The new LOS will incorporate machine learning and automation to deliver real-time credit assessments, enabling faster loan processing and competitive interest rates, alongside improved credit risk insights. This strategic partnership underscores Mahindra Finance’s dedication to providing responsible financing solutions to India’s emerging MSME sector.
Salesforce Introduces Agentforce Accelerator to Empower Nonprofits Salesforce has unveiled the Salesforce Accelerator — Agents for Impact, a groundbreaking initiative aimed at helping nonprofits harness the power of Agentforce. This suite of AI-driven tools enables organizations to build and deploy autonomous AI agents that can perform critical tasks across various functions. Through a combination of technology, funding, and expertise, the accelerator aims to empower nonprofits to enhance operational efficiency and amplify their impact in an AI-driven future. Why It Matters Nonprofits often face challenges such as staffing shortages and burnout, limiting their ability to address pressing social and environmental issues. AI agents can play a transformative role by augmenting nonprofit teams, enabling them to: While the potential is significant, developing and implementing AI solutions often remains financially and technically inaccessible for many nonprofits. How the Accelerator Works The Salesforce Accelerator — Agents for Impact bridges this gap by providing a comprehensive support package: Nonprofits from all focus areas can apply for the accelerator starting October 29, 2024, with selected organizations notified by December. Track Record of Impact The Agents for Impact initiative builds on Salesforce’s broader accelerator program, which has provided million since 2022 to support innovative nonprofit solutions in areas like AI, education, and climate action. Scaling Nonprofit Potential With the launch of Salesforce Accelerator — Agents for Impact, nonprofits now have unprecedented opportunities to adopt AI-driven solutions that enhance efficiency and scale their missions. This program reflects Salesforce’s ongoing commitment to empowering organizations to drive meaningful change in an increasingly AI-powered world. 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
Automate LinkedIn Outreach with We-Connect’s New Salesforce Integration Sales and marketing teams can now streamline their LinkedIn outreach and lead management efforts with We-Connect’s powerful new integration for Salesforce, the world’s leading CRM platform. We-Connect, the premier LinkedIn automation tool, has officially launched its native integration with Salesforce, enabling seamless synchronization of contact data, campaign metrics, and outreach activity. This integration provides sales and marketing teams with a unified platform to manage all LinkedIn outreach efforts directly within Salesforce’s familiar interface. Transforming LinkedIn Outreach for Sales and Marketing Teams Traditionally, LinkedIn outreach happens outside CRM systems, leaving teams without a clear way to track campaign effectiveness. Sales reps often resort to manual searches on LinkedIn rather than leveraging data already housed in their CRM. The We-Connect and Salesforce integration revolutionizes this process by: Key Features of the Integration A Game-Changer for Outreach Efforts “Our new Salesforce integration brings LinkedIn outreach into a single, unified platform,” said Gary Egan, Product Manager at We-Connect. “With this integration, sales and marketing teams can stay aligned, act on real-time insights, and scale their outreach efforts like never before.” By consolidating LinkedIn activities within Salesforce, teams can better measure campaign performance, maintain a consistent buyer journey, and boost efficiency—all while leveraging Salesforce’s powerful CRM capabilities. For more details, visit the We-Connect Salesforce Integration page. About We-Connect Founded in 2018, We-Connect is the leading LinkedIn automation tool for sales, marketing, recruiting, and business professionals. Its advanced features help users automate LinkedIn interactions, connect with the right people, and generate high-quality leads effortlessly. We-Connect empowers professionals to build meaningful relationships, drive growth, and achieve their business goals with efficiency and precision. Learn more about how We-Connect transforms LinkedIn outreach at We-Connect.io. 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
Don’t get spooked – OpenAI introduces Canvas—a fresh interface for collaborative writing and coding with ChatGPT, designed to go beyond simple conversation. Canvas opens in a separate window, enabling you and ChatGPT to work on projects side by side, creating and refining ideas in real time. This early beta provides an entirely new way of collaborating with AI—combining conversation with the ability to edit and enhance content together. Built on GPT-4o, Canvas can be selected in the model picker during the beta phase. Starting today, we’re rolling it out to ChatGPT Plus and Team users globally, with Enterprise and Education users gaining access next week. Once out of beta, Canvas will be available to all ChatGPT Free users. Enhancing Collaboration with ChatGPT While ChatGPT’s chat interface works well for many tasks, projects requiring editing and iteration benefit from more. Canvas provides a workspace designed for such needs. Here, ChatGPT can better interpret your objectives, offering inline feedback and suggestions across entire projects—similar to a copy editor or code reviewer. You control every aspect in Canvas, from direct editing to leveraging shortcuts like adjusting text length, debugging code, or quickly refining writing. You can also revert to previous versions with Canvas’s back button. OpenAI Introduces Canvas Canvas opens automatically when ChatGPT detects an ideal scenario, or you can prompt it by typing “use Canvas” in your request to begin working collaboratively on an existing project. Writing Shortcuts Include: Coding in Canvas Canvas makes coding revisions more transparent, streamlining the iterative coding process. Track ChatGPT’s edits more clearly and take advantage of features that make debugging and revising code simpler. OpenAI Introduces Canvas to a world of new possibilities for truly developing and working with artificial intelligence. Coding Shortcuts Include: Training the Model to Collaborate GPT-4o has been optimized to act as a collaborative partner, understanding when to open a Canvas, make targeted edits, or fully rewrite content. Our team implemented core behaviors to support a seamless experience, including: These improvements are backed by over 20 internal automated evaluations and refined with synthetic data generation techniques, allowing us to enhance response quality and interaction without relying on human-generated data. Key Challenges as OpenAI Introduces Canvas A core challenge was determining when to trigger Canvas. We trained GPT-4o to recognize prompts like “Write a blog post about the history of coffee beans” while avoiding over-triggering for simple Q&A requests. For writing tasks, we reached an 83% accuracy in correct Canvas triggers, and a 94% accuracy in coding tasks compared to baseline models. Fine-tuning continues to ensure targeted edits are favored over full rewrites when needed. Finally, improving comment generation required iterative adjustments and human evaluations, with the integrated Canvas model now outperforming baseline GPT-4o in accuracy by 30% and quality by 16%. What’s Next Canvas is the first major update to ChatGPT’s visual interface since launch, with more enhancements planned to make AI more versatile and accessible. Canvas is also integrated with Salesforce. 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
Sales Incentives That Actually Work Spooktacular Sales incentives to kick off your Halloween. Sales Incentives That Work are an essential tool to encourage and reward desired behaviors within a sales team. They serve as a motivational driver, helping teams achieve their goals while reinforcing the company’s objectives. Incentivizing a team goes beyond merely showing up for work—it involves sparking enthusiasm and a genuine drive to exceed expectations. With the right incentive program, companies can energize their teams, ensuring they come to work motivated and ready to achieve results. Below is a guide on different types of sales incentives, their impact on performance, and best practices for creating an effective incentive program. What Are Sales Incentives? Sales incentives are rewards offered to sales representatives in addition to their base pay for exceptional performance, typically when they meet or exceed sales targets. While monetary rewards such as bonuses are common, sales incentives can take many forms, all aimed at motivating the sales force toward specific goals. Types of Sales Incentives There are five main categories of sales incentives, each tailored to motivate different roles and sales activities: How Sales Incentives Motivate a Team Sales incentives work by reinforcing the behaviors a company wants its team to prioritize. For highly competitive reps, the prospect of a reward adds an extra layer of motivation. For others who may not be as driven by competition, incentives provide tangible goals to strive toward. Moreover, well-structured incentive programs can enhance job satisfaction, decrease turnover, and ensure top performers stay motivated. 10 Effective Sales Incentives While monetary rewards are often the most straightforward incentive, incorporating a mix of creative rewards can enhance a company’s incentive program. Some ideas include: Best Practices for Implementing Sales Incentives A well-designed sales incentive program ensures both clarity and motivation. Some best practices for implementation include: Measuring the Impact of Sales Incentives To gauge the success of a sales incentive program, companies should track key performance indicators (KPIs) like overall sales, new and returning customer numbers, program costs versus ROI, employee turnover, and employee satisfaction. Monitoring these metrics ensures that the program aligns with both sales goals and employee satisfaction. Tectonic’s Final Thoughts Sales incentives are an effective way to drive performance and boost morale. By designing a simple, clear, and motivating incentive program, companies can align their sales goals with individual motivation, ensuring both business success and employee satisfaction. If you need assistance utilizing sales incentives and your Salesforce platform, 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
In 2023, Salesforce Admins created 230,000 objects, generated over 2.7 billion reports monthly, and performed 1 trillion AI predictions weekly. These figures highlight the critical role of an Admin in the Salesforce ecosystem. However, becoming an expert Salesforce Admin requires more than just technical know-how. A blend of both technical and soft skills is essential to truly excel in this role. Whether you’re new to the role or looking to enhance your skills, learning the right abilities is key to thriving as a Salesforce Admin. In this guide, we’ll cover the essential Salesforce Admin skills you need to effectively manage the platform and drive business success. Key Takeaways 14 Essential Salesforce Admin Skills to Master These are some of the key skills outlined in Salesforce’s “Admin Skills Kit” that will help you secure top Salesforce Admin positions. 1. Communication Strong communication skills are critical for Salesforce Admins, who act as the link between technical teams, stakeholders, and users. You’ll need to explain complex processes clearly so that all parties can understand. Why It Matters: Clear communication ensures users can effectively engage with the platform and make informed decisions based on Salesforce data. How To Improve: Practice simplifying technical concepts, actively listen to others’ needs, and refine your ability to convey information clearly. 2. Problem Solving Challenges will arise in your role, from system errors to user issues. Having strong problem-solving skills allows you to identify the root cause of issues and quickly implement effective solutions. Why It Matters: Efficient problem resolution minimizes downtime and keeps the Salesforce platform running smoothly. How To Improve: Break down problems into manageable parts, brainstorm solutions, and always evaluate the impact of your decisions. 3. Attention to Detail Salesforce admins deal with complex data and processes that require accuracy. From maintaining data integrity to configuring processes, attention to detail is crucial. Why It Matters: Even small errors can result in inaccurate data, security risks, or inefficient processes. How To Improve: Double-check your work, use Salesforce validation tools, and ensure all workflows are correctly configured before launching. 4. Learner’s Mindset Salesforce evolves constantly, with frequent updates and new features. A learner’s mindset helps you stay on top of these changes and continuously improve your skills. Why It Matters: The more you know, the more value you bring. Keeping up with updates ensures you’re using the latest tools to benefit your organization. How To Improve: Engage with Salesforce communities, complete Trailhead modules, and attend webinars to stay current. 5. User Management As an Admin, you’ll be responsible for managing users, creating profiles, setting roles and permissions, and ensuring proper access to data. Why It Matters: Proper user management boosts productivity while ensuring data security. How To Improve: Learn the ins and outs of Salesforce profiles, roles, and permission sets, and practice managing users in a sandbox environment. 6. Security Management In today’s digital age, data security is a top priority. Salesforce Admins are responsible for safeguarding organizational data from unauthorized access or breaches. Why It Matters: Poor security can lead to data leaks, damaging the company’s reputation and finances. How To Improve: Master security settings, understand field-level permissions, and stay informed on two-step verification and audit tracking. 7. Business Analysis Admins need to understand the business needs of their organization. Business analysis skills help you gather requirements, understand workflows, and tailor Salesforce to meet those needs. Why It Matters: The better you understand the business, the more effectively you can customize Salesforce to add value. How To Improve: Collaborate with stakeholders to identify pain points and design solutions that address specific business needs. 8. Data Analysis Working with large data sets is a regular part of being a Salesforce Admin. Knowing how to analyze data and generate insights is essential. Why It Matters: Data analysis drives informed decision-making, streamlines workflows, and improves communication. How To Improve: Familiarize yourself with Salesforce’s reporting tools, dashboards, and data export features. 9. Data Management Effective data management is critical to maintaining a well-functioning Salesforce system. This involves data transfers, cleaning, deduplication, and archiving. Why It Matters: Clean and organized data supports accurate reporting and better decision-making. How To Improve: Learn best practices for data imports, validation rules, and data maintenance tools like Data Loader. 10. Designer’s Mindset Admins with a designer’s mindset can create user-friendly interfaces and workflows that enhance the system’s usability. Why It Matters: A well-designed interface improves the user experience, making work more efficient and enjoyable. How To Improve: Use the Lightning App Builder to create custom page layouts and optimize user flows. 11. Change Management When implementing new processes or updates, effective change management is key. This includes communication, ensuring smooth transitions, and training users on new features. Why It Matters: Proper change management ensures high adoption rates and a smooth transition to new features or updates. How To Improve: Develop communication plans, conduct training, and gather user feedback during transitions. 12. Process Automation Salesforce’s automation capabilities allow admins to streamline repetitive tasks. Automation skills help you create workflows, approval processes, and automated communications. Why It Matters: Automating repetitive tasks saves time and ensures that critical business processes are followed consistently. How To Improve: Learn how to use Flows, Process Builder, and Workflow Rules to automate business operations. 13. Product Management A product management mindset helps Admins align the platform with users’ and stakeholders’ needs, ensuring Salesforce delivers value to the organization. Why It Matters: Meeting evolving business needs ensures you continue to add value as an Admin. How To Improve: Collect user feedback, prioritize requests, and align updates with overall business goals. 14. Project Management Salesforce Admins often lead projects such as implementing new features or migrating data. Strong project management skills are essential to execute these tasks effectively. Why It Matters: Good project management ensures that goals are met on time and within budget. How To Improve: Get comfortable with project management tools, scheduling, resource management, and stakeholder communication. Conclusion The role of a Salesforce Admin combines both technical and soft skills like communication,
How to Create a Winning AI Strategy for Your Business To maximize the value of AI, organizations must align their AI projects with strategic business objectives. Here’s a 10-step guide to crafting an effective AI strategy, including sample templates to support your planning. While AI adoption is on the rise, many companies still struggle to unlock its full potential. According to the 2024 IDC report Scaling AI Initiatives Responsibly, even organizations with advanced AI practices, termed “AI Masters,” face a 13% failure rate, while those still emerging in AI see a 20% failure rate. Challenges such as poor data quality and cultural resistance often contribute to these failures. To avoid these pitfalls, companies need to adopt a more deliberate and strategic approach to AI implementation. As Nick Kramer from SSA & Company states, “It’s not just about implementing the right technology; a lot of work needs to be done beforehand to succeed with AI.” What is an AI Strategy and Why is it Important? An AI strategy unifies all necessary components—such as data, technology, and talent—required to achieve business goals through AI. This includes: A well-designed AI strategy sets clear directions on how AI should be leveraged to achieve optimal outcomes within the organization. 10 Steps to Craft a Successful AI Strategy Resources for AI Strategy Templates If you’re ready to start building your AI strategy, here are several resources offering templates and guidance: By following these steps and utilizing the right resources, businesses can ensure they capture AI in ways that align with their strategic goals and maximize their competitive edge. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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
TrueDialog Unveils SMS Integration for Salesforce Marketing Cloud
TrueDialog, a leader in SMS texting solutions, has expanded its Salesforce offerings with the launch of its SMS integration for Salesforce Marketing Cloud Engagement, complementing its existing Sales Cloud application. Now available on Salesforce AppExchange, this addition enables seamless documentation of SMS activities across the Salesforce Cloud ecosystem.
With TrueDialog’s integration for Salesforce Marketing Cloud, companies can incorporate SMS into customized, targeted marketing journeys. This includes sending promotional messages, geo-targeted offers, triggered alerts, order confirmations, account updates, and more—all as part of a cohesive customer engagement strategy.
TrueDialog’s solution uniquely enables SMS message flow across Salesforce Marketing Cloud, Sales Cloud, Service Cloud, and Education Cloud, ensuring communication continuity across platforms. “TrueDialog is the only provider offering bidirectional SMS integration between Salesforce Marketing Cloud and other Salesforce Clouds, including Sales, Service, Commerce, and Education Clouds,” said John Wright, CEO of TrueDialog. “Our solution records text messages on all relevant Salesforce Clouds, maintaining communication continuity between companies and their customers—something no other provider offers.”
The TrueDialog SMS application is fully native to Salesforce Marketing Cloud, allowing users to work within their regular workflow without switching applications to integrate SMS steps. TrueDialog also offers flexible options for short and long codes, automated 10DLC registration for long codes, and TrueDelivery, a tool for assessing SMS deliverability.
The rise of agentic AI has dominated recent discussions in enterprise technology, sparking debates over its transformative potential and practical applications. Just weeks ago, few had heard of the term. Now, every tech vendor is racing to stake their claim in this emerging space, positioning agentic AI as the successor to AI co-pilots. While co-pilots assist users with tasks, agentic AI represents the next step: delegating tasks to intelligent agents capable of independent execution, akin to assigning work to a junior colleague. But beyond the buzz, the pressing questions remain: Cutting Through the Hype Recent launches provide a snapshot of how enterprises are beginning to deploy agentic AI. Salesforce’s Agentforce, Asana’s AI Studio, and Atlassian’s Rovo AI Assistant all emphasize the ability of these agents to streamline workflows by interpreting unstructured data and automating complex tasks. These tools promise flexibility over previous rigid, rule-based systems. For example, instead of painstakingly scripting every step, users can instruct an agent to “follow documented policies, analyze data, and propose actions,” reserving human approval for final execution. However, the performance of these agents hinges on data quality and system robustness. Salesforce’s Marc Benioff, for instance, critiques Microsoft’s Copilot for lacking a robust data model, emphasizing Salesforce’s own structured approach as a competitive edge. Similarly, Asana and Atlassian highlight the structured work graphs underpinning their platforms as critical for accurate and reliable outputs. Key Challenges Despite the promise, there are significant challenges to deploying agentic AI effectively: Early Wins and Future Potential Early adopters are seeing value in high-volume, repetitive scenarios such as customer service. For example: However, these successes represent low-hanging fruit. The true promise lies in rethinking how enterprises work. As one panelist at Atlassian’s event noted: “We shouldn’t just use this AI to enhance existing processes. We should ask whether these are the processes we want for the future.” The Path Forward The transformative potential of agentic AI will depend on broader process standardization. Just as standardized shipping containers revolutionized logistics, and virtual containers transformed IT operations, similar breakthroughs in process design could unlock exponential gains for AI-driven workflows. For now, enterprises should: Conclusion Agentic AI holds immense potential, but its real power lies in enabling enterprises to question and redesign how work gets done. While it may still be in its early days, businesses that align their AI investments with strategic goals—and not just immediate fixes—will be best positioned to thrive in this new era of intelligent automation. 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 Agent Workflows: The Ultimate Guide to Choosing Between LangChain and LangGraph Explore two transformative libraries—LangChain and LangGraph—both created by the same developer, designed to build Agentic AI applications. This guide dives into their foundational components, differences in handling functionality, and how to choose the right tool for your use case. Language Models as the Bridge Modern language models have unlocked revolutionary ways to connect users with AI systems and enable AI-to-AI communication via natural language. Enterprises aiming to harness Agentic AI capabilities often face the pivotal question: “Which tools should we use?” For those eager to begin, this question can become a roadblock. Why LangChain and LangGraph? LangChain and LangGraph are among the leading frameworks for crafting Agentic AI applications. By understanding their core building blocks and approaches to functionality, you’ll gain clarity on how each aligns with your needs. Keep in mind that the rapid evolution of generative AI tools means today’s truths might shift tomorrow. Note: Initially, this guide intended to compare AutoGen, LangChain, and LangGraph. However, AutoGen’s upcoming 0.4 release introduces a foundational redesign. Stay tuned for insights post-launch! Understanding the Basics LangChain LangChain offers two primary methods: Key components include: LangGraph LangGraph is tailored for graph-based workflows, enabling flexibility in non-linear, conditional, or feedback-loop processes. It’s ideal for cases where LangChain’s predefined structure might not suffice. Key components include: Comparing Functionality Tool Calling Conversation History and Memory Retrieval-Augmented Generation (RAG) Parallelism and Error Handling When to Choose LangChain, LangGraph, or Both LangChain Only LangGraph Only Using LangChain + LangGraph Together Final Thoughts Whether you choose LangChain, LangGraph, or a combination, the decision depends on your project’s complexity and specific needs. By understanding their unique capabilities, you can confidently design robust Agentic AI workflows. 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 Rise of Agentforce: How AI Agents Are Shaping the Future of Work Salesforce wrapped up its annual Dreamforce conference this September, leaving attendees with more than just memories of John Mulaney’s quips. As the swarms of Waymos ferried participants across a cleaner-than-usual San Francisco, it became clear that AI-powered agents—dubbed Agentforce—are poised to transform the workplace. These agents, controlled within Salesforce’s ecosystem, could significantly change how work is done and how customer experiences are delivered. Dreamforce has always been known for its bold predictions about the future, but this year’s vision of AI-based agents felt particularly compelling. These agents represent the next frontier in workplace automation, but as exciting as this future is, some important questions remain. Reality Check on the Agentforce Vision During his keynote, Salesforce CEO Marc Benioff raised an interesting point: “Why would our agents be so low-hallucinogenic?” While the agents have access to vast amounts of data, workflows, and services, they currently function best within Salesforce’s own environment. Benioff even made the claim that Salesforce pioneered prompt engineering—a statement that, for some, might have evoked a scene from Austin Powers, with Dr. Evil humorously taking credit for inventing the question mark. But can Salesforce fully realize its vision for Agentforce? If they succeed, it could be transformative for how work gets done. However, as with many AI-driven innovations, the real question lies in interoperability. The Open vs. Closed Debate As powerful as Salesforce’s ecosystem is, not all business data and workflows live within it. If the future of work involves a network of AI agents working together, how far can a closed ecosystem like Salesforce’s really go? Apple, Microsoft, Amazon, and other tech giants also have their sights set on AI-driven agents, and the race is on to own this massive opportunity. As we’ve seen in previous waves of technology, this raises familiar debates about open versus closed systems. Without a standard for agents to work together across platforms, businesses could find themselves limited. Closed ecosystems may help solve some problems, but to unlock the full potential of AI agents, they must be able to operate seamlessly across different platforms and boundaries. Looking to the Open Web for Inspiration The solution may lie in the same principles that guide the open web. Just as mobile apps often require a web view to enable an array of outcomes, the same might be necessary in the multi-agent landscape. Tools like Slack’s Block Kit framework allow for simple agent interactions, but they aren’t enough for more complex use cases. Take Clockwise Prism, for example—a sophisticated scheduling agent designed to find meeting times when there’s no obvious availability. When integrated with other agents to secure that critical meeting, businesses will need a flexible interface to explore multiple scheduling options. A web view for agents could be the key. The Need for an Open Multi-Agent Standard Benioff repeatedly stressed that businesses don’t want “DIY agents.” Enterprises seek controlled, repeatable workflows that deliver consistent value—but they also don’t want to be siloed. This is why the future requires an open standard for agents to collaborate across ecosystems and platforms. Imagine initiating a set of work agents from within an Atlassian Jira ticket that’s connected to a Salesforce customer case—or vice versa. For agents to seamlessly interact regardless of the system they originate from, a standard is needed. This would allow businesses to deploy agents in a way that’s consistent, integrated, and scalable. User Experience and Human-in-the-Loop: Crucial Elements for AI Agents A significant insight from the integration of LangChain with Assistant-UI highlighted a crucial factor: user experience (UX). Whether it’s streaming, generative interfaces, or human-in-the-loop functionality, the UX of AI agents is critical. While agents need to respond quickly and efficiently, businesses must have the ability to involve humans in decision-making when necessary. This principle of human-in-the-loop is key to the agent’s scheduling process. While automation is the goal, involving the user at crucial points—such as confirming scheduling options—ensures that the agent remains reliable and adaptable. Any future standard must prioritize this capability, allowing for user involvement where necessary, while also enabling full automation when confidence levels are high. Generative or Native UI? The discussion about user interfaces for agents often leads to a debate between generative UI and native UI. The latter may be the better approach. A native UI, controlled by the responding service or agent, ensures the interface is tailored to the context and specifics of the agent’s task. Whether this UI is rendered using AI or not is an implementation detail that can vary depending on the service. What matters is that the UI feels native to the agent’s task, making the user experience seamless and intuitive. What’s Next? The Push for an Open Multi-Agent Future As we look ahead to the multi-agent future, the need for an open standard is more pressing than ever. At Clockwise, we’ve drafted something we’re calling the Open Multi-Agent Protocol (OMAP), which we hope will foster collaboration and innovation in this space. The future of work is rapidly approaching, where new roles—like Agent Orchestrators—will emerge, enabling people to leverage AI agents in unprecedented ways. While Salesforce’s vision for Agentforce is ambitious, the key to unlocking its full potential lies in creating a standard that allows agents to work together, across platforms, and beyond the boundaries of closed ecosystems. With the right approach, we can create a future where AI agents transform work in ways we’re only beginning to imagine. 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
Researchers from Mass General Brigham recently published findings in PAIN indicating that large language models (LLMs) do not exhibit race- or sex-based biases when recommending opioid treatments. The team highlighted that, while biases are prevalent in many areas of healthcare, they are particularly concerning in pain management. Studies have shown that Black patients’ pain is often underestimated and undertreated by clinicians, while white patients are more likely to be prescribed opioids than other racial and ethnic groups. These disparities raise concerns that AI tools, including LLMs, could perpetuate or exacerbate such biases in healthcare. To investigate how AI tools might either mitigate or reinforce biases, the researchers explored how LLM recommendations varied based on patients’ race, ethnicity, and sex. Using 40 real-world patient cases from the MIMIC-IV Note data set—each involving complaints of headache, abdominal, back, or musculoskeletal pain—the cases were stripped of references to sex and race. Random race categories (American Indian or Alaska Native, Asian, Black, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, and white) and sex (male or female) were then assigned to each case. This process was repeated until all combinations of race and sex were generated, resulting in 480 unique cases. These cases were analyzed using GPT-4 and Gemini, both of which assigned subjective pain ratings and made treatment recommendations. The analysis found that neither model made opioid treatment recommendations that differed by race or sex. However, the tools did show some differences—GPT-4 tended to rate pain as “severe” more frequently than Gemini, which was more likely to recommend opioids. While further validation is necessary, the researchers believe the results indicate that LLMs could help address biases in healthcare. “These results are reassuring in that patient race, ethnicity, and sex do not affect recommendations, indicating that these LLMs have the potential to help address existing bias in healthcare,” said co-first authors Cameron Young and Ellie Einchen, students at Harvard Medical School, in a press release. However, the study has limitations. It categorized sex as a binary variable, omitting a broader gender spectrum, and it did not fully represent mixed-race individuals, leaving certain marginalized groups underrepresented. The team suggested future research should incorporate these factors and explore how race influences LLM recommendations in other medical specialties. Marc Succi, MD, strategic innovation leader at Mass General Brigham and corresponding author of the study, emphasized the need for caution in integrating AI into healthcare. “There are many elements to consider, such as the risks of over-prescribing or under-prescribing medications and whether patients will accept AI-influenced treatment plans,” Succi said. “Our study adds key data showing how AI has the potential to reduce bias and improve health equity.” Succi also noted the broader implications of AI in clinical decision support, suggesting that AI tools will serve as complementary aids to healthcare professionals. “In the short term, AI algorithms can act as a second set of eyes, running in parallel with medical professionals,” he said. “However, the final decision will always remain with the doctor.” These findings offer important insights into the role AI could play in reducing bias and enhancing equity in pain management and healthcare overall. 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
Since the launch of ChatGPT in 2022, developers have been split into two camps: those who ban AI in coding and those who embrace it. Many seasoned programmers not only avoid AI-generated code but also prohibit their teams from using it. Their reasoning is simple: “AI-generated code is unreliable.” Even if one doesn’t agree with this anti-AI stance, they’ve likely faced challenges, hurdles, or frustrations when using AI for programming. The key is finding the right strategies to use AI to your advantage. Many are still using outdated AI strategies from two years ago, likened to cutting down a tree with kitchen knives. Two Major Issues with AI for Developers The Wrong Way to Use AI… …can be broken down into two parts: When ChatGPT first launched, the typical way to work with AI was to visit the website and chat with GPT-3.5 in a browser. The process was straightforward: copy code from the IDE, paste it into ChatGPT with a basic prompt like “add comments,” get the revised code, check for errors, and paste it back into the IDE. Many developers, especially beginners and students, are still using this same method. However, the AI landscape has changed significantly over the last two years, and many have not adjusted their approach to fully leverage AI’s potential. Another common pitfall is how developers use AI. They ask the LLM to generate code, test it, and go back and forth to fix any issues. Often, they fall into an endless loop of AI hallucinations when trying to get the LLM to understand what’s wrong. This can be frustrating and unproductive. Four Tools to Boost Programming Productivity with AI 1. Cursor: AI-First IDE Cursor is an AI-first IDE built on VScode but enhanced with AI features. It allows developers to integrate a chatbot API and use AI as an assistant. Some of Cursor’s standout features include: Cursor integrates seamlessly with VScode, making it easy for existing users to transition. It supports various models, including GPT-4, Claude 3.5 Sonnet, and its built-in free cursor-small model. The combination of Cursor and Sonnet 3.5 has been particularly praised for producing reliable coding results. This tool is a significant improvement over copy-pasting code between ChatGPT and an IDE. 2. Micro Agent: Code + Test Case Micro Agent takes a different approach to AI-generated code by focusing on test cases. Instead of generating large chunks of code, it begins by creating test cases based on the prompt, then writes code that passes those tests. This method results in more grounded and reliable output, especially for functions that are tricky but not overly complex. 3. SWE-agent: AI for GitHub Issues Developed by Princeton Language and Intelligence, SWE-agent specializes in resolving real-world GitHub repository issues and submitting pull requests. It’s a powerful tool for managing large repositories, as it reviews codebases, identifies issues, and makes necessary changes. SWE-agent is open-source and has gained considerable popularity on GitHub. 4. AI Commits: git commit -m AI Commits generates meaningful commit messages based on your git diff. This simple tool eliminates the need for vague or repetitive commit messages like “minor changes.” It’s easy to install and uses GPT-3.5 for efficient, AI-generated commit messages. The Path Forward To stay productive and achieve goals in the rapidly evolving AI landscape, developers need the right tools. The limitations of AI, such as hallucinations, can’t be eliminated, but using the appropriate tools can help mitigate them. Simple, manual interactions like generating code or comments through ChatGPT can be frustrating. By adopting the right strategies and tools, developers can avoid these pitfalls and confidently enhance their coding practices. AI is evolving fast, and keeping up with its changes is crucial. The right tools can make all the difference in your programming workflow. 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
Organizations must acknowledge the risks associated with implementing AI systems to use the technology ethically and minimize liability. Throughout history, companies have had to manage the risks associated with adopting new technologies, and AI is no exception. Some AI risks are similar to those encountered when deploying any new technology or tool, such as poor strategic alignment with business goals, a lack of necessary skills to support initiatives, and failure to secure buy-in across the organization. For these challenges, executives should rely on best practices that have guided the successful adoption of other technologies. In the case of AI, this includes: However, AI introduces unique risks that must be addressed head-on. Here are 15 areas of concern that can arise as organizations implement and use AI technologies in the enterprise: Managing AI Risks While AI risks cannot be eliminated, they can be managed. Organizations must first recognize and understand these risks and then implement policies to minimize their negative impact. These policies should ensure the use of high-quality data, require testing and validation to eliminate biases, and mandate ongoing monitoring to identify and address unexpected consequences. Furthermore, ethical considerations should be embedded in AI systems, with frameworks in place to ensure AI produces transparent, fair, and unbiased results. Human oversight is essential to confirm these systems meet established standards. For successful risk management, the involvement of the board and the C-suite is crucial. As noted, “This is not just an IT problem, so all executives need to get involved in this.” 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
Skechers Boosts Direct-to-Consumer Sales with Ecommerce Platform Upgrades Skechers, now a global brand in 2024, credits its recent ecommerce platform upgrades for saving time and increasing direct-to-consumer sales. However, it wasn’t always equipped with the right technology to support its massive growth. During Salesforce’s Dreamforce conference in San Francisco, Eric Cheng, Skechers USA Inc.’s director of ecommerce architecture, shared insights into how key technology decisions helped the brand expand and enhance its website and content capabilities. “Today, we’re present in over 180 countries worldwide,” Cheng said, speaking on stage at the Moscone Center. Skechers’ journey began in 1992, and its expansion has taken the brand across borders, reaching millions of customers worldwide. “We connect hundreds of millions of customers through our retail stores and ecommerce platform to deliver a unique experience,” Cheng noted, emphasizing the need to meet the diverse demands of each market. Skechers ranks No. 273 in the Top 1000, Digital Commerce 360’s ranking of the largest North American e-retailers by online sales, where it is categorized as an Apparel & Accessories retailer. Digital Commerce 360 projects that Skechers will reach 0.65 million in online sales by 2024. Ecommerce Platform Challenges Cheng acknowledged that Skechers’ digital transformation wasn’t immediate: “The journey did not just happen overnight; it took time and effort.” Skechers faced challenges in three key areas: content management, scalability, and customer experience. The legacy system was inadequate, lacking robust tools for efficient content delivery, previewing scheduled content, and handling localization. As Cheng described, launching a marketing page often required the content team to be on standby at midnight—an unsustainable approach for 17 countries. How Skechers Solved Its Ecommerce Challenges To overcome these hurdles, Skechers partnered with Astound Digital. Together, they implemented Salesforce Service Cloud and Manhattan Active Omni for order management. Kyle Montgomery, senior vice president of commerce at Astound Digital, joined Cheng on stage and highlighted the goal: “Their vision was to unify, supply, and scale.” This transformation enabled Skechers to bring 17 countries in Europe, Japan, and North America onto a single platform. Jennifer Lane, Salesforce’s director of success guides, also emphasized the flexibility achieved using Salesforce’s Page Designer and localization solutions from Salesforce’s AppExchange. Integrations with Thomson Reuters for tax, CyberSource for payments, and Salesforce Marketing Cloud for personalization further enhanced Skechers’ capabilities. The Results Cheng highlighted three key improvements after the ecommerce overhaul. First, content creation and localization tools improved operational efficiency by over 500%. The time to launch in new markets was dramatically reduced from five months to just a few weeks. Additionally, Skechers saw a notable sales boost, with a 24.5% increase in its direct-to-consumer segment during Q1 2023. Skechers’ success demonstrates the significant impact of a well-executed ecommerce platform upgrade, allowing the brand to scale globally while improving customer experience and operational efficiency. Contact Tectonic to learn what Salesforce can do for you. 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
