The new LOS will incorporate machine learning and automation to deliver real-time credit assessments, enabling faster loan processing and competitive interest rates, alongside improved credit risk insights. This strategic partnership underscores Mahindra Finance’s dedication to providing responsible financing solutions to India’s emerging MSME sector.
10 Top AI Jobs in 2025 As we approach 2025, the demand for AI expertise is on the rise. Companies are seeking professionals with a strong background in AI, paired with practical experience. This insight explores 10 of the top AI jobs, the skills they require, and the industries that are driving AI adoption. If you are of the camp worrying about artificial intelligence replacing you, read on to see how you can leverage AI to upskill your career. AI is increasingly becoming an integral part of our lives, influencing various sectors from healthcare and finance to manufacturing, retail, and education. It is automating routine tasks, enhancing user experiences, and improving decision-making processes. AI is transitioning from data centers into everyday devices such as smartphones, IoT devices, and autonomous vehicles, becoming more efficient and safer thanks to advancements in real-time processing, lower latency, and enhanced privacy measures. The ethical use of AI is also at the forefront, emphasizing fairness, transparency, and accountability in AI models and decision-making processes. This proactive approach to ethics contrasts with past technological advancements, where ethical considerations often lagged behind. The rapid growth of AI translates to an increasing number of job opportunities. Below, we discuss the skills sought in AI specialists, the industries adopting AI at a fast pace, and a rundown of the 10 hottest AI jobs for 2025. Top AI Job Skills While many programmers are self-taught, the AI field demands a higher level of expertise. An analysis of 15,000 job postings found that 77% of AI roles require a master’s degree, while only 8% of positions are available to candidates with just a high school diploma. Most job openings call for mid-level experience, with only 12% for entry-level roles. Interestingly, while remote work is common in IT, only 11% of AI jobs offer fully remote positions. Being a successful AI developer requires more than coding skills; proficiency in core AI programming languages (like Python, Java, and R) is essential. Additional skills in communication, digital marketing strategies, effective collaboration, and analytical abilities are also critical. Moreover, a basic understanding of psychology is beneficial for simulating human behavior, and knowledge of AI security, privacy, and ethical practices is increasingly necessary. Industries Embracing AI Certain sectors are rapidly adopting AI technologies, including: 10 Top AI Jobs AI job roles are evolving quickly. Specialists are increasingly in demand over generalists, with a focus on deep knowledge in specific areas. Here are 10 promising AI job roles for 2025, along with their expected salaries based on job postings. As AI continues to evolve, these roles will play a pivotal part in shaping the future of various industries. Preparing for a career in AI requires a combination of technical skills, ethical understanding, and a willingness to adapt to new technologies. As we’ve seen with Salesforce a push for upskilling in artificial intelligence is here. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
Don’t get spooked – OpenAI introduces Canvas—a fresh interface for collaborative writing and coding with ChatGPT, designed to go beyond simple conversation. Canvas opens in a separate window, enabling you and ChatGPT to work on projects side by side, creating and refining ideas in real time. This early beta provides an entirely new way of collaborating with AI—combining conversation with the ability to edit and enhance content together. Built on GPT-4o, Canvas can be selected in the model picker during the beta phase. Starting today, we’re rolling it out to ChatGPT Plus and Team users globally, with Enterprise and Education users gaining access next week. Once out of beta, Canvas will be available to all ChatGPT Free users. Enhancing Collaboration with ChatGPT While ChatGPT’s chat interface works well for many tasks, projects requiring editing and iteration benefit from more. Canvas provides a workspace designed for such needs. Here, ChatGPT can better interpret your objectives, offering inline feedback and suggestions across entire projects—similar to a copy editor or code reviewer. You control every aspect in Canvas, from direct editing to leveraging shortcuts like adjusting text length, debugging code, or quickly refining writing. You can also revert to previous versions with Canvas’s back button. OpenAI Introduces Canvas Canvas opens automatically when ChatGPT detects an ideal scenario, or you can prompt it by typing “use Canvas” in your request to begin working collaboratively on an existing project. Writing Shortcuts Include: Coding in Canvas Canvas makes coding revisions more transparent, streamlining the iterative coding process. Track ChatGPT’s edits more clearly and take advantage of features that make debugging and revising code simpler. OpenAI Introduces Canvas to a world of new possibilities for truly developing and working with artificial intelligence. Coding Shortcuts Include: Training the Model to Collaborate GPT-4o has been optimized to act as a collaborative partner, understanding when to open a Canvas, make targeted edits, or fully rewrite content. Our team implemented core behaviors to support a seamless experience, including: These improvements are backed by over 20 internal automated evaluations and refined with synthetic data generation techniques, allowing us to enhance response quality and interaction without relying on human-generated data. Key Challenges as OpenAI Introduces Canvas A core challenge was determining when to trigger Canvas. We trained GPT-4o to recognize prompts like “Write a blog post about the history of coffee beans” while avoiding over-triggering for simple Q&A requests. For writing tasks, we reached an 83% accuracy in correct Canvas triggers, and a 94% accuracy in coding tasks compared to baseline models. Fine-tuning continues to ensure targeted edits are favored over full rewrites when needed. Finally, improving comment generation required iterative adjustments and human evaluations, with the integrated Canvas model now outperforming baseline GPT-4o in accuracy by 30% and quality by 16%. What’s Next Canvas is the first major update to ChatGPT’s visual interface since launch, with more enhancements planned to make AI more versatile and accessible. Canvas is also integrated with Salesforce. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more
In 1963, Peter Drucker wrote one of the most influential articles on business, Managing for Business Effectiveness. Much like Fred Brooks’ 1975 classic, The Mythical Man-Month, it has profound lessons. However, through today’s lens of AI and automation, it seems we may have misinterpreted Drucker’s insights, inadvertently industrializing the problem rather than solving it. Pitfall of process optimization. Pitfalls of process optimization. One pivotal point from Drucker’s essay (highlighted by Dave Duggal) is: “The major problem is the confusion between effectiveness and efficiency. There is nothing more useless than doing efficiently what should not be done at all. Yet our tools — especially accounting concepts and data — all focus on efficiency. What we need is a way to identify areas of effectiveness and a method to concentrate on them.” While Drucker emphasized focusing on results and making data-driven decisions, his warning that “our data and accounting focus on efficiency” has been largely overlooked. Instead of addressing this, businesses have industrialized the pursuit of efficiency at the expense of effectiveness. The Efficiency Trap Drucker’s assertion that “there is nothing more useless than doing with great efficiency what should not be done at all” remains true, yet much of the business and IT landscape has fixated on eliminating steps, even if the return on this effort is minimal. He warned that too much focus is placed on problems rather than opportunities and on areas where even exceptional performance yields little impact. This mirrors many process optimization efforts, where the goal is often to remove unnecessary steps, focusing on efficiency rather than true effectiveness. The Pitfall of Process Optimization Entire business methodologies were built around simplifying processes and eliminating redundant steps. Companies created cultures centered on optimization, believing that by cutting out inefficiencies, they would achieve success. Yet, as Drucker noted, this focus on efficiency has often resulted in neglecting broader opportunities. Poor Data, Poor Outcomes Drucker’s concerns about tools and data have proven strangely prophetic. Instead of focusing on effectiveness, many organizations now face data problems, often rooted in over-optimized processes. Some of the firms most dedicated to process optimization are the very ones known for slow responses to market changes, as their data fails to keep pace with business needs. Focusing on Process, Missing the Bigger Picture When businesses focus narrowly on processes, they overlook key information needed downstream. This might improve micro-level efficiency, but it often damages macro-level outcomes. For instance, optimizing an order submission process may mean critical data isn’t captured, leading to issues further along in the supply chain. This process-driven thinking fosters data silos—disconnected systems that, while progressing individual steps, fail to offer the necessary insights for broader business decisions. Effectiveness Requires Understanding Reality AI amplifies these challenges. To fully leverage AI, businesses must shift from process-centric to reality-based thinking. Companies that can manage their digital reality, enabling AI to make smart, outcome-driven decisions, will outperform those stuck in outdated process mentalities. AI won’t just optimize individual steps like restocking inventory; it will manage complex tasks such as provisioning networks, negotiating with suppliers, or resolving customer complaints. To support this, businesses must move beyond step-based optimization and embrace new approaches that focus on multi-dimensional KPIs and AI-driven outcomes. A Shift from Process to Reality The future of business optimization will require understanding KPIs in a multi-dimensional way, embedding AI into operations, and allowing it to drive business outcomes. This will necessitate a shift in data architecture, with a focus on operational reality rather than reporting. The Dangers of Ignoring the Shift Businesses that cling to process thinking may find isolated success with AI but risk falling behind competitors that embrace a broader transformation. Like retailers who tried to compete with Amazon by merely launching websites without addressing underlying fulfillment challenges, companies may see short-term gains but falter in the long run. The Cultural Challenge of Transformation Switching from process-focused thinking to a reality-based approach will be difficult. Since Drucker’s 1963 essay, the industrialization of step-elimination has become deeply ingrained in business culture. Processes are comfortable; they allow for focused problem-solving in isolated areas. Moving to a mindset that prioritizes operational reality, dependencies, and cross-functional collaboration is a significant cultural shift. Embracing the Change However, the businesses that make this transition will gain a competitive advantage. Those that recognize the scale of change required—making cultural, organizational, and architectural shifts—will operate in a different league than those who don’t. By shifting from efficiency-driven processes to reality-based effectiveness, organizations can unlock the full potential of AI, ensuring not just operational improvements but transformational business success. You can avoid the pitfalls of process optimization. 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
Intelligent Adoption Framework Marks a New Era for AI IntegrationAfter a surge of initial excitement, AI has now entered a phase of more thoughtful and strategic adoption, focusing on sustainable progress and measurable results. Following years of hype in which artificial intelligence was hailed as a revolutionary force poised to instantly transform industries, AI is now facing a more tempered reality. As it settles into Gartner’s “Trough of Disillusionment,” organizations are grappling with the reality of high costs and challenges scaling experimental projects. However, this phase of learning is typical for any emerging technology, and the journey to unlock AI’s full potential is far from over. Steve Daly, Senior Vice President of Solutions at New Era Technology, explains: “AI has been around for 70 years, but the recent hype inflated expectations. At $30 per user per month for tools like Microsoft 365 Copilot, they’re appealing for proof-of-concept projects. But once those initial tests are over, many companies struggle to find a clear ROI when scaling.” Cost is not the only barrier to broader AI adoption. Concerns over data security and sharing sensitive information are top priorities for many organizations. Daly adds, “New Era’s robust data and security practice has shifted to offer Copilot Studio, allowing companies to build GenAI solutions with tighter security controls. With Copilot Studio, you can limit access to specific files or libraries, ensuring greater control over sensitive data.” Moving Beyond OverpromisesBuilding confidence in AI requires addressing several factors. First, organizations must tackle security and data control issues, alongside developing a clear business model to justify AI investments. Equally important is maintaining momentum—patience and persistence are key to seeing projects through to success, or determining when to pivot. Daly observes, “We’re seeing many projects lose steam. Around half of AI initiatives stall due to poor security practices and suboptimal data management. Projects must demonstrate progress, and that’s difficult in the innovation phase when you don’t always know what you don’t know.” Introducing Intelligent AdoptionThis is where Copilot Studio and New Era’s Intelligent Adoption Framework come into play. The framework is designed to help organizations chart their AI development journey and ensure investments yield tangible results. Copilot Studio supports IT teams by focusing on the tasks that truly drive value, helping them stay on track toward their goals. The Intelligent Adoption Framework is built around three core pillars: technical redesign, organizational readiness, and user readiness. New Era’s framework leverages its expertise to guide businesses through the steps necessary to define their AI strategy, align their corporate vision, and identify the most valuable use cases for AI adoption. Daly concludes, “It’s not just about purchasing licenses—it’s about creating a roadmap for successful adoption. We’re developing packaged solutions, such as ‘train the trainer’ programs from day one, followed by proof-of-concept demonstrations using Copilot Studio. Our goal is to help customers answer key questions, like when to build a GenAI chatbot, while navigating the complexities of AI adoption and managing the pressures CIOs face from stakeholders.” In this new era of AI, success will be determined not by rushed deployment, but by strategic, intelligent adoption that ensures sustained value over time. 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
Salesforce Launches Agentforce Platform to Revolutionize Autonomous AI Agents Salesforce has announced the general availability of Agentforce, a transformative platform that empowers businesses to create, test, and deploy autonomous AI agents seamlessly integrated into their Salesforce workflows. Key Features of Agentforce Agentforce combines cutting-edge technology with user-friendly design, enabling businesses to: The agents also suggest knowledge, actions, and guardrails to optimize performance, enhancing operational efficiency across customer service, sales, and other functions. Real-World Applications During Dreamforce 2024, Salesforce customers built over 10,000 AI Agents to support a wide range of industries and use cases, including: Revolutionizing AI in Business Agentforce represents a shift from generation-based AI to action-based AI, emphasizing solutions that actively drive business outcomes. Salesforce CEO Marc Benioff described the platform as a game-changer, stating: “Agentforce is redefining what’s possible, ushering in a new era of AI abundance and limitless workforces that augment every employee, build deeper customer relationships, and drive unprecedented growth and profitability.” Built on a Unified Salesforce Ecosystem Agentforce is fully integrated with Salesforce’s robust platform, offering seamless connections to enterprise data, metadata, AI models, workflows, security, and applications. The platform leverages Data Cloud, enabling AI Agents to access unified data and collaborate across Salesforce apps. This integration ensures businesses can scale AI capabilities across the entire customer experience ecosystem without the need for extensive customization. Support for Adoption and Growth Salesforce is rolling out several initiatives to support Agentforce users: Pricing starts at $2 per conversation, with volume discounts available. Empowering Nonprofits with Agents for Impact Salesforce is extending Agentforce’s potential through the Agents for Impact Accelerator, offering nonprofits resources to build customized AI agents. This initiative underscores Salesforce’s commitment to scaling AI’s community impact. Early Success and Adoption Leading organizations such as The Adecco Group, BACA Systems, OpenTable, Saks, and Wiley have already adopted Agentforce, leveraging it to streamline workflows and deliver impactful customer experiences. By bridging the gap between AI innovation and practical, risk-aware application, Salesforce positions Agentforce as a cornerstone of the next generation of AI-driven business solutions. 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
Primary care in the United States urgently needs a redesign, as rural hospital closures and a shortage of providers are severely limiting access for nearly one-third of the population. While advanced technologies like virtual care have helped expand primary care access, there is still a strong preference for in-person visits. To address this, healthcare providers must create a hybrid care model that integrates both virtual and in-person services to better meet patient needs. Hackensack Meridian Health, a New Jersey-based health system, has embraced an AI-based solution to establish this hybrid care model. Through a partnership with K Health, the system aims to create a seamless patient journey that fluidly transitions between virtual and in-person care as needed. According to Dr. Daniel Varga, chief physician executive at Hackensack Meridian Health, the need for this partnership became apparent during the COVID-19 pandemic, which disrupted in-person care across New Jersey. “Before the pandemic, we did zero virtual visits in our offices,” Varga said. “By early 2020, we were doing thousands per day, and we realized there was real demand for it, but we didn’t have the skill set to execute it properly.” With the support of K Health, Varga believes the health system now has the technology and expertise to integrate AI-driven virtual care into its network of 18 hospitals. However, successful implementation requires overcoming technology integration challenges. The AI-Powered Virtual Care Solution The partnership between Hackensack Meridian Health and K Health has two key components, Varga explained. The first is a 24/7 AI-driven virtual care service, and the second is a professional services agreement between K Health’s doctors and the Hackensack medical group. The AI system used in the virtual care platform is built to learn from clinical data, distinguishing it from traditional symptom-checking tools. According to K Health co-founder Ran Shaul, the AI analyzes data from patients’ EHRs and symptom inputs to provide detailed insights into the patient’s health history, giving primary care providers a comprehensive view of the patient‘s current health concerns. “We know about your chronic conditions, your recent visits, and whether you’ve followed up on key health checks like mammograms,” Shaul explained. “It creates a targeted medical chart rather than a generic symptom analysis.” In addition, K Health’s virtual physicians and Hackensack Meridian’s medical group are integrated, sharing the same tax ID and EHR system, which ensures continuity of care between virtual and in-person visits. Varga highlighted that this integration allows for seamless transitions between care settings, where virtual doctors’ notes are readily available to in-person providers the following day. “If a patient sees a virtual doctor at 2 a.m., I have the 24/7 notes right in front of me the next morning in the office,” Varga said. The service is accessible to all patients, including new patients and those recently discharged from Hackensack Meridian Health’s inpatient services who require follow-up care. Overcoming Challenges in Implementation Deploying an AI-driven virtual care system across 18 hospitals presents significant challenges, but Hackensack Meridian Health has developed several strategies to ensure smooth implementation. First, the health system provided training to all 36,000 team members to familiarize them with the platform. Additionally, a dedicated team was created to enhance collaboration between the traditional medical group and the virtual care team. One major focus was connecting hospitals and 24/7 virtual care services to ensure continuity of care for patients leaving emergency departments or being discharged from inpatient care. “Many patients don’t have a primary care doctor when they leave the hospital,” Varga explained. “With this virtual service, we can immediately book a virtual appointment for them before they leave the ED.” Provider Hybrid Care Models provide better patient care, follow-up, and outcomes. The system also offers language accessibility, with patients able to interact with the platform in Spanish and request Spanish-speaking clinicians. This feature is part of the health system’s broader strategy to break down barriers to care access and improve health equity. Improving Access and Health Equity-Provider Hybrid Care Model Shaul noted that the convenience of scheduling virtual appointments at any time helps patients who would otherwise struggle to see a doctor due to work schedules or long travel distances. The virtual care service also addresses the needs of patients with limited English proficiency, allowing them to access care in their native language. By connecting patients who lack a usual source of care with primary care providers through the virtual platform, Hackensack Meridian Health aims to close care gaps. Access to primary care is critical for improving health outcomes, yet the number of Americans with a regular source of care has dropped by 10% in the past 18 years. This decline disproportionately affects Hispanic individuals, those with lower education levels, and the uninsured. Varga emphasized that the virtual care service aligns with Hackensack’s goal of meeting patients where they are—whether virtually, in their hospitals, or at brick-and-mortar medical offices. “The reason we have such a geographically diverse spread of sites is that we believe in meeting patients where they are,” Varga said. “If that means a virtual visit, we’ll meet them there. If it means the No. 1 ranked hospital in New Jersey, we’ll meet them there. And if it’s a medical office, that’s where we’ll meet them.” Salesforce and Tectonic can help your provider solution offer the same diversity. Contact us today! Heath and Life Sciences are winning a competitive edge with Salesforce for better patient outcomes. 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
In the rapidly evolving world of large language models and generative AI, a new concept is gaining momentum: AI agents. AI Agents Interview explores. AI agents are advanced tools designed to handle complex tasks that traditionally required human intervention. While they may be confused with robotic process automation (RPA) bots, AI agents are much more sophisticated, leveraging generative AI technology to execute tasks autonomously. Companies like Google are positioning AI agents as virtual assistants that can drive productivity across industries. In this Q&A, Jason Gelman, Director of Product Management for Vertex AI at Google Cloud, shares insights into Google’s vision for AI agents and some of the challenges that come with this emerging technology. AI Agents Interview How does Google define AI agents? Jason Gelman: An AI agent is something that acts on your behalf. There are two key components. First, you empower the agent to act on your behalf by providing instructions and granting necessary permissions—like authentication to access systems. Second, the agent must be capable of completing tasks. This is where large language models (LLMs) come in, as they can plan out the steps to accomplish a task. What used to require human planning is now handled by the AI, including gathering information and executing various steps. What are current use cases where AI agents can thrive? Gelman: AI agents can be useful across a wide range of industries. Call centers are a common example where customers already expect AI support, and we’re seeing demand there. In healthcare, organizations like Mayo Clinic are using AI agents to sift through vast amounts of information, helping professionals navigate data more efficiently. Different industries are exploring this technology in unique ways, and it’s gaining traction across many sectors. What are some misconceptions about AI agents? Gelman: One major misconception is that the technology is more advanced than it actually is. We’re still in the early stages, building critical infrastructure like authentication and function-calling capabilities. Right now, AI agents are more like interns—they can assist, but they’re not yet fully autonomous decision-makers. While LLMs appear powerful, we’re still some time away from having AI agents that can handle everything independently. Developing the technology and building trust with users are key challenges. I often compare this to driverless cars. While they might be safer than human drivers, we still roll them out cautiously. With AI agents, the risks aren’t physical, but we still need transparency, monitoring, and debugging capabilities to ensure they operate effectively. How can enterprises balance trust in AI agents while acknowledging the technology is still evolving? Gelman: Start simple and set clear guardrails. Build an AI agent that does one task reliably, then expand from there. Once you’ve proven the technology’s capability, you can layer in additional tasks, eventually creating a network of agents that handle multiple responsibilities. Right now, most organizations are still in the proof-of-concept phase. Some companies are using AI agents for more complex tasks, but for critical areas like financial services or healthcare, humans remain in the loop to oversee decision-making. It will take time before we can fully hand over tasks to AI agents. AI Agents Interview What is the difference between Google’s AI agent and Microsoft Copilot? Gelman: Microsoft Copilot is a product designed for business users to assist with personal tasks. Google’s approach with AI agents, particularly through Vertex AI, is more focused on API-driven, developer-based solutions that can be integrated into applications. In essence, while Copilot serves as a visible assistant for users, Vertex AI operates behind the scenes, embedded within applications, offering greater flexibility and control for enterprise customers. The real potential of AI agents lies in their ability to execute a wide range of tasks at the API level, without the limitations of a low-code/no-code interface. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
Salesforce cites data from its research, revealing that U.S. consumers spend up to nine hours resolving a single customer service issue, with 67% ending the process frustrated. Agentforce aims to eliminate these inefficiencies by providing always-on support and actionable solutions.
Additionally, Arc’s collaboration with Perplexity elevates browsing by transforming search experiences. Perplexity functions as a personal AI research assistant, fetching and summarizing information along with sources, visuals, and follow-up questions. Premium users even have access to advanced large language models like GPT-4 and Claude. Together, Arc and Perplexity revolutionize how users navigate the web. 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
Artificial intelligence (AI) is transforming the content creation industry, sparking conversations about trust, authenticity, and the future of human creativity. As developers increasingly adopt AI tools, their trust in these technologies grows. Over 75% of developers now express confidence in AI, a trend that highlights the far-reaching potential of these advancements across industries. A study shared by Parametric Architecture underscores the expanding reliance on AI, with sectors ranging from marketing to architecture integrating these tools for tasks like design and communication. Yet, the implications for trust and authenticity remain nuanced, as stakeholders grapple with ensuring AI-driven content meets ethical and quality standards. Major players like Microsoft are capitalizing on this AI surge, offering solutions that enhance business efficiency. From automating emails to managing records, Microsoft’s tools demonstrate how AI can bridge the gap between human interaction and machine-driven processes. These advancements also intensify competition with other industry leaders, including Salesforce, as businesses seek smarter ways to streamline operations. In marketing, AI’s influence is particularly transformative. As noted by Karla Jo Helms in MarketingProfs, platforms like Google are adapting to the proliferation of AI-generated content by implementing stricter guidelines to combat misinformation. With projections suggesting that 90% of online content could be AI-generated by 2026, marketers face the dual challenge of maintaining authenticity while leveraging automation. Trust remains central to these efforts. According to Helms, “82% of consumers say brands must advertise on safe, accurate, and trustworthy content.” To meet these expectations, marketers must prioritize quality and transparency, aligning with Google’s emphasis on value-driven content over mass-produced AI outputs. This focus on trustworthiness is critical to maintaining audience confidence in an increasingly automated landscape. Beyond marketing, AI is making waves in diverse fields. In agriculture, Southern land-grant scientists are leveraging AI for precision spraying and disease detection, helping farmers reduce costs while improving efficiency. These innovations highlight how AI can drive strategic advancements even in traditional sectors. Across industries, the interplay between AI adoption and ethical content creation poses critical questions. AI should serve as a collaborator, enhancing rather than replacing human creativity. Achieving this balance requires transparency about AI’s role, along with regulatory frameworks to ensure accountability and ethical use. As AI takes center stage in content creation, industries must address challenges around trust and authenticity. The focus must shift from merely implementing AI to integrating it responsibly, fostering user confidence while maintaining the integrity of human narratives. Looking ahead, the path to success lies in balancing automation’s efficiency with genuine storytelling. By emphasizing ethical practices, clear communication about AI’s contributions, and a commitment to quality, content creators can cultivate trust and establish themselves as dependable voices in an increasingly AI-driven world. 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
Nearly half of customer service teams, over 40% of salespeople, and a third of marketers have fully implemented artificial intelligence (AI) to enhance their work. However, 77% of business leaders report persistent challenges related to trusted data and ethical concerns that could stall their AI initiatives, according to Salesforce research released today. The Trends in AI for CRM report analyzed data from multiple studies, revealing that companies are worried about missing out on the opportunities generative AI presents if the data powering large language models (LLMs) isn’t rooted in their own trusted customer records. At the same time, respondents expressed ongoing concerns about the lack of clear company policies governing the ethical use of AI, as well as the complexity of a vendor landscape where 80% of enterprises are currently using multiple LLMs. Salesforce’s Four Keys to Enterprise AI Success Why it matters: AI is one of the most transformative technologies in generations, with projections forecasting a net gain of over $2 trillion in new business revenues by 2028 from Salesforce and its network of partners alone. As enterprises across industries develop their AI strategies, leaders in customer-facing departments such as sales, service, and marketing are eager to leverage AI to drive internal efficiencies and revolutionize customer experiences. Key Findings from the Trends in AI for CRM Report Expert Perspective “This is a pivotal moment as business leaders across industries look to AI to unlock growth, efficiency, and customer loyalty,” said Clara Shih, CEO of Salesforce AI. “But success requires much more than an LLM. Enterprise deployments need trusted data, user access control, vector search, audit trails and citations, data masking, low-code builders, and seamless UI integration. Salesforce brings all of these components together with our Einstein 1 Platform, Data Cloud, Slack, and dozens of customizable, turnkey prompts and actions offered across our clouds.” 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
At the Gartner IT Symposium/Xpo 2024, industry leaders emphasized that rising energy consumption and costs are fast becoming constraints on IT capabilities. Solutions discussed include adopting acceleration technologies, exploring microgrids, and keeping an eye on emerging energy-efficient technologies. With enterprise AI applications expanding, computing demands – and the energy needed to support them – are rapidly increasing. Nvidia’s CEO, Jensen Huang, highlighted this challenge, noting that advancements in traditional computing are failing to keep pace with data processing needs. “If compute demand grows exponentially while general-purpose performance stagnates, you’ll face not just cost inflation but significant energy inflation,” he said. Huang suggested that leveraging accelerated computing can mitigate some of these impacts, improving energy efficiency. Another approach highlighted was the use of microgrids, with Gartner predicting that Fortune 500 companies will shift up to $500 billion toward such systems by 2027 to manage ongoing energy risks and AI demand. Gartner’s Daryl Plummer noted that these independent energy networks could help energy-intensive enterprises avoid dependence on strained public power grids. Hyperscalers, including major cloud providers, are already exploring alternative power sources, such as nuclear energy, to meet escalating demands. For instance, Microsoft has announced plans to source energy from the Three Mile Island nuclear plant. While emerging technologies like quantum, neuromorphic, and photonic computing offer the promise of significant energy efficiency, they’re still years away from maturity. Gartner analyst Frank Buytendijk predicted it will take five to ten years before these options become viable solutions. “Energy-efficient computing is on the horizon, but we have a ways to go,” he said. Until then, enterprises will need to consider proactive strategies to manage energy risks and costs as part of their AI and IT planning. 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
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
Deploying Salesforce Flow tests is not just about hitting “go” and hoping for the best. It requires more than simply moving automations from a Sandbox environment to production. Successful deployment demands thoughtful planning and attention to detail. In this post, we’ll dive deeper into deploying Flow tests effectively, covering key factors like independent testing and ensuring environment consistency. Building on our ongoing series, we’ll provide practical insights to help you achieve smooth deployments and reliable test execution. Key Considerations for Deploying Flow Tests Steps to Deploy Flow Tests Using Change Sets Final Thoughts Deploying Flow tests effectively is critical for maintaining the integrity of your automations across environments. Skipping the testing phase is like driving with a blindfold—one mistake could disrupt your workflows and cause chaos in critical processes. By following these guidelines, particularly focusing on independent testing and post-deployment checks, you can help ensure your Salesforce Flows continue to operate smoothly. Stay tuned for future insights for Flownatics where we’ll dive into more advanced aspects of Flow tests, helping you further optimize your Salesforce automation processes. Need more advice on testing your automations in Salesforce? Let’s chat! 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
