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Market Insights and Forecast for Quote Generation Software
26Oct

Market Insights and Forecast for Quote Generation Software

Market Insights and Forecast for Quote Generation Software for Salesforce (2024-2031): Key Players, Technology Advancements, and Growth Opportunities A recent research report by WMR delves into the Quote Generation Software for Salesforce Market, offering over 150 pages of in-depth analysis on business strategies employed by both leading and emerging industry players. The study provides insights into market developments, technological advancements, drivers, opportunities, and overall market status. Understanding market segments is essential to identify key factors driving growth. Comprehensive Market Insights The report provides an extensive analysis of the global market landscape, including business expansion strategies designed to increase revenue. It compiles critical data about target customers, evaluating the potential success of products and services prior to launch. The research offers valuable insights for stakeholders, including detailed updates on the impact of COVID-19 on business operations and the broader market. The report assesses whether a target market aligns with an enterprise’s goals, emphasizing that market success hinges on understanding the target audience. Key Players Featured: Market Segmentation By Types: By Applications: Geographical Overview The Quote Generation Software for Salesforce Market varies significantly across regions, driven by factors such as economic development, technical advancements, and cultural differences. Businesses looking to expand globally must account for these variations to leverage local opportunities effectively. Key regions include: Competitive Landscape The report offers a detailed competitive analysis, highlighting: Highlights from the Report Key Market Questions Addressed: Reasons to Purchase this Report: This report provides a valuable roadmap for businesses aiming to navigate the evolving Quote Generation Software for Salesforce Market, helping them make informed decisions and strategically position themselves for growth. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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October 26, 2024in Data, Enterprise, Salesforce, Technology
Data Quality Management Process
26Oct

Data Quality Management Process

Data quality is often paradoxical—simple in its fundamentals, yet challenging in its details. A solid data quality management program is essential for ensuring processes run smoothly. What is Data Quality? At its core, data quality means having accurate, consistent, complete, and up-to-date data. However, quality is also context-dependent. Different tasks or applications require different types of data and, consequently, different standards of quality. Data that works well for one purpose may not be suitable for another. For instance, a list of customer names and addresses might be ideal for a marketing campaign but insufficient for tracking customer sales history. There isn’t a universal quality standard. A data set of credit card transactions, filled with cancellations and verification errors, may seem messy for sales analysis—but that’s exactly the kind of data the fraud analysis team wants to see. The most accurate way to assess data quality is to ask, “Is the data fit for its current purpose?” Steps to Build a Data Quality Management Process The goal of data quality management is not perfection. Instead, it focuses on ensuring reliable, high-quality data across the organization. Here are five key steps in developing a robust data quality process: Step 1: Data Quality Assessment Begin by assessing the current state of data. All relevant parties—from business units to IT—should understand the current condition of the organization’s data. Check for errors, duplicates, or missing entries and evaluate accuracy, consistency, and completeness. Techniques like data profiling can help identify data issues. This step forms the foundation for the rest of the process. Step 2: Develop a Data Quality Strategy Next, develop a strategy to improve and maintain data quality. This blueprint should define the use cases for data, the required quality for each, and the rules for data collection, storage, and processing. Choose the right tools and outline how to handle errors or discrepancies. This strategic plan will guide the organization toward sustained data quality. Step 3: Initial Data Cleansing This is where you take action to improve your data. Clean, correct, and prepare the data based on the issues identified during the assessment. Remove duplicates, fill in missing information, and resolve inconsistencies. The goal is to establish a strong baseline for future data quality efforts. Remember, data quality isn’t about perfection—it’s about making data fit for purpose. Step 4: Implement the Data Quality Strategy Now, put the plan into action by integrating data quality standards into daily workflows. Train teams on new practices and modify existing processes to include data quality checks. If done correctly, data quality management becomes a continuous, self-correcting process. Step 5: Monitor Data Quality Finally, monitor the ongoing process. Data quality management is not a one-time event; it requires continuous tracking and review. Regular audits, reports, and dashboards help ensure that data standards are maintained over time. In summary, an effective data quality process involves understanding current data, creating a plan for improvement, and consistently monitoring progress. The aim is not perfection, but ensuring data is fit for purpose. The Impact of AI and Machine Learning on Data Quality The rise of AI and machine learning (ML) brings new challenges to data quality management. For AI and ML, the quality of training data is crucial. The performance of models depends on the accuracy, completeness, and bias of the data used. If the training data is flawed, the model will produce flawed outcomes. Volume is another challenge. AI and ML models require vast amounts of data, and ensuring the quality of such large datasets can be a significant task. Organizations may need to prepare data specifically for AI and ML projects. This might involve collecting new data, transforming existing data, or augmenting it to meet the requirements of the models. Special attention must be paid to avoid bias and ensure diversity in the data. In some cases, existing data may not be sufficient or representative enough to meet future needs. Implementing specific validation checks for AI and ML training data is essential. This includes checking for bias, ensuring diversity, and verifying that the data accurately represents the problem the model is designed to address. By applying these practices, organizations can tackle the evolving challenges of data quality in the age of AI and machine learning. Create a great Data Quality Management Process. 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

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October 26, 2024in Data
AI FOMO
26Oct

AI FOMO

Enterprise interest in artificial intelligence has surged in the past two years, with boardroom discussions centered on how to capitalize on AI advancements before competitors do. Generative AI has been a particular focus for executives since the launch of ChatGPT in November 2022, followed by other major product releases like Amazon’s Bedrock, Google’s Gemini, Meta’s Llama, and a host of SaaS tools incorporating the technology. However, the initial rush driven by fear of missing out (FOMO) is beginning to fade. Business and tech leaders are now shifting their attention from experimentation to more practical concerns: How can AI generate revenue? This question will grow in importance as pilot AI projects move into production, raising expectations for financial returns. Using AI to Increase Revenue AI’s potential to drive revenue will be a critical factor in determining how quickly organizations adopt the technology and how willing they are to invest further. Here are 10 ways businesses can harness AI to boost revenue: 1. Boost Sales AI-powered virtual assistants and chatbots can help increase sales. For example, Ikea’s generative AI tool assists customers in designing their living spaces while shopping for furniture. Similarly, jewelry insurance company BriteCo launched a GenAI chatbot that reduced chat abandonment rates, leading to more successful customer interactions and potentially higher sales. A TechTarget survey revealed that AI-powered customer-facing tools like chatbots are among the top investments for IT leaders. 2. Reduce Customer Churn AI helps businesses retain clients, reducing revenue loss and improving customer lifetime value. By analyzing historical data, AI can profile customer attributes and identify accounts at risk of leaving. AI can then assist in personalizing customer experiences, decreasing churn and fostering loyalty. 3. Enhance Recommendation Engines AI algorithms can analyze customer data to offer personalized product recommendations. This drives cross-selling and upselling opportunities, boosting revenue. For instance, Meta’s AI-powered recommendation engine has increased user engagement across its platforms, attracting more advertisers. 4. Accelerate Marketing Strategies While marketing doesn’t directly generate revenue, it fuels the sales pipeline. Generative AI can quickly produce personalized content, such as newsletters and ads, tailored to customer interests. Gartner predicts that by 2025, 30% of outbound marketing messages will be AI-generated, up from less than 2% in 2022. 5. Detect Fraud AI is instrumental in detecting fraudulent activities, helping businesses preserve revenue. Financial firms like Capital One use machine learning to detect anomalies and prevent credit card fraud, while e-commerce companies leverage AI to flag fraudulent orders. 6. Reinvent Business Processes AI can transform entire business processes, unlocking new revenue streams. For example, Accenture’s 2024 report highlighted an insurance company that expects a 10% revenue boost after retooling its underwriting workflow with AI. In healthcare, AI could streamline revenue cycle management, speeding up reimbursement processes. 7. Develop New Products and Services AI accelerates product development, particularly in industries like pharmaceuticals, where it assists in drug discovery. AI tools also speed up the delivery of digital products, as seen with companies like Ally Financial and ServiceNow, which have reduced software development times by 20% or more. 8. Provide Predictive Maintenance AI-driven predictive maintenance helps prevent costly equipment downtime in industries like manufacturing and fleet management. By identifying equipment on the brink of failure, AI allows companies to schedule repairs and avoid revenue loss from operational disruptions. 9. Improve Forecasting AI’s predictive capabilities enhance planning and forecasting. By analyzing historical and real-time data, AI can predict product demand and customer behavior, enabling businesses to optimize inventory levels and ensure product availability for ready-to-buy customers. 10. Optimize Pricing AI can dynamically adjust prices based on factors like demand shifts and competitor pricing. Reinforcement learning algorithms allow businesses to optimize pricing in real time, ensuring they maximize revenue even as market conditions change. Keeping ROI in Focus While AI offers numerous ways to generate new revenue streams, it also introduces costs in development, infrastructure, and operations—some of which may not be immediately apparent. For instance, research from McKinsey & Company shows that GenAI models account for only 15% of a project’s total cost, with additional expenses related to change management and data preparation often overlooked. To make the most of AI, organizations should prioritize use cases with a clear return on investment (ROI) and postpone those that don’t justify the expense. A focus on ROI ensures that AI deployments align with business goals and contribute to sustainable revenue growth. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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October 26, 2024in Generative AI
Transform Customer Experience
25Oct

Transform Customer Experience

In today’s AI-driven business environment, customer experience (CX) has evolved from being a buzzword to a critical factor in determining success. It’s no longer enough for businesses to offer high-quality products or excellent service alone—today’s customers are always online, engaged, and seeking the most convenient, relevant, and enjoyable experiences. This is where Salesforce Data Cloud becomes a game-changer, providing the tools needed to meet modern customer expectations. Transforming Customer Experience with Salesforce Data Cloud Salesforce enables businesses to collect, integrate, and leverage critical customer information within its ecosystem, offering an all-encompassing view of each customer. This unified customer data allows organizations to forecast visitor trends, assess marketing impact, and predict customer behavior. As data-driven decision-making becomes increasingly central to business strategy, Salesforce Data Cloud and its Customer Data Platform (CDP) features provide a significant competitive edge—whether in e-commerce, fintech, or B2B industries. Data Cloud is more than just your traditional CDP. It’s the only data platform native to the world‘s #1 AI CRM. This means that marketers can quickly access and easily action on unified data – from across the entire business – to drive growth and increase customer lifetime value. Data Cloud’s Role in Enhancing CX By unifying data in one place, Salesforce Data Cloud enables organizations to access real-time customer insights. This empowers them to track customer activity across channels like email, social media, and online sales, facilitating targeted marketing strategies. Businesses can analyze customer behavior and deliver personalized messaging, aligning marketing, sales, and customer service efforts to ensure consistency. With these capabilities, Salesforce customers can elevate the CX by delivering the right content, at the right time, to the right audience, ultimately driving customer satisfaction and growth. New Features of Salesforce Data Cloud Salesforce continues to evolve, introducing cutting-edge features that reshape customer interaction: To fully maximize these features, partnering with a Salesforce Data Cloud consultant can help businesses unlock the platform’s full potential and refine their customer engagement strategies. Agentic AI Set to Supercharge Business Processes Salesforce’s vision extends beyond customer relationship management with the integration of Agentic AI through its Customer 360 platform. According to theCUBE Research analysts, this signals a shift toward using AI agents to automate complex business processes. These AI agents, built on Salesforce’s vast data resources, promise to revolutionize how companies operate, offering customized, AI-driven business tools. “If they can pull this off, where it becomes a more dynamic app platform, more personalized, really focused on those processes all the way back to the data, it’s going to be a clear win for them,” said Strechay. “They’re sitting on cloud; they’re sitting on IaaS. That’s a huge win from that perspective.” AI agents create a network of microservices that think and act independently, involving human intervention only when necessary. This division of labor allows businesses to capture expertise in routine tasks while freeing human workers to focus on more complex decision-making. However, the success of these AI agents depends on access to accurate and reliable data. As Gilbert explained, “Agents can call on other agents, and when they’re not confident of a step in a process or an outcome, they can then bounce up to an inbox for a human to supervise.” The goal isn’t to eliminate humans but to capture their expertise for simpler processes. Empowering Developers and Citizen Creators At the core of this AI-driven transformation is Salesforce’s focus on developers. The platform’s low-code tools allow businesses to easily customize AI agents and automate business processes, empowering both experienced developers and citizen creators. With simple language commands or goal-setting, companies can build and train these AI agents, streamlining operations. “It’s always going to be about good data—that’s the constant,” Bertrand said. “The second challenge is how to train agents and humans to work together effectively. While some entry-level jobs may be replaced, AI will continue to evolve, creating new opportunities in the future.” Is Salesforce Data Cloud the Right Fit for Your Business? Salesforce Data Cloud offers comprehensive capabilities for businesses of all sizes, but it’s essential to assess whether it aligns with your specific needs. The platform is particularly valuable for: For businesses that fit these scenarios, working with Salesforce’s partner ecosystem or a Data Cloud consultant can help ensure successful integration and optimization. What’s New in Salesforce’s Latest Release? The latest Salesforce Spring Release introduced several exciting features, further enhancing Salesforce Data Cloud: These updates reflect Salesforce’s commitment to providing innovative, data-driven solutions that enhance customer experiences and drive business success. 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

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October 25, 2024in AI Tools, Data, Generative AI, Salesforce, Salesforce Data Cloud
What Should Enterprises Build with Agentic AI?
25Oct

What Should Enterprises Build with Agentic AI?

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

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October 25, 2024in Agentforce Platform, Data, Enterprise, Generative AI, Salesforce, Technology
AI Innovation at Salesforce
24Oct

AI Innovation at Salesforce

AI innovation is advancing at an unprecedented pace, unlike anything I’ve seen in nearly 25 years at Salesforce. It’s now a top priority for every CEO, CTO, and CIO I speak with. As a trusted partner, we help customers innovate, iterate, and navigate the evolving AI landscape. They recognize AI’s immense potential to revolutionize every aspect of business, across all industries. While they’re already seeing significant advancements, we are still just scratching the surface of AI’s full transformational promise. They seek AI technologies that will enhance productivity, augment employee performance at scale, improve customer relationships, and ultimately drive rapid time to value and higher margins. That’s where our new Agentforce Platform comes in. Agentforce represents a breakthrough in AI, delivering on the promise of autonomous AI agents. These agents perform advanced planning and decision-making with minimal human input, automating entire workflows, making real-time decisions, and adapting to new information—all without requiring human intervention. Salesforce customers are embracing Agentforce and integrating it with other products, including Einstein AI, Data Cloud, Sales Cloud, and Service Cloud. Here are some exciting ways our customers are utilizing these tools: Strengthening Customer Relationships with AI Agents OpenTable is leveraging autonomous AI agents to handle the massive scale of its operations, supporting 60,000 restaurants and millions of diners. By piloting Agentforce for Service, they’ve automated common tasks like account reactivations, reservation management, and loyalty point expiration. The AI agents even answer complex follow-up questions, such as “when do my points expire in Mexico?”—a real “wow” moment for OpenTable. These agents are redefining how customers engage with companies. Wiley, an educational publisher, faces a seasonal surge in service requests each school year. By piloting Agentforce Service Agent, they increased case resolution by 40-50% and sped up new agent onboarding by 50%, outperforming their previous systems. Harnessing Data Insights The Adecco Group, a global leader in talent solutions, wanted to unlock insights from its vast data reserves. Using Data Cloud, they’re connecting multiple Salesforce instances to give 27,000 recruiters and sales staff real-time, 360-degree views of their operations. This empowers Adecco to improve job fill rates and streamline operations for some of the world’s largest companies. Workday, a Salesforce customer for nearly two decades, uses Service Cloud to power customer service and Slack for internal collaboration. Our new partnership with Workday will integrate Agentforce with their platform, creating a seamless employee experience across Salesforce, Slack, and Workday. This includes AI-powered employee service agents accessible across all platforms. Wyndham Resorts is transforming its guest experience by using Data Cloud to harmonize CRM data across Sales Cloud, Marketing Cloud, and Service Cloud. By consolidating their systems, Wyndham anticipates a 30% reduction in call resolution time and an overall enhanced customer experience through better access to accurate guest and property data. Empowering Employees Air India, with ambitions to capture 30% of India’s airline market, is using Data Cloud, Service Cloud, and Einstein AI to unify data across merged airlines and enhance customer service. Now, human agents spend more time with customers while AI handles routine tasks, resulting in faster resolution of 550,000 monthly service calls. Heathrow Airport is focused on improving employee efficiency and personalizing passenger experiences. Service Cloud and Einstein chatbots have significantly reduced call volumes, with chatbots answering 4,000 questions monthly. Since launching, live chat usage has surged 450%, and average call times have dropped 27%. These improvements have boosted Heathrow’s digital revenue by 30% since 2019. Driving Productivity and Margins Aston Martin sought to improve customer understanding and dealer collaboration. By adopting Data Cloud, they unified their customer data, reducing redundancy by 52% and transitioning from six data systems to one, streamlining operations. Autodesk, a leader in 3D design and engineering software, uses Einstein for Service to generate AI-driven case summaries, cutting the time spent summarizing customer chats by 63%. They also use Salesforce to enhance data security, reducing ongoing maintenance by 30%. Creating a Bright Future for Our Customers For over 25 years, Salesforce has guided customers through transformative technological shifts. The fusion of AI and human intelligence is the most profound shift we’ve seen, unlocking limitless potential for business success. Join them at Dreamforce next month, where we’ll celebrate customer achievements and share the latest innovations. 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

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October 24, 2024in Agentforce Platform
Rise of Agentforce
24Oct

Rise of Agentforce

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

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October 24, 2024in Data, Enterprise, Salesforce, Technology
Salesforce Says AI Should Be a Partner
24Oct

Salesforce Says AI Should Be a Partner

Salesforce Says AI Should Be a Partner, Not Just a Tool As AI continues to evolve rapidly, Salesforce’s chief ethical and humane use officer, Paula Goldman, urged businesses to rethink how they integrate AI in the workplace. According to Goldman, we are at a pivotal moment where AI should be seen as a partner rather than merely a tool. Goldman emphasized the concept of agentic AI, which refers to AI systems that can act independently to achieve goals or make decisions on behalf of the company. However, with this autonomy comes the need for proper safeguards to prevent issues like bias and misinformation, especially considering AI’s tendency to generate “hallucinations” or inaccurate outputs. One powerful example Goldman provided was during a company board meeting where AI identified bias in real-time. The AI flagged a pattern that participants either didn’t notice or were hesitant to address, leading to richer discussions and better decision-making. She also cited a healthcare scenario where a nurse used AI during patient intake. The AI collected information through questions and answers, freeing up the nurse to focus on the patient’s body language and emotional cues, enhancing the human element of care. Goldman concluded by saying that the future of AI depends on how businesses choose to leverage it. “To make AI work for our businesses, we have to make sure it works for the people our businesses serve and the people our businesses employ,” she said. In short, AI should act as a collaborative partner, enhancing human judgment and decision-making while staying within ethical boundaries. 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

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October 24, 2024in Agentforce Platform
Scaling Generative AI
23Oct

Scaling Generative AI

Many organizations follow a hybrid approach to AI infrastructure, combining public clouds, colocation facilities, and on-prem solutions. Specialized GPU-as-a-service vendors, for instance, are becoming popular for handling high-demand AI computations, helping businesses manage costs without compromising performance. Business process outsourcing company TaskUs, for example, focuses on optimizing compute and data flows as it scales its gen AI deployments, while Cognizant advises that companies distinguish between training and inference needs, each with different latency requirements.

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October 23, 2024in Data, Enterprise, Generative AI, Google, Salesforce
AI Agents and Digital Transformation
23Oct

Ready for AI Agents

Brands that can effectively integrate agentic AI into their operations stand to gain a significant competitive edge. But as with any innovation, success will depend on balancing the promise of automation with the complexities of trust, privacy, and user experience.

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October 23, 2024in Data, Enterprise, Generative AI, Google, Salesforce
AI Risk Management
22Oct

AI Risk Management

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

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October 22, 2024in AI Tools, Data, Enterprise, Generative AI, Laws, Technology
What is Explainable AI
22Oct

What is Explainable AI

Building a trusted AI system starts with ensuring transparency in how decisions are made. Explainable AI is vital not only for addressing trust issues within organizations but also for navigating regulatory challenges. According to research from Forrester, many business leaders express concerns over AI, particularly generative AI, which surged in popularity following the 2022 release of ChatGPT by OpenAI. “AI faces a trust issue,” explained Forrester analyst Brandon Purcell, underscoring the need for explainability to foster accountability. He highlighted that explainability helps stakeholders understand how AI systems generate their outputs. “Explainability builds trust,” Purcell stated at the Forrester Technology and Innovation Summit in Austin, Texas. “When employees trust AI systems, they’re more inclined to use them.” Implementing explainable AI does more than encourage usage within an organization—it also helps mitigate regulatory risks, according to Purcell. Explainability is crucial for compliance, especially under regulations like the EU AI Act. Forrester analyst Alla Valente emphasized the importance of integrating accountability, trust, and security into AI efforts. “Don’t wait for regulators to set standards—ensure you’re already meeting them,” she advised at the summit. Purcell noted that explainable AI varies depending on whether the AI model is predictive, generative, or agentic. Building an Explainable AI System AI explainability encompasses several key elements, including reproducibility, observability, transparency, interpretability, and traceability. For predictive models, transparency and interpretability are paramount. Transparency involves using “glass-box modeling,” where users can see how the model analyzed the data and arrived at its predictions. This approach is likely to be a regulatory requirement, especially for high-risk applications. Interpretability is another important technique, useful for lower-risk cases such as fraud detection or explaining loan decisions. Techniques like partial dependence plots show how specific inputs affect predictive model outcomes. “With predictive AI, explainability focuses on the model itself,” Purcell noted. “It’s one area where you can open the hood and examine how it works.” In contrast, generative AI models are often more opaque, making explainability harder. Businesses can address this by documenting the entire system, a process known as traceability. For those using models from vendors like Google or OpenAI, tools like transparency indexes and model cards—which detail the model’s use case, limitations, and performance—are valuable resources. Lastly, for agentic AI systems, which autonomously pursue goals, reproducibility is key. Businesses must ensure that the model’s outputs can be consistently replicated with similar inputs before deployment. These systems, like self-driving cars, will require extensive testing in controlled environments before being trusted in the real world. “Agentic systems will need to rack up millions of virtual miles before we let them loose,” Purcell concluded. 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

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October 22, 2024in Data, Generative AI, Google, Technology
Cohere-Powered Slack Agents
21Oct

Cohere-Powered Slack Agents

Salesforce AI and Cohere-Powered Slack Agents: Seamless CRM Data Interaction and Enhanced Productivity Slack agents, powered by Salesforce AI and integrated with Cohere, enable seamless interaction with CRM data within the Slack platform. These agents allow teams to use natural language to surface data insights and take action, simplifying workflows. With Slack’s AI Workflow Builder and support for third-party AI agents, including Cohere, productivity is further enhanced through automated processes and customizable AI assistants. By leveraging these technologies, Slack agents provide users with direct access to CRM data and AI-powered insights, improving efficiency and collaboration. Key Features of Slack Agents: Salesforce AI and Cohere Productivity Enhancements with Slack Agents: Salesforce AI and Cohere AI Agent Capabilities in Slack: Salesforce and Cohere Data Security and Compliance for Slack Agents FAQ What are Slack agents, and how do they integrate with Salesforce AI and Cohere?Slack agents are AI-powered assistants that enable teams to interact with CRM data directly within Slack. Salesforce AI agents allow natural language data interactions, while Cohere’s integration enhances productivity with customizable AI assistants and automated workflows. How do Salesforce AI agents in Slack improve team productivity?Salesforce AI agents enable users to interact with both CRM and conversational data, update records, and analyze opportunities using natural language. This integration improves workflow efficiency, leading to a reported 47% productivity boost. What features does the Cohere integration with Slack AI offer?Cohere integration offers customizable AI assistants that can help generate workflows, summarize channel content, and provide intelligent responses to user queries within Slack. How do Slack agents handle data security and compliance?Slack agents leverage cloud-native DLP solutions, automatically detecting sensitive data across different file types and setting up automated remediation processes for enhanced security and compliance. Can Slack agents work with AI providers beyond Salesforce and Cohere?Yes, Slack supports AI agents from various providers. In addition to Salesforce AI and Cohere, integrations include Adobe Express, Anthropic, Perplexity, IBM, and Amazon Q Business, offering users a wide array of AI-powered capabilities. 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

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October 21, 2024in Data, Salesforce Slack
salesforce ai pitchfield
21Oct

Salesforce AI Pitchfield

AI Pitchfield is more than a showcase of entrepreneurial talent—it’s a launchpad for the next generation of AI pioneers. By fostering connections and providing critical investment opportunities, Salesforce and its partners are driving the evolution of AI across India and Southeast Asia. This initiative reflects Salesforce’s commitment to advancing technology, empowering startups, and shaping a future where AI continues to transform industries and unlock untapped potential.

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October 21, 2024in Artificial Intelligence, Salesforce, Technology

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