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AI Agent Rivalry
16Nov

AI Agent Rivalry

Microsoft and Salesforce’s AI Agent Rivalry Heats Up The battle for dominance in the AI agent space has escalated, with Salesforce CEO Marc Benioff intensifying his criticism of Microsoft’s AI solutions. Following remarks at Dreamforce 2024, Benioff took to X (formerly Twitter) to call out Microsoft for what he called “rebranding Copilot as ‘agents’ in panic mode.” The AI Agent rivalry winner may be determined not by flashy features but by delivering tangible, transformative outcomes for businesses navigating the complexities of AI adoption. AI Agent Rivalry. Benioff didn’t hold back, labeling Microsoft’s Copilot as “a flop”, citing issues like data leaks, inaccuracies, and requiring customers to build their own large language models (LLMs). In contrast, he touted Salesforce’s Agentforce as a solution that autonomously drives sales, service, marketing, analytics, and commerce without the complications he attributes to Microsoft’s offerings. Microsoft’s Copilot: A New UI for AI Microsoft recently unveiled new autonomous agent capabilities for Copilot Studio and Dynamics 365, positioning these agents as tools to enhance productivity across teams and functions. CEO Satya Nadella described Copilot as “the UI for AI” and emphasized its flexibility, allowing businesses to create, manage, and integrate agents seamlessly. Despite the fanfare, Benioff dismissed Copilot’s updates, likening it to “Clippy 2.0” and claiming it fails to deliver accuracy or transformational impact. Salesforce Expands Agentforce with Strategic Partnerships At Dreamforce 2024, Salesforce unveiled its Agentforce Partner Network, a global ecosystem featuring collaborators like AWS, Google Cloud, IBM, and Workday. The move aims to bolster the capabilities of Agentforce, Salesforce’s AI-driven platform that delivers tailored, autonomous business solutions. Agentforce allows businesses to deploy customizable agents without complex coding. With features like the Agent Builder, users can craft workflows and instructions in natural language, making the platform accessible to both technical and non-technical teams. Flexibility and Customization: Salesforce vs. Microsoft Both Salesforce and Microsoft emphasize AI’s transformative potential, but their approaches differ: Generative AI vs. Predictive AI Salesforce has doubled down on generative AI, with Einstein GPT producing personalized content using CRM data while also providing predictive analytics to forecast customer behavior and sales outcomes. Microsoft, on the other hand, combines generative and predictive AI across its ecosystem. Copilot not only generates content but also performs autonomous decision-making in Dynamics 365 and Azure, positioning itself as a comprehensive enterprise solution. The Rise of Multi-Agent AI Systems The competition between Microsoft and Salesforce reflects a broader trend in AI-driven automation. Companies like OpenAI are experimenting with frameworks like Swarm, which simplifies the creation of interconnected AI agents for tasks such as lead generation and marketing campaign development. Similarly, startups like DevRev are introducing conversational AI builders to design custom agents, offering enterprises up to 95% task accuracy without the need for coding. What Lies Ahead in the AI Agent Landscape? As Salesforce and Microsoft push the boundaries of AI integration, businesses are evaluating these tools for their flexibility, customization, and impact on operations. While Salesforce leads in CRM-focused AI, Microsoft’s integrated approach appeals to enterprises seeking cross-functional AI solutions. In the end, the winner may be determined not by flashy features but by delivering tangible, transformative outcomes for businesses navigating the complexities of AI adoption. AI Agent Rivalry. 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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November 16, 2024in Agentforce Platform, Data, Enterprise, Generative AI, Google, Salesforce0 Comments
Salesforce AI Research Introduces LaTRO
14Nov

Salesforce AI Research Introduces LaTRO

Salesforce AI Research Introduces LaTRO: A Breakthrough in Enhancing Reasoning for Large Language Models Large Language Models (LLMs) have revolutionized tasks such as answering questions, generating content, and assisting with workflows. However, they often struggle with advanced reasoning tasks like solving complex math problems, logical deduction, and structured data analysis. Salesforce AI Research has addressed this challenge by introducing LaTent Reasoning Optimization (LaTRO), a groundbreaking framework that enables LLMs to self-improve their reasoning capabilities during training. The Need for Advanced Reasoning in LLMs Reasoning—especially sequential, multi-step reasoning—is essential for tasks that require logical progression and problem-solving. While current models excel at simpler queries, they often fall short in tackling more complex tasks due to a reliance on external feedback mechanisms or runtime optimizations. Enhancing reasoning abilities is therefore critical to unlocking the full potential of LLMs across diverse applications, from advanced mathematics to real-time data analysis. Existing techniques like Chain-of-Thought (CoT) prompting guide models to break problems into smaller steps, while methods such as Tree-of-Thought and Program-of-Thought explore multiple reasoning pathways. Although these techniques improve runtime performance, they don’t fundamentally enhance reasoning during the model’s training phase, limiting the scope of improvement. Salesforce AI Research Introduces LaTRO: A Self-Rewarding Framework LaTRO shifts the paradigm by transforming reasoning into a training-level optimization problem. It introduces a self-rewarding mechanism that allows models to evaluate and refine their reasoning pathways without relying on external feedback or supervised fine-tuning. This intrinsic approach fosters continual improvement and empowers models to solve complex tasks more effectively. How LaTRO Works LaTRO’s methodology centers on sampling reasoning paths from a latent distribution and optimizing these paths using variational techniques. Here’s how it works: This self-rewarding cycle ensures that the model continuously refines its reasoning capabilities during training. Unlike traditional methods, LaTRO’s framework operates autonomously, without the need for external reward models or costly supervised feedback loops. Key Benefits of LaTRO Performance Highlights LaTRO’s effectiveness has been validated across various datasets and models: Applications and Implications LaTRO’s ability to foster logical coherence and structured reasoning has far-reaching applications in fields requiring robust problem-solving: By enabling LLMs to autonomously refine their reasoning processes, LaTRO brings AI closer to achieving human-like cognitive abilities. The Future of AI with LaTRO LaTRO sets a new benchmark in AI research by demonstrating that reasoning can be optimized during training, not just at runtime. This advancement by Salesforce AI Research highlights the potential for self-evolving AI models that can independently improve their problem-solving capabilities. Salesforce AI Research Introduces LaTRO As the field of AI progresses, frameworks like LaTRO pave the way for more autonomous, intelligent systems capable of navigating complex reasoning tasks across industries. LaTRO represents a significant leap forward, moving AI closer to achieving true autonomous reasoning. 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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November 14, 2024in Data0 Comments
copilots and agentic ai
13Nov

Copilots and Agentic AI

Agentic AI vs. Copilots: Defining the Future of Generative AI Artificial Intelligence has rapidly evolved, progressing from simple automation to generative models, to copilots. But now, a new player—Agentic AI—has emerged, promising to redefine the AI landscape. Is Agentic AI the next logical step, or will it coexist alongside copilots, each serving distinct roles? Copilots and Agentic AI. Generative AI: Creativity with a Human Touch Since the launch of ChatGPT, generative AI has dominated tech priorities, offering businesses the ability to generate content—text, images, videos, and more—from pre-defined data. However, while revolutionary, generative AI still relies heavily on human input to guide its output, making it a powerful collaborator rather than an autonomous actor. Enter Agentic AI: Autonomy Redefined Agentic AI represents a leap forward, offering systems that possess autonomy and the ability to act independently to achieve pre-defined goals. Unlike generative AI copilots that respond to human prompts, Agentic AI makes decisions, plans actions, and learns from experience. Think of it as Siri or Alexa—enhanced with autonomy and learning capabilities. Gartner recently spotlighted Agentic AI as its top technology trend for 2025, predicting that by 2028, at least 15% of day-to-day work decisions will be made autonomously, up from virtually none today. Agentforce and the Third Wave of AI Salesforce’s “Agentforce,” unveiled at Dreamforce, is a prime example of Agentic AI’s potential. These autonomous agents are designed to augment employees by handling tasks across sales, service, marketing, and commerce. Salesforce CEO Mark Benioff described it as the “Third Wave of AI,” going beyond copilots to deliver intelligent agents deeply embedded into customer workflows. Salesforce aims to empower one billion AI agents by 2025, integrating Agentforce into every aspect of customer success. Benioff took a swipe at competitors’ bolt-on generative AI solutions, emphasizing that Agentforce is deeply embedded for maximum value. The Role of Copilots: Collaboration First While Agentic AI gains traction, copilots like Microsoft’s Copilot Studio and SAP’s Joule remain critical for businesses focused on intelligent augmentation. Copilots act as productivity boosters, working alongside humans to optimize processes, enhance creativity, and provide decision-making support. SAP’s Joule, for example, integrates seamlessly into existing systems to optimize operations while leaving strategic decision-making in human hands. This collaborative model aligns well with businesses prioritizing agility and human oversight. Agentic AI: Opportunities and Challenges Agentic AI’s autonomy offers significant potential for streamlining complex processes, reducing human intervention, and driving productivity. However, it also comes with risks. Eleanor Watson, AI ethics engineer at Singularity University, warns that Agentic AI systems require careful alignment of values and goals to avoid unintended consequences like dangerous shortcuts or boundary violations. In contrast, copilots retain human agency, making them particularly suited for creative and knowledge-based roles where human oversight remains essential. Copilots and Agentic AI The choice between Agentic AI and copilots hinges on an organization’s priorities and risk tolerance. For simpler, task-specific applications, copilots excel by providing assistance without removing human input. Agentic AI, on the other hand, shines in complex, multi-task scenarios where autonomy is key. Dom Couldwell, head of field engineering EMEA at DataStax, emphasizes the importance of understanding when to deploy each model. “Use a copilot for specific, focused tasks. Use Agentic AI for complex, goal-oriented processes involving multiple tasks. And leverage Retrieval Augmented Generation (RAG) in both to provide context to LLMs.” The Road Ahead: Coexistence or Dominance? As AI evolves, Agentic AI and copilots may coexist, serving complementary roles. Businesses seeking full automation and scalability may gravitate toward Agentic AI, while those prioritizing augmented intelligence and human collaboration will continue to rely on copilots. Ultimately, the future of AI will be defined not by one model overtaking the other, but by how well each aligns with the specific needs, goals, and challenges of the organizations adopting them. 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

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November 13, 2024in Artificial Intelligence, Data, Generative AI, Salesforce, Technology0 Comments
2025: The Rise of AI Agents and Industry-Focused Innovation
13Nov

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

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

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November 13, 2024in Agentforce Platform, Data, Enterprise, Generative AI, Salesforce, Technology0 Comments
Scarf and Salesforce
13Nov

Scarf and Salesforce

Scarf Integrates Open Source Software Tracking Platform with Salesforce At KubeCon + CloudNativeCon 2024, Scarf announced the integration of its open-source software usage tracking platform with Salesforce CRM. This integration arrives as debates around the definition and economics of open source remain a hot topic in the tech community. Scarf also introduced updates to its platform, including enhanced event data correction and flagging capabilities for improved accuracy in company matching and attribution. New data filtering options were also added for more refined data exports. The Scarf platform enables IT vendors to identify organizations consuming open-source software at significant scale, presenting opportunities to offer additional support or promote commercial add-ons for open-source tools. To date, the Scarf gateway has tracked over seven billion events, connecting usage data to specific organizations via attributes such as internet addresses. Strengthening the Open Source Ecosystem Scarf CEO Avi Press emphasized the platform’s role in maintaining the economic viability of the open-source ecosystem, often in partnership with organizations like The Linux Foundation. Without these insights, fewer IT vendors would sponsor open-source projects, Press noted, which would hinder the ecosystem’s growth and sustainability. However, the open-source community frequently experiences friction. Licensing changes by IT vendors often lead to project forks, with contributors reverting to previous licensing terms, sometimes backed by cloud providers. Press believes targeted commercial value opportunities—supported by tools like Scarf—can reduce this friction by fostering more productive engagements between vendors and organizations. Challenges and Evolving Definitions in Open Source While open source remains foundational to the tech world, it continues to face ideological and practical challenges. For decades, debates over licensing models have sparked disagreements, including the current contention around defining open-source AI models. Many models fail to disclose critical training details, leading to further disputes. Ultimately, each organization must navigate these issues by adopting its own definition of open source and deciding how best to support the ecosystem. Tools like Scarf’s platform aim to bridge gaps, enabling IT vendors and organizations to collaborate more effectively, ensuring the continued growth of open source. 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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November 13, 2024in Data, Salesforce, Technology0 Comments
13Nov

AI’s Impact on Future Information Ecosystems

AI’s Impact on Future Information Ecosystems The proliferation of generative AI technology has ignited a renewed focus within the media industry on how to strategically adapt to its capabilities. Media professionals are now confronted with crucial questions: What are the most effective ways to leverage this technology for efficiency in news production and to enhance audience experiences? Conversely, what threats do these technological advancements pose? Is legacy media on the brink of yet another wave of disintermediation from its audiences? Additionally, how does the evolution of technology impact journalism ethics? AI’s Impact on Future Information Ecosystems. In response to these challenges, the Open Society Foundations (OSF) launched the AI in Journalism Futures project earlier this year. The first phase of this ambitious initiative involved an open call for participants to develop future-oriented scenarios that explore the potential driving forces and implications of AI within the broader media ecosystem. The project sought to answer questions about what might transpire among various stakeholders in 5, 10, or 15 years. As highlighted by Nick Diakopoulos, scenarios are a valuable method for capturing a diverse range of perspectives on complex issues. While predicting the future is not the goal, understanding a variety of plausible alternatives can significantly inform current strategic thinking. Ultimately, more than 800 individuals from approximately 70 countries contributed short scenarios for analysis. The AI in Journalism Futures project subsequently utilized these scenarios as a foundation for a workshop, which refined the ideas outlined in their report. Diakopoulos emphasizes the importance of examining this broad set of initial scenarios, which OSF graciously provided in anonymized form. This analysis specifically explores (1) the various types of impacts identified within the scenarios, (2) the associated timeframes for these impacts—whether they are short, medium, or long-term, and (3) the global differences in focus across regions, highlighting how different parts of the world emphasized distinct types of impacts. While many additional questions could be explored regarding this data—such as the drivers of impacts, final outcomes, severity, stakeholders involved, or technical capabilities emphasized—this analysis focuses primarily on impacts. Refining the Data The initial pool of 872 scenarios underwent a rigorous process of cleaning, filtering, transformation, and verification before analysis. Firstly, scenarios shorter than 50 words were excluded from consideration, resulting in 852 scenarios for analysis. Additionally, 14 scenarios that were not written in English were translated using Google Sheets. To enable geographic and temporal analysis, the country of origin for each scenario writer was mapped to their respective continents, and the free-text “timeframe” field was converted into numerical representations of years. Next, impacts were extracted from each scenario using an LLM (GPT-4 in this case). The prompts for the LLM were refined through iteration, with a clear definition established for what constitutes an “impact.” Diakopoulos defined an impact as “a significant effect, consequence, or outcome that an action, event, or other factor has in the scenario.” This definition encompasses not only the ultimate state of a scenario but also intermediate outcomes. The LLM was instructed to extract distinct impacts, with each impact represented by a one-sentence description and a short label. For instance, one impact could be described as, “The proliferation of flawed AI systems leads to a compromised information ecosystem, causing a general doubt in the reliability of all information,” labeled as “Compromised Information Ecosystem.” To ensure the accuracy of this extraction process, a random sample of five scenarios was manually reviewed to validate the extracted impacts against the established definition. All extracted impacts passed the checks, leading to confidence in scaling the analysis across the entire dataset. This process resulted in the identification of 3,445 impacts from the 852 scenarios. AI’s Impact on Future Information Ecosystems A typology of impact types was developed based on the 3,445 impact descriptions, utilizing a novel method for qualitative thematic analysis from a Stanford University study. This approach clusters input texts, synthesizes concepts that reflect abstract connections, and produces scoring definitions to assess the relevance of each original text. For example, a concept like “AI Personalization” might be defined by the question, “Does the text discuss how AI personalizes content or enhances user engagement?” Each impact description was then scored against these concepts to tabulate occurrence frequencies. Impacts of AI on Media Ecosystems Through this analytical approach, 19 impact themes emerged, along with their corresponding scoring definitions: Interestingly, many scenarios articulated themes around how AI intersects with fact-checking, trust, misinformation, ethics, labor concerns, and evolving business models. Although some concepts may not be entirely distinct, this categorization offers a meaningful overview of the key ideas represented in the data. Distribution of Impact Themes Comparing these findings with those in the OSF report reveals some discrepancies. For instance, while the report emphasizes personalization and misinformation, these themes were less prevalent in the analyzed scenarios. Moreover, themes such as the rise of AI agents and audience fragmentation were mentioned but did not cluster significantly in the analysis. To capture potentially interesting but less prevalent impacts, the clustering was rerun with a smaller minimum cluster size. This adjustment yielded hundreds more concept themes, revealing insights into longer-tail issues. Positive visions for generative AI included reduced language barriers and increased accessibility for marginalized audiences, while concerns about societal fragmentation and privacy were also raised. Impacts Over Time and Around the World The analysis also explored how the impacts varied based on the timeframe selected by writers and their geographic locations. Using a Chi-Squared test, it was determined that “AI Personalization” trends towards long-term implications, while both “AI Fact-Checking” and “AI and Misinformation” skew toward shorter-term issues. This suggests that scenario writers perceive misinformation impacts as imminent threats, likely reflecting ongoing developments in the media landscape. When examining the distribution of impacts by region, it was found that “AI Fact-Checking” was more frequently noted by writers from Africa and Asia, while “AI and Misinformation” was less prevalent in scenarios from African writers but more so in those from Asian contributors. This indicates a divergence in perspectives on AI’s role in the media ecosystem.

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November 13, 2024in AI Tools, Data, Generative AI, Google, Salesforce, Technology0 Comments
Commerce Cloud and Agentic AI
12Nov

Gen X and Millennials Lead in Embracing Agentic AI

Gen X and Millennials Lead in Embracing Agentic AI: Salesforce Report Generation X and millennials are showing greater openness to adopting agentic artificial intelligence (AI), according to Salesforce’s State of the AI Connected Customer report. Agentic AI refers to autonomous agents capable of independently making decisions and performing tasks, learning and adapting from experiences without direct human supervision. This technology is making significant inroads across industries, with applications ranging from personalized recommendations and inventory management in retail to supply chain optimization in logistics. It also finds use in healthcare, finance, telecom, IT, and customer service. Generational Differences in AI Adoption The report highlights that millennials (57%) and Gen Xers (58%) in India are more inclined to embrace AI agents for faster and more proactive customer service compared to Gen Z (51%) and Baby Boomers (42%). These autonomous agents enhance customer experiences by delivering personalized and relevant content, which resonates more with the tech-savvy Gen X and millennial demographics. Who Are These Generations? Building Trust in the AI Era The report reveals a sharp decline in consumer trust, with trust levels at their lowest in eight years. Over half of the respondents feel companies are less trustworthy than a year ago and believe businesses mishandle customer data. Arun Parameswaran, SVP & Managing Director, Sales and Distribution at Salesforce India, emphasized the critical role of trust in AI strategies: “As we enter a new era of intelligent customer engagement, brands that prioritize trust in their AI strategies will be best positioned to deliver impactful, lasting connections.” Transparency, according to the report, is key to restoring consumer confidence in the AI-driven era. Companies that adopt responsible AI practices, particularly in the design and deployment of agentic AI, can foster stronger customer relationships. Global Perspective The findings are based on a survey of 15,015 consumers across India, Australia, Brazil, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Netherlands, Norway, Singapore, Spain, Sweden, the UK, and the US. As businesses increasingly integrate agentic AI into their operations, understanding generational attitudes and prioritizing ethical AI practices will be essential for fostering trust and delivering exceptional customer experiences. 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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November 12, 2024in Artificial Intelligence, Data, Salesforce, Technology0 Comments
Enterprises are Adopting AI-powered Automation Platforms
12Nov

Enterprises are Adopting AI-powered Automation Platforms

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

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November 12, 2024in Agentforce Platform, Artificial Intelligence, Data, Enterprise, Salesforce, Technology0 Comments
Snowflake Security and Development
12Nov

Snowflake Security and Development

Snowflake Unveils AI Development and Enhanced Security Features At its annual Build virtual developer conference, Snowflake introduced a suite of new capabilities focused on AI development and strengthened security measures. These enhancements aim to simplify the creation of conversational AI tools, improve collaboration, and address data security challenges following a significant breach earlier this year. AI Development Updates Snowflake announced updates to its Cortex AI suite to streamline the development of conversational AI applications. These new tools focus on enabling faster, more efficient development while ensuring data integrity and trust. Highlights include: These features address enterprise demands for generative AI tools that boost productivity while maintaining governance over proprietary data. Snowflake aims to eliminate barriers to data-driven decision-making by enabling natural language queries and easy integration of structured and unstructured data into AI models. According to Christian Kleinerman, Snowflake’s EVP of Product, the goal is to reduce the time it takes for developers to build reliable, cost-effective AI applications: “We want to help customers build conversational applications for structured and unstructured data faster and more efficiently.” Security Enhancements Following a breach last May, where hackers accessed customer data via stolen login credentials, Snowflake has implemented new security features: These additions come alongside existing tools like the Horizon Catalog for data governance. Kleinerman noted that while Snowflake’s previous security measures were effective at preventing unauthorized access, the company recognizes the need to improve user adoption of these tools: “It’s on us to ensure our customers can fully leverage the security capabilities we offer. That’s why we’re adding more monitoring, insights, and recommendations.” Collaboration Features Snowflake is also enhancing collaboration through its new Internal Marketplace, which enables organizations to share data, AI tools, and applications across business units. The Native App Framework now integrates with Snowpark Container Services to simplify the distribution and monetization of analytics and AI products. AI Governance and Competitive Position Industry analysts highlight the growing importance of AI governance as enterprises increasingly adopt generative AI tools. David Menninger of ISG’s Ventana Research emphasized that Snowflake’s governance-focused features, such as LLM observability, fill a critical gap in AI tooling: “Trustworthy AI enhancements like model explainability and observability are vital as enterprises scale their use of AI.” With these updates, Snowflake continues to compete with Databricks and other vendors. Its strategy focuses on offering both API-based flexibility for developers and built-in tools for users seeking simpler solutions. By combining innovative AI development tools with robust security and collaboration features, Snowflake aims to meet the evolving needs of enterprises while positioning itself as a leader in the data platform and AI space. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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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November 12, 2024in AI Tools, Data, Enterprise, Generative AI, Snowflake0 Comments
healthcare Can prioritize ai governance
11Nov

Healthcare Can Prioritize AI Governance

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

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November 11, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Laws, Life Sciences Cloud, Salesforce, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Technology0 Comments
Pioneering AI-Driven Customer Engagement
11Nov

Pioneering AI-Driven Customer Engagement

With Salesforce at the forefront of the AI revolution, Agentforce, introduced at Dreamforce, represents the next phase in customer service automation. It integrates AI and human collaboration to automate repetitive tasks, freeing human talent for more strategic activities, ultimately improving customer satisfaction. Tallapragada emphasized how this AI-powered tool enables businesses, particularly in the Middle East, to scale operations and enhance efficiency, aligning with the region’s appetite for growth and innovation.

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November 11, 2024in Agentforce Platform, Data, Generative AI, Hyperforce, Salesforce Development Services0 Comments
Cybersecurity
05Nov

Cybersecurity Regulations for Hospitals

Beyond the 72-hour reporting requirement, which took effect on October 2, 2024, hospitals must implement key cybersecurity measures, such as multifactor authentication and a robust incident response plan, by October 2025. These regulations currently apply only to general hospitals, excluding other healthcare facilities like nursing homes and diagnostic centers.

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November 5, 2024in Data, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Salesforce Optimization, Salesforce Shield, Technology
Martech Modernization
04Nov

Martech Modernization

Martech Modernization: The Trends, Challenges, and Opportunities A Snapshot of Martech Strategies and CDP Adoption According to research by Acxiom, 65% of companies with a defined martech strategy utilize a Customer Data Platform (CDP), compared to just 43% without one. This significant gap underscores the strategic role of CDPs in martech adoption. Additionally, nearly all businesses surveyed plan to revise their martech stack within the next 12 months, with 29% adding new tools and 15% consolidating existing ones. The Transformative Marketing Landscape Rapid technological advancements, changing customer expectations, and evolving identity ecosystems are fundamentally reshaping how brands engage their audiences. In this environment, modernizing martech has become essential for delivering the personalized experiences customers demand. However, modernization presents challenges such as siloed data, fragmented technologies, and gaps in expertise, complicating efforts to adapt. To gain insights into these dynamics, Acxiom surveyed 200 martech decision-makers from the US and UK about their modernization plans, motivations, and obstacles. The findings reveal a widespread push for martech updates, with brands seeking support to navigate this complex journey. SECTION ONE: A Martech Reset is Underway Martech Strategy Rises to the Top When asked to prioritize their marketing objectives, 34% of respondents listed developing a martech strategy among their top three goals. This places it alongside traditional objectives like increasing brand awareness and customer acquisition, reflecting its growing importance in achieving broader marketing goals. Even considering that survey respondents may prioritize martech more than the average business leader, the results highlight the industry’s dynamism and the pressing need for a martech reset. Widespread Stack Adjustments Nearly all surveyed businesses (99%) plan to adjust their martech stack in the next year. Key trends include: This widespread activity emphasizes the high priority placed on martech optimization. Streamlining and Experimentation Some organizations focus on refining their existing stacks, while others are piloting new platforms: C-Suite Engagement Martech modernization has also captured the attention of executive leadership. 60% of respondents noted that martech has become a higher priority for their C-suite in recent years, particularly in smaller companies leveraging technology to maximize resources and compete with larger rivals. Budget Increases Despite Economic Pressures In a challenging economic climate, 65% of respondents expect their martech budgets to grow over the next year, while only 10% foresee cuts. This trend reflects the recognition of martech as a strategic investment critical for maintaining competitiveness. SECTION TWO: Drivers of Martech Modernization Why Modernize? Modernization efforts are driven by a mix of goal-oriented and technical motivations. Key drivers include: Secondary motivations include streamlining integration, ensuring regulatory compliance, and reducing operational complexity. Key Takeaways As martech modernization accelerates, businesses must balance innovation with strategic planning to navigate this transformative era successfully. 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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November 4, 2024in Data, Technology
Clean Energy Transition Campaign
04Nov

Clean Energy Transition Campaign

EnergyFlex Launches Clean Energy Transition Campaign in Partnership with Carat SA EnergyFlex, an Australian veteran-owned energy analytics and renewables education company, has joined forces with Carat SA to launch its first-ever brand campaign. This initiative is designed to accelerate Australia’s clean energy transition by equipping individuals and businesses with the tools, knowledge, and confidence to become “Renewables Ready.” Empowering Australians with Free Energy Tools Founded in 2021, EnergyFlex aims to put every Australian on the path to free and clean energy. The company’s free app, launched in May, helps users: The app, available on both iOS and Android, is part of EnergyFlex’s mission to make renewable energy adoption accessible and impactful for all Australians. Voices from the Partnership Garry Harding, CEO and Co-Founder of EnergyFlex, emphasized the campaign’s focus on financial, community, and environmental benefits: “We want to make it as easy as possible for Australians to understand the positive impact of the renewable energy transition. Partnering with Carat SA enables us to raise awareness and bring these tools and education to homes and businesses across the country.” Adele Gibb, Managing Director of Carat SA, highlighted the synergy between the campaign and Carat’s values: “Working with a forward-thinking brand like EnergyFlex aligns perfectly with dentsu’s B2B2S philosophy—creating solutions that are good for business, people, and society.” About Carat SA As a leading global media agency, Carat SA operates across 190+ offices in 135+ countries, bringing expertise and innovation to drive impactful campaigns. With this collaboration, EnergyFlex and Carat SA are poised to inspire a nationwide shift toward renewable energy adoption, helping Australia lead ,.the way in sustainability. 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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November 4, 2024in Salesforce

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