. Additionally, Salesforce research shows that nearly 80% of global workers are open to an AI-driven future. While the pace of these changes may vary, it is clear that the future of work will look vastly different from today. According to the Slack Workforce Index, the number of leaders looking to integrate AI tools into their business has skyrocketed 7x since September 2023. Mick Costigan, VP, Salesforce Futures In the [still] early phases of a major technology shift, we tend to over-focus on the application of technology innovations to existing workflows. Such advances are important, but closing the imagination gap about the possible new shapes of work requires us to consider more than just technology. It requires us to think about people, both as the customers who react to new offerings and as the employees who are responsible for delivering them. Some will eagerly adopt new technology. Others will resist and drag their feet. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
Unlock the Full Potential of Your Salesforce Data with Snowflake At Tectonic, we’ve dedicated years to helping businesses maximize their Salesforce investment, driving growth and enhancing customer experiences. Now, we’re expanding those capabilities by integrating with Snowflake.Imagine the power of merging Salesforce data with other sources, gaining deeper insights, and making smarter decisions—without the hassle of complex infrastructure. Snowflake brings this to life with a flexible, scalable solution for unifying your data ecosystem.In this insight, we’ll cover why Snowflake is essential for Salesforce users, how seamlessly it integrates, and why Tectonic is the ideal partner to help you leverage its full potential. Why Snowflake Matters for Salesforce Users Salesforce excels at managing customer relationships, but businesses today need data from multiple sources—e-commerce, marketing platforms, ERP systems, and more. That’s where Snowflake shines. With Snowflake, you can unify these data sources, enrich your Salesforce data, and turn it into actionable insights. Say goodbye to silos and blind spots. Snowflake is easy to set up, scales effortlessly, and integrates seamlessly with Salesforce, making it ideal for enhancing CRM data across various business functions.The Power of Snowflake for Salesforce Users Enterprise-Grade Security & GovernanceSnowflake ensures that your data is secure and compliant. With top-tier security and data governance tools, your customer data remains protected and meets regulatory requirements across platforms, seamlessly integrating with Salesforce. Cross-Cloud Data SharingSnowflake’s Snowgrid feature makes it easy for Salesforce users to share and collaborate on data across clouds. Teams across marketing, sales, and operations can access the same up-to-date information, leading to better collaboration and faster, more informed decisions. Real-Time Data ActivationCombine Snowflake’s data platform with Salesforce Data Cloud to activate insights in real-time, enabling enriched customer experiences through dynamic insights from web interactions, purchase history, and service touchpoints. Tectonic + Snowflake: Elevating Your Salesforce Experience Snowflake offers powerful data capabilities, but effective integration is key to realizing its full potential—and that’s where Tectonic excels. Our expertise in Salesforce, now combined with Snowflake, ensures that businesses can maximize their data strategies. How Tectonic Helps: Strategic Integration Planning: We assess your current data ecosystem and design a seamless integration between Salesforce and Snowflake to unify data without disrupting operations. Custom Data Solutions: From real-time dashboards to data enrichment workflows, we create solutions tailored to your business needs. Ongoing Support and Optimization: Tectonic provides continuous support, adapting your Snowflake integration to meet evolving data needs and business strategies. Real-World Applications Retail: Integrate in-store and e-commerce sales data with Salesforce for real-time customer insights. Healthcare: Unify patient data from wearables, EMRs, and support interactions for a holistic customer care experience. Financial Services: Enhance Salesforce data with third-party risk assessments, enabling quicker, more accurate underwriting. Looking Ahead: The Tectonic Advantage Snowflake opens up new possibilities for Salesforce-powered businesses. Effective integration, however, requires strategic planning and hands-on expertise. Tectonic has a long-standing track record of helping clients get the most out of Salesforce, and now, Snowflake adds an extra dimension to our toolkit. Whether you want to better manage data, unlock insights, or enhance AI initiatives, Tectonic’s combined Salesforce and Snowflake expertise ensures you’ll harness the best of both worlds. Stay tuned as we dive deeper into Snowflake’s features, such as Interoperable Storage, Elastic Compute, and Cortex AI with Arctic, and explore how Tectonic is helping businesses unlock the future of data and AI. Ready to talk about how Snowflake and Salesforce can transform your business? Contact Tectonic today! Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
On Wednesday, GitHub announced the general availability of Copilot Autofix, an AI-driven tool designed to identify and remediate software vulnerabilities. Originally unveiled in March and tested in public beta, Copilot Autofix integrates GitHub’s CodeQL scanning engine with GPT-4, heuristics, and Copilot APIs to generate code suggestions for developers. The tool provides prompts based on CodeQL analysis and code snippets, allowing users to accept, edit, or reject the suggestions. In a blog post, Mike Hanley, GitHub’s Chief Security Officer and Senior Vice President of Engineering, highlighted the challenges developers and security teams face in addressing existing vulnerabilities. “Code scanning tools can find vulnerabilities, but the real issue is remediation, which requires security expertise and time—both of which are in short supply,” Hanley noted. “The problem isn’t finding vulnerabilities; it’s fixing them.” According to GitHub, the private beta of Copilot Autofix showed that users could respond to a CodeQL alert and automatically remediate a vulnerability in a pull request in just 28 minutes on average, compared to 90 minutes for manual remediation. The tool was even faster for common vulnerabilities like cross-site scripting, with remediation times averaging 22 minutes compared to three hours manually, and SQL injection flaws, which were fixed in 18 minutes on average versus almost four hours manually. Hanley likened the efficiency of Copilot Autofix in fixing vulnerabilities to the speed at which GitHub Copilot, their generative AI coding assistant released in 2022, produces code for developers. However, there have been concerns that GitHub Copilot and similar AI coding assistants could replicate existing vulnerabilities in the codebases they help generate. Industry analyst Katie Norton from IDC noted that while the replication of vulnerabilities is concerning, the rapid pace at which AI coding assistants generate new software could pose a more significant security issue. Chris Wysopal, CTO and co-founder of Veracode, echoed this concern, pointing out that faster coding speeds have led to more software being produced and a larger backlog of vulnerabilities for developers to manage. Norton also emphasized that AI-powered tools like Copilot Autofix could help alleviate the burden on developers by reducing these backlogs and enabling them to fix vulnerabilities without needing to be security experts. Other vendors, including Mobb and Snyk, have also developed AI-powered autoremediation tools. Initially supporting JavaScript, TypeScript, Java, and Python during its public beta, Copilot Autofix now also supports C#, C/C++, Go, Kotlin, Swift, and Ruby. Hanley also highlighted that Copilot Autofix would benefit the open-source software community. GitHub has previously provided open-source maintainers with free access to enterprise security tools for code scanning, secret scanning, and dependency management. Starting in September, Copilot Autofix will also be made available for free to these maintainers. “As the global home of the open-source community, GitHub is uniquely positioned to help maintainers detect and remediate vulnerabilities, making open-source software safer and more reliable for everyone,” Hanley said. Copilot Autofix is now available to all GitHub customers globally. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
