From smarter search engines to AI-powered creative workflows, vectors are the bridge between unstructured data and actionable intelligence. By fully leveraging this technology, businesses can transform their approach to data and secure their place in a rapidly evolving, data-centric world.
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 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
Essential Technologies for Value-Based Care Success As healthcare providers increasingly adopt value-based care, they must invest in the right technologies and resources to succeed in this model, which incentivizes high-quality, cost-effective care. Value-Based Care Technologies tie reimbursement to care quality, making providers accountable for patient outcomes while providing resources to enhance care. As of 2021, nearly 60% of healthcare payments were already tied to value-based models, according to the Health Care Payment Learning and Action Network (HCP LAN). While partnerships can initiate value-based care, providers must invest in the right technology to fully achieve the intended outcomes. Health Information Exchange (HIE) A robust health information exchange (HIE) is fundamental to value-based care, as it enables providers and payers to access high-quality data seamlessly. HIE allows healthcare professionals to share patients’ medical information electronically across organizations, promoting care coordination by giving providers a comprehensive view of patient needs. For patients, HIE enables more informed involvement in their care by making their health data accessible across specialists, labs, and pharmacies. While joining an HIE may involve new technology investments and workflow adjustments, it ultimately enhances provider access to critical health data. Population Health Management Tools Population health management tools help providers assess health outcomes within groups rather than focusing on individuals alone. These tools aggregate and analyze data, allowing practices to identify high-risk patients and create targeted interventions. This not only enhances health outcomes but can also reduce costs by avoiding expensive treatments. Patient engagement tools, such as telehealth and remote patient monitoring, are essential in population health management, especially for monitoring high-risk patients when in-person care is not feasible. Digital surveys integrated within patient portals can provide insights into social determinants of health, adding a broader context to patient needs. Data Analytics Data analytics transform healthcare data into actionable insights across four types: descriptive, diagnostic, predictive, and prescriptive. Providers can use these analytics to reduce hospital readmissions, predict diseases, and identify chronic illnesses. Data integration and risk stratification capabilities are especially valuable in value-based care, enabling providers to track patient health outcomes effectively and prioritize high-risk cases. Artificial Intelligence & Machine Learning AI and machine learning support many data analytics functions, helping identify patient needs and easing administrative burdens. Given staffing shortages and burnout—reported by 63% of physicians in 2021, according to the American Medical Association (AMA)—AI can automate tasks like documentation, charting, and scheduling, allowing providers to focus more on patient care. Additionally, AI-driven automation in revenue cycle management tasks, such as billing and coding, can reduce the administrative workload associated with value-based care. Price Transparency Technology Price transparency empowers patients to seek cost-effective care, a core principle of value-based models. When providers comply with transparency regulations, patients can better understand their costs and make informed decisions. For providers, leveraging price transparency tools ensures compliance and facilitates partnerships with payers by enabling more effective negotiation, which supports the overall goals of value-based care. As healthcare continues shifting to value-based models, investing in these technologies is critical for providers aiming for long-term success. While these tools rdo equire substantial investment, they are essential for improving patient outcomes, optimizing care quality, and ensuring sustainability in value-based care. When evaluating and choosing healthcare technology tools, contact Tectonic for help. 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
Successfully adopting AI requires thoughtful planning and a focus on human impact. While the pressure to leverage AI is immense across industries, the path to transforming its potential into meaningful outcomes is less clear. Businesses must address critical questions: What impact do we aim to achieve? Are we prepared for the organizational changes AI will bring? Mark Wakelin, Executive Vice President of Global Professional Services at Salesforce, emphasizes the importance of understanding the “why” behind adopting AI. “You need a clear vision of the impact you want to have and the use cases you’ll deploy,” he explains. A Readiness Checklist Before diving into AI initiatives, organizations must evaluate their readiness. This involves: “This isn’t just a technology equation,” Wakelin notes. “AI is also a legal, ethical, and humanitarian equation. It has the potential to significantly impact humanity, and we need to approach it within the context of workforce operations.” Linking AI to Business Value A common mistake in AI strategies is failing to align initiatives with tangible business outcomes. Wakelin recalls an engineer boasting about processing billions of images with AI but unable to articulate its business application. Companies must start by identifying where AI can have the greatest impact: Trust as the Foundation For AI to succeed, trust must be at the core of its implementation. This includes: “Trust is earned through predictable, integrity-driven behaviors,” says Wakelin. Unlike humans, machines lack relationships, so fostering trust within the ecosystem is crucial. Starting with People AI strategies should prioritize people, not technology. Wakelin stresses the need for transparency and proactive communication about AI implementation. This includes clear plans for: Partnering for Success Salesforce Partner Services supports organizations through this journey by: Reach out to Tectonic today to road map AI adoption for your organization. These steps help customers adopt AI thoughtfully, balancing opportunities with risks, and ensuring initiatives are controlled and trust-driven. A Vision for AI’s Future “AI is the most exciting development of my 35-year career,” Wakelin shares. He envisions AI enhancing productivity, education, and work-life balance while fostering diversity and equity. In the coming years, AI holds the promise of significantly improving society—provided organizations keep people at the center of its evolution. 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 Flow Tests: What Are the Limitations? Salesforce Flow Tests are essential for ensuring automation reliability, but they aren’t without their constraints. Recognizing these limitations is key to refining your automation strategy and avoiding potential roadblocks. Here’s an overview of common challenges, along with insights into how you can navigate them to maximize the effectiveness of your testing processes. The Role of Flow Tests in Automation Automated processes in Salesforce are powerful, but they don’t optimize themselves. Proper setup and rigorous testing are essential to ensure that your automations run smoothly. While Salesforce Flow Tests help verify functionality, they have inherent limitations that, if misunderstood, could lead to inefficiencies or rework. By understanding these boundaries, you can make informed decisions to strengthen your overall approach to testing and automation. Key Limitations of Salesforce Flow Tests Final Thoughts Mastering Salesforce Flow Tests means leveraging their strengths while acknowledging their constraints. Optimized automations require careful planning, robust testing, and a clear understanding of the tools’ boundaries. Have questions about improving your Salesforce Flows or testing strategy? Let’s chat and explore ways to fine-tune your automations! 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
If you’re ready to embrace these innovations, reach out to Tectonic for expert guidance on optimizing your Salesforce instance. Together, we can help your organization harness the full potential of these game-changing features.
GoTo Adds AI-Powered Integrations to GoTo Connect for Enhanced CRM Connectivity GoTo has introduced advanced AI-driven integrations between its GoTo Connect platform and major CRM systems to deliver seamless connectivity and improve customer experience (CX) across various channels. GoTo Connect’s newly integrated CRM platforms include Salesforce, HubSpot, Zoho, ServiceNow, MS Dynamics, Freshdesk, Zendesk, and more, enabling businesses to manage customer interactions more effectively. Enhanced Customer Relationship Management Olga Lagunova, Chief Product and Technology Officer at GoTo, emphasized the impact of these integrations on customer relationships:“Working across multiple systems can be inefficient and time-consuming, detracting from valuable customer service time. Our new GoTo Connect integrations enhance how businesses interact with customers by centralizing workflows within the platform,” Lagunova noted. “Our AI capabilities, like call summaries and recordings stored directly in CRMs, empower teams to work smarter within their preferred tools.” New AI-Driven Features With this update, GoTo Connect automatically generates and stores AI-based call summaries and transcriptions within CRM contact records. This streamlined process gives teams faster access to comprehensive customer profiles, reducing manual tasks and increasing efficiency. The system also stores call and message details automatically in the CRM, maintaining a full history of interactions to support a seamless customer journey. A recent Zendesk study cited by GoTo revealed that over 70% of customers are frustrated by needing to repeat information to different service agents. GoTo Connect addresses this by providing agents with a unified view of customer data and current conversations, helping reduce silos and improve service quality. Streamlined Workflows and Insights Beyond call summaries, GoTo Connect’s integration offers screen pop-ups displaying customer details for agents, contact syncing, click-to-call features, call and messaging logs, and voicemail transcriptions. These features help teams respond more promptly and accurately to customer inquiries. Expanding on GoTo Connect CX In related news, GoTo recently launched GoTo Connect CX, combining its virtual phone system with AI-powered tools to create enhanced, efficient CX solutions for businesses of all sizes, while reducing operational costs. The new integrations and GoTo Connect CX are now available to all GoTo Connect customers, reflecting GoTo’s commitment to unifying customer service tools for a better, more connected experience. 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
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 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
AI Inference vs. Training: Key Differences and Tradeoffs AI training and inference are the foundational phases of machine learning, each with distinct objectives and resource demands. Optimizing the balance between the two is crucial for managing costs, scaling models, and ensuring peak performance. Here’s a closer look at their roles, differences, and the tradeoffs involved. Understanding Training and Inference Key Differences Between Training and Inference 1. Compute Costs 2. Resource and Latency Considerations Strategic Tradeoffs Between Training and Inference Key Considerations for Balancing Training and Inference As AI technology evolves, hardware advancements may narrow the gap in resource requirements between training and inference. Nonetheless, the key to effective machine learning systems lies in strategically balancing the demands of both processes to meet specific goals and constraints. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce 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
Salesforce Unveils Agentforce Testing Center to Streamline AI Agent Lifecycle Management Salesforce has introduced the Agentforce Testing Center, a suite of tools designed to help enterprises test, deploy, and monitor autonomous AI agents in a secure and controlled environment. These innovations aim to support businesses adopting agentic AI, a transformative approach that enables intelligent systems to reason, act, and execute tasks on behalf of employees and customers. Agentforce Testing Center: A New Paradigm for AI Agent Deployment The Agentforce Testing Center offers several key capabilities to help businesses confidently deploy AI agents without risking disruptions to live production systems: Supporting a Limitless Workforce Adam Evans, EVP and GM for Salesforce AI Platform, emphasized the importance of these tools in accelerating the adoption of AI agents: “Agentforce is helping businesses create a limitless workforce. To deliver this value fast, CIOs need new tools for testing and monitoring agentic systems. Salesforce is meeting the moment with Agentforce Testing Center, enabling companies to roll out trusted AI agents with no-code tools for testing, deploying, and monitoring in a secure, repeatable way.” From Testing to Deployment Once testing is complete, enterprises can seamlessly deploy their AI agents to production using Salesforce’s proprietary tools such as Change Sets, DevOps Center, and the Salesforce CLI. Additionally, the Digital Wallet feature offers transparent usage monitoring, allowing teams to track consumption and optimize resources throughout the AI development lifecycle. Customer and Analyst Perspectives Shree Reddy, CIO of PenFed, praised the potential of Agentforce and Data Cloud Sandboxes: “By enabling rigorous pre-deployment testing, we can deliver faster, more accurate support and recommendations to our members, aligning with our commitment to financial well-being.” Keith Kirkpatrick, Research Director at The Futurum Group, highlighted the broader implications: “Salesforce is instilling confidence in AI adoption by testing hundreds of variations of agent interactions in parallel. These enhancements make it easier for businesses to pressure-test autonomous systems and ensure reliability.” Availability With these tools, Salesforce solidifies its leadership in the agentic AI space, empowering enterprises to adopt AI systems with confidence and transform their operations at scale. 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
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 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
As organizations embrace the growing presence of AI agents, leaders must address concerns about allowing autonomous systems to operate in sensitive environments. AI agents, often viewed as the future of how enterprises deploy large language models, raise important questions around security and identity management. The rise of agentic AI has been notable in 2024, with Google launching its Vertex AI Agents, Salesforce introducing Agentforce, and AWS rolling out the re Agent for Amazon Bedrock. These agents promise to deliver significant value by executing tasks using natural language commands, reasoning through the best solutions, and taking action without human intervention. However, as Katie Norton, research manager for DevSecOps & Software Supply Chain Security at IDC, highlighted at Venafi’s Machine Identity Conference, AI agents present unique security challenges. Unlike robotic process automation (RPA), AI agents act autonomously, creating a need for secure machine identities, especially as they access sensitive data across multiple systems. Matt McLarty, CTO at Boomi, added that the complexity of managing agentic AI revolves around ensuring proper authentication and authorization. He pointed out scenarios where agents dynamically interact with systems, such as opening support tickets, which require secure verification of agent access rights. While these agents offer significant potential, businesses are not yet prepared to issue credentials for autonomous agents, according to McLarty. The current reliance on existing authentication and authorization systems needs to evolve to support these new AI capabilities. He also emphasized the importance of pairing agents with human oversight, ensuring that access and actions are traceable. As AI advances into its third wave, characterized by autonomous agents capable of reasoning and action, companies need to rethink their approaches to workforce collaboration. These agents will handle low-value, time-consuming tasks, while human workers focus on strategic initiatives. In sales, for example, AI agents will manage customer interactions, schedule meetings, and resolve basic issues, allowing salespeople to build deeper relationships. At Dreamforce 2024, Salesforce unveiled Agentforce, a platform that empowers organizations to build and deploy customized AI agents across service, sales, marketing, and commerce. This suite aims to increase efficiency, productivity, and customer satisfaction. However, for AI agents to succeed, they must complement human skills and operate within established guardrails. Organizations need to implement audit trails to ensure accountability and develop training programs for employees to effectively collaborate with AI. Ultimately, the future of work will feature a hybrid workforce where humans and AI agents work together to drive innovation and success. As companies move forward, they must ensure AI agents understand their limits and recognize when human intervention is necessary. This balance between AI-driven efficiency and human oversight will enable businesses to thrive in an ever-evolving 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 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 AI Productivity Paradox: Why Aren’t More Workers Using AI Tooks Like ChatGPT?The Real Barrier Isn’t Technical Skills — It’s Time to Think Despite the transformative potential of tools like ChatGPT, most knowledge workers aren’t utilizing them effectively. Those who do tend to use them for basic tasks like summarization. Less than 5% of ChatGPT’s user base subscribes to the paid Plus version, indicating that a small fraction of potential professional users are tapping into AI for more complex, high-value tasks. Having spent over a decade building AI products at companies such as Google Brain and Shopify Ads, the evolution of AI has been clearly evident. With the advent of ChatGPT, AI has transitioned from being an enhancement for tools like photo organizers to becoming a significant productivity booster for all knowledge workers. Most executives are aware that today’s buzz around AI is more than just hype. They’re eager to make their companies AI-forward, recognizing that it’s now more powerful and user-friendly than ever. Yet, despite this potential and enthusiasm, widespread adoption remains slow. The real issue lies in how organizations approach work itself. Systemic problems are hindering the integration of these tools into the daily workflow. Ultimately, the question executives need to ask isn’t, “How can we use AI to work faster? Or can this feature be built with AI?” but rather, “How can we use AI to create more value? What are the questions we should be asking but aren’t?” Real-world ImpactRecently, large language models (LLMs)—the technology behind tools like ChatGPT—were used to tackle a complex data structuring and analysis task. This task would typically require a cross-functional team of data analysts and content designers, taking a month or more to complete. Here’s what was accomplished in just one day using Google AI Studio: However, the process wasn’t just about pressing a button and letting AI do all the work. It required focused effort, detailed instructions, and multiple iterations. Hours were spent crafting precise prompts, providing feedback, and redirecting the AI when it went off course. In this case, the task was compressed from a month-long process to a single day. While it was mentally exhausting, the result wasn’t just a faster process—it was a fundamentally better and different outcome. The LLMs uncovered nuanced patterns and edge cases within the data that traditional analysis would have missed. The Counterintuitive TruthHere lies the key to understanding the AI productivity paradox: The success in using AI was possible because leadership allowed for a full day dedicated to rethinking data processes with AI as a thought partner. This provided the space for deep, strategic thinking, exploring connections and possibilities that would typically take weeks. However, this quality-focused work is often sacrificed under the pressure to meet deadlines. Ironically, most people don’t have time to figure out how they could save time. This lack of dedicated time for exploration is a luxury many product managers (PMs) can’t afford. Under constant pressure to deliver immediate results, many PMs don’t have even an hour for strategic thinking. For many, the only way to carve out time for this work is by pretending to be sick. This continuous pressure also hinders AI adoption. Developing thorough testing plans or proactively addressing AI-related issues is viewed as a luxury, not a necessity. This creates a counterproductive dynamic: Why use AI to spot issues in documentation if fixing them would delay launch? Why conduct further user research when the direction has already been set from above? Charting a New Course — Investing in PeopleProviding employees time to “figure out AI” isn’t enough; most need training to fully understand how to leverage ChatGPT beyond simple tasks like summarization. Yet the training required is often far less than what people expect. While the market is flooded with AI training programs, many aren’t suitable for most employees. These programs are often time-consuming, overly technical, and not tailored to specific job functions. The best results come from working closely with individuals for brief periods—10 to 15 minutes—to audit their current workflows and identify areas where LLMs could be used to streamline processes. Understanding the technical details behind token prediction isn’t necessary to create effective prompts. It’s also a myth that AI adoption is only for those with technical backgrounds under 40. In fact, attention to detail and a passion for quality work are far better indicators of success. By setting aside biases, companies may discover hidden AI enthusiasts within their ranks. For example, a lawyer in his sixties, after just five minutes of explanation, grasped the potential of LLMs. By tailoring examples to his domain, the technology helped him draft a law review article he had been putting off for months. It’s likely that many companies already have AI enthusiasts—individuals who’ve taken the initiative to explore LLMs in their work. These “LLM whisperers” could come from any department: engineering, marketing, data science, product management, or customer service. By identifying these internal innovators, organizations can leverage their expertise. Once these experts are found, they can conduct “AI audits” of current workflows, identify areas for improvement, and provide starter prompts for specific use cases. These internal experts often better understand the company’s systems and goals, making them more capable of spotting relevant opportunities. Ensuring Time for ExplorationBeyond providing training, it’s crucial that employees have the time to explore and experiment with AI tools. Companies can’t simply tell their employees to innovate with AI while demanding that another month’s worth of features be delivered by Friday at 5 p.m. Ensuring teams have a few hours a month for exploration is essential for fostering true AI adoption. Once the initial hurdle of adoption is overcome, employees will be able to identify the most promising areas for AI investment. From there, organizations will be better positioned to assess the need for more specialized training. ConclusionThe AI productivity paradox is not about the complexity of the technology but rather how organizations approach work and innovation. Harnessing AI’s potential is simpler than “AI influencers” often suggest, requiring only
With all the buzz in the news about AI, it may feel like AI is everywhere. In fact, as of 2023, over 80% of global companies report adopting AI to enhance their business operations. This means if your company isn’t yet leveraging AI to strengthen customer relationships, you risk falling behind. The good news is that Salesforce CRM already comes with a suite of AI tools ready for use. In this insight, we’ll explore how combining quality data, AI, and Salesforce can help you build more meaningful, lasting relationships with your customers. How Does Salesforce Use AI? Salesforce offers various built-in functionalities to create customizable, predictive, and generative AI experiences tailored to your business needs. One standout tool is Agentforce, which enables the creation of autonomous AI agents. If you have numerous routine tasks but limited staff, Agentforce could be the solution. For instance, if you lack an in-house customer support agent, Agentforce can build an AI service agent to handle incoming cases, responding intuitively in real-time. Not enough sales reps? No problem—create an AI sales agent to manage records, interact with leads, answer questions, and schedule meetings. Another significant AI feature is generative AI in Salesforce. According to KPMG, 77% of executives believe generative AI will have a more profound societal impact in the next three to five years than any other emerging technology. So, how can it improve your business? Salesforce’s in-house LLM, xGen, helps you generate human-like text and create original visual content from existing data or user input. This capability can enhance user experiences by automating the generation of dynamic and personalized imagery for applications. Generative AI also transforms how users interact with and consume data. Complex datasets can now be converted into easily understandable formats—visualizations, charts, or graphs—generated from natural language prompts. These insights make data accessible, enabling users to share knowledge and drive informed decisions. How Can You Use AI to Improve Customer Relationships? AI is reshaping business models, workflows, and customer engagement. By harnessing quality data, AI, and Salesforce, you can enhance how you connect with customers. Here are key ways to leverage this combination for a smarter customer strategy: Challenges You May Encounter on Your AI Journey Adopting AI in Salesforce, especially Einstein AI, offers many benefits, but it also comes with challenges. Here are some factors to consider for a successful rollout: Importance of Data Quality When Using AI Analytics Data quality is essential for AI accuracy and reliability. Poor data can skew predictions and erode user trust. Key factors that contribute to high data quality include: AI can also enhance data quality by automating data validation and cleansing. Machine learning algorithms can detect and address anomalies, duplicate records, and incomplete datasets, improving the reliability of your data over time. The Future of CRM: AI-Driven Customer Engagement and Business Growth Integrating AI into Salesforce is revolutionizing CRM by enabling businesses to engage with customers more intelligently. From automating routine tasks to enhancing decision-making and delivering personalized communication, AI-driven innovations are empowering businesses to build stronger relationships with customers. As AI continues to evolve, those who embrace it will gain a competitive edge and drive long-term growth. The future of CRM is here—and it’s smarter, faster, and more customer-focused than ever. 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
While AI plays a crucial role in delivering personalized customer experiences, the real game-changer lies in data connectivity. It’s not about having the flashiest AI model but about how data is managed across every interaction. Imagine a global retail brand with a network of physical stores, an e-commerce site, and active social media. An in-store shopper “likes” a product on Instagram, triggering an autonomous agent to send a personalized discount code. This leads them to a tailored shopping page based on their preferences, with items in their size, style, and budget. After the purchase, the agent follows up to ensure satisfaction and offers discounts on complementary items, creating a VIP-like experience. While AI can automate these touchpoints, the real enabler is a robust data platform that brings each data point into a cohesive, connected system. Without this, AI’s effectiveness is limited; Gartner predicts that 30% of generative AI projects will be abandoned by 2025 due to poor data quality. Why Data – Not a Custom AI Model – Drives Results Many companies think they need a custom-trained language model (LLM) to achieve superior customer insights. However, training an LLM is costly, requires specialized expertise, and lacks built-in permission controls. And because LLMs require constant updates to stay relevant, it’s a significant commitment with diminishing returns. Instead, a more practical approach is to integrate a pre-trained LLM with a data platform that aggregates data from diverse systems in real time. By providing AI with current, connected data, companies can enhance accuracy, identify trends, and respond to customer needs quickly. The Power of Integrated Data Platforms Consider the shopper who “liked” a social media post and then interacted with an autonomous agent. Alone, these interactions may not signify much, but together they reveal a clear buying intent. Scaling this approach allows brands to spot valuable opportunities faster. Autonomous agents can help sales teams prioritize prospects, resolve service cases, and drive sales – all in real time. This isn’t limited to retail. In healthcare, agents can streamline patient benefits verification, summarize insurance details, and assist with pharmacy coverage. From finance to manufacturing, data-enabled AI agents have endless applications. Metadata: The Secret to Seamless AI For AI to deliver tailored, high-value interactions, it needs organized data – and metadata is key. By categorizing and tagging data, metadata enables AI to find the right information, whether drafting a customer message or identifying top sales prospects. A unified data platform that harmonizes data and metadata yields sharper predictions and more personalized outputs, outperforming siloed data systems. Data-Driven Precision in Customer Experience An effective data platform is the backbone of an agile, customer-centric business, making AI far more than guesswork. By using data to understand customer needs in real time, businesses can deliver targeted, timely experiences that build loyalty and growth. A comprehensive data strategy is essential to unlocking AI’s potential. This data strategy guide provides best practices for aligning data with business goals, unearthing insights, and enhancing customer engagement. A unified data platform connects your organization around the customer – transforming a simple “like” into a sale, and a sale into loyalty. Without it, your AI is just guessing. 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 Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more
