Artificial Intelligence Archives

21Mar

Salesforce and Singapore Airlines

Singapore Airlines (SIA), a Headline Partner of the APEX FTE Asia Expo in Singapore on 11-12 November 2025, is teaming up with Salesforce to co-develop cutting-edge Artificial Intelligence (AI) solutions for the airline industry. This collaboration, centered at the Salesforce AI Research hub in Singapore, aims to deliver greater value and innovative benefits to the sector. As part of this initiative, SIA is integrating Salesforce’s Agentforce, Einstein in Service Cloud, and Data Cloud into its customer case management system, enabling the airline to provide more consistent, personalised, and efficient service to its customers. SIA will deploy Agentforce, an AI system that uses autonomous agents to handle specific tasks, streamlining customer service operations. This allows SIA’s customer service representatives to focus on delivering enhanced, personalised attention during customer interactions. Data Cloud, Salesforce’s hyperscale data engine, powers Agentforce by consolidating relevant data, enabling AI agents to provide customer service representatives with tailored advice and solutions, further enhancing the customer experience. Mr. Goh Choon Phong, Chief Executive Officer of Singapore Airlines, highlighted the airline’s commitment to innovation: “As the world’s leading digital airline, Singapore Airlines is dedicated to investing in and leveraging advanced technologies to enhance customer experiences, improve operational efficiencies, drive revenue generation, and boost employee productivity. Over the past 18 months, the SIA Group has been an early adopter of Generative AI solutions, developing over 250 use cases and implementing around 50 initiatives across our end-to-end operations. Salesforce is a pioneer in Agentic AI, and integrating Agentforce, Einstein in Service Cloud, and Data Cloud into our customer case management system marks the first step in our collaboration. Together, we will co-create AI solutions that drive meaningful and impactful change, setting new standards for service excellence in the airline industry.” In addition to Agentforce, SIA will utilise Einstein Generative AI capabilities within Service Cloud to summarise customers’ previous interactions with the airline. This feature provides customer service representatives with actionable insights, enabling them to better understand and anticipate customer needs, tailor solutions, and reduce average response times. The result is a more efficient, proactive, and personalised customer service experience. Marc Benioff, Chair and Chief Executive Officer of Salesforce, emphasised the transformative potential of this partnership: “The rise of digital labour, powered by autonomous AI agents, is not just reimagining the customer experience – it’s transforming business. We’re thrilled to partner with Singapore Airlines, a trailblazer in this AI revolution, to elevate their already outstanding customer service to unprecedented heights, augment their employees, and collaborate on groundbreaking AI solutions for the airline industry. With our deeply unified digital labour platform, we’re bringing humans together with trusted, autonomous AI agents, unlocking new levels of productivity, innovation, and growth.” This collaboration between Singapore Airlines and Salesforce represents a significant step forward in the airline industry’s adoption of AI-driven solutions. By combining SIA’s industry expertise with Salesforce’s innovative AI technologies, the partnership aims to redefine customer service standards, enhance operational efficiency, and set a new benchmark for excellence in the aviation sector. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

March 21, 2025in Agentforce Platform, Agentic AI, Artificial Intelligence, Data, Generative AI, Salesforce, Salesforce Hospitality Travel and Transportation0 Comments

17Mar

AI vs Human Intelligence

Artificial Intelligence vs. Human Intelligence: Key Differences Explained Artificial intelligence (AI) often mimics human-like capabilities, but there are fundamental differences between natural human intelligence and artificial systems. While AI has made remarkable strides in replicating certain aspects of human cognition, it operates in ways that are distinct from how humans think, learn, and solve problems. Below, we explore three key areas where AI and human intelligence diverge. Defining Intelligence Human IntelligenceHuman intelligence is often described using terms like smartness, understanding, brainpower, reasoning, sharpness, and wisdom. These concepts reflect the complexity of human cognition, which has been debated for thousands of years. At its core, human intelligence is a biopsychological capacity to acquire, apply, and adapt knowledge and skills. It encompasses not only logical reasoning but also emotional understanding, creativity, and social interaction. Artificial IntelligenceAI refers to machines designed to perform tasks traditionally associated with human intelligence, such as learning, problem-solving, and decision-making. Over the past few decades, AI has advanced rapidly, particularly in areas like machine learning and generative AI. However, AI lacks the depth and breadth of human intelligence, operating instead through algorithms and data processing. Human Intelligence: What Humans Do Better Humans excel in areas that require empathy, judgment, intuition, and creativity. These qualities are deeply rooted in our evolution as social beings. For example: These capabilities make human intelligence uniquely suited for tasks that involve emotional connection, ethical decision-making, and creative thinking. Artificial Intelligence: What AI Does Better AI outperforms humans in several areas, particularly those involving data processing, pattern recognition, and speed: However, AI’s strengths are limited to the data it is trained on and the algorithms it uses, lacking the adaptability and contextual understanding of human intelligence. 3 Key Differences Between AI and Human Intelligence AI and Human Intelligence: Working Together The future lies in human-AI collaboration, where the strengths of both are leveraged to address complex challenges. For example: While some may find the idea of integrating AI into decision-making unsettling, the scale of global challenges—from climate change to healthcare—demands the combined power of human and artificial intelligence. By working together, humans and AI can amplify each other’s strengths while mitigating weaknesses. Conclusion AI and human intelligence are fundamentally different, each excelling in areas where the other falls short. Human intelligence is unparalleled in creativity, empathy, and ethical reasoning, while AI dominates in data processing, pattern recognition, and speed. The key to unlocking the full potential of AI lies in human-AI collaboration, where the unique strengths of both are harnessed to solve the world’s most pressing problems. As we move forward, this partnership will likely become not just beneficial but essential. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, 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

March 17, 2025in Artificial Intelligence, Data, Generative AI0 Comments

AI in Airport Operations

VINCI Airports Leverages AI to Enhance Passenger Experience and Optimize Operations Across airside, landside, and terminal operations, VINCI Airports— a Corporate Partner of the FTE Digital, Innovation & Startup Hub— is harnessing Artificial Intelligence (AI) to transform passenger experiences, streamline airport flow, and reduce CO2 emissions. As an Innovation Center of Excellence for VINCI Airports, Lyon Airport is at the forefront of testing and implementing Generative AI (GenAI) to enhance customer interactions and operational efficiency. “AI is more than a buzzword—it’s a powerful tool for driving efficiency, improving interactions, personalizing services, and saving time,” says César Clary, Head of Digital & Innovation at Aéroports de Lyon/VINCI Airports. However, he emphasizes that AI should serve as a means to an end, not just a goal in itself. “We are making significant strides in leveraging AI to personalize services, improve efficiency, and reshape airport management.” AI-Powered Enhancements at Lyon Airport With over 10 million passengers passing through Lyon Airport each year, maintaining a cutting-edge customer experience is a priority. VINCI Airports has integrated AI-driven solutions into key customer touchpoints through in-house development and strategic partnerships: “The goal is to create more personalized and seamless interactions for travelers while supporting our staff,” Clary explains. By enabling natural language communication, real-time insights, and personalized recommendations, GenAI and Agentic AI are revolutionizing customer interactions and setting the stage for future service innovations. AI in Airport Operations Beyond customer service, AI is enhancing operational efficiency through: Overcoming Challenges in AI Implementation Despite AI’s vast potential, its adoption comes with challenges. Effective AI integration requires: Clary offers a strategic approach for AI adoption: “Spend time on algorithms and technology, but above all, invest in people, processes, and change management. Start small, demonstrate value, and educate your teams to ensure successful adoption.” With Lyon Airport leading the way, VINCI Airports is proving that GenAI is not just a futuristic concept but a transformative force in modern mobility. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

March 17, 2025in Agentic AI, Artificial Intelligence, Data, Generative AI, Salesforce Hospitality Travel and Transportation, Technology0 Comments

14Mar

AI-Driven Salesforce Explained

AI-driven Salesforce refers to the integration of Artificial Intelligence (AI) into Salesforce’s Customer Relationship Management (CRM) platform to enhance its capabilities, automate processes, and deliver smarter, data-driven insights. Salesforce has embedded AI into its ecosystem through Salesforce Einstein, its proprietary AI technology. Here’s a breakdown of how AI drives Salesforce: 1. What is AI-Driven Salesforce? AI-driven Salesforce leverages machine learning, natural language processing (NLP), predictive analytics, and automation to help businesses make smarter decisions, improve customer experiences, and streamline operations. It transforms raw data into actionable insights and automates repetitive tasks, enabling teams to focus on strategic activities. 2. Key Features of AI-Driven Salesforce a) Salesforce Einstein Einstein is the AI layer built into Salesforce that powers intelligent features across the platform. Key capabilities include: b) AI-Powered Insights c) Personalization d) Automation e) Predictive Intelligence 3. Benefits of AI-Driven Salesforce a) Enhanced Customer Experience b) Increased Efficiency c) Data-Driven Decision Making d) Improved Sales Performance e) Scalability 4. Use Cases of AI-Driven Salesforce a) Sales b) Marketing c) Customer Service d) Commerce 5. The Future of AI in Salesforce In summary, AI-driven Salesforce empowers businesses to work smarter, not harder, by leveraging data and automation to deliver better customer experiences and drive growth. It’s a game-changer for sales, marketing, service, and beyond! Content updated January 2025. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

March 14, 2025in Artificial Intelligence, Data, Salesforce, Salesforce Einstein, Technology0 Comments

05Mar

Salesforce Personalization for Fundraising

The Power of Personalization in Fundraising with Salesforce Successful fundraisers have long recognized that personalization in communicaation drives stronger donor relationships and inspires far greater generosity. However, achieving meaningful engagement at scale has traditionally been a time-intensive challenge. Salesforce, the world’s leading CRM, is transforming nonprofit fundraising by seamlessly integrating donor data with cutting-edge artificial intelligence. This powerful combination enables organizations to build deeper connections with donors through hyper-personalized interactions. How Salesforce is Revolutionizing Donor Engagement: Scalable Solutions for Every Nonprofit Salesforce is built to support organizations of all sizes, from small grassroots initiatives to large national institutions. As your objectives evolve, Salesforce’s flexible platform scales with you, ensuring you always have the right tools to achieve your fundraising goals. Now is the perfect time to leverage Salesforce’s power to enhance personalized giving. Getting Started with Salesforce Advancing Your Salesforce Strategy By leveraging Salesforce’s powerful tools and automation, nonprofits can enhance personalization, drive engagement, and build lasting donor relationships—all while streamlining operations and maximizing fundraising success. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

March 5, 2025in Artificial Intelligence, Data, Salesforce, Salesforce Personalization0 Comments

The Evolving Role of the Chief Experience Officer

04Mar

Have We Got a Job for You

The Evolving Role of the Chief Experience Officer The chief experience officer (CXO) role varies significantly across different organizations, depending on which department owns customer experience—marketing, customer service, or an independent team. Many companies are still on their first CXO, and the position continues to evolve, particularly as artificial intelligence (AI) becomes integral to customer experience (CX) strategies. According to new research from Deloitte, who surveyed 250 CX leaders across various industries, the CXO role is becoming increasingly technology-driven. AI-powered personalization and automation are reshaping CX, yet CXOs often face significant challenges, including limited influence and budget constraints. Defining the CXO’s Responsibilities The responsibilities of a CXO vary widely based on the organization’s structure. Some CXOs lead initiatives within contact centers, while others focus on product development or digital transformation. Regardless of their direct oversight, CXOs are typically accountable for the end-to-end customer journey, addressing pain points, and driving customer-centric strategies. Reporting structures also differ. Some CXOs report to the chief marketing officer (CMO), while others operate at the same level as the CMO or report directly to the CEO or board. Their role extends beyond operational oversight, requiring them to influence company-wide CX strategies, advocate for customer needs, and oversee the technology platforms that shape customer interactions. One of the critical challenges many CXOs face is acting as the customer’s voice in executive meetings, often playing the role of a contrarian to ensure that customer-centric decisions remain a priority. However, the ultimate goal is to create a company culture where customer advocacy is embedded across leadership, making the CXO’s role less about persuasion and more about strategic execution. Driving Change with Limited Resources CXOs often must drive meaningful change despite limited budgets and internal resistance. In the early stages of the role, proving the business value of CX improvements is imparative. Organizations are more likely to invest in CX when presented with compelling data demonstrating a direct impact on pipeline growth, customer lifetime value, and revenue. By leveraging data-driven insights, CXOs can build a strong business case for customer experience initiatives, making it easier to influence executive decisions and organizational behavior. Technology’s Role in Human-Centered CX With nearly every customer touchpoint mediated through technology, the CXO’s role has increasingly aligned with human-centered design principles. As organizations adopt AI and automation, CXOs ensure that these technologies serve a human purpose—reducing friction in customer interactions, streamlining employee workflows, and enhancing overall engagement. Rather than implementing technology for its own sake, CXOs focus on solving real customer problems, such as minimizing complexity in digital interactions, improving accessibility, and enhancing service responsiveness. This requires a balance between technological feasibility and human desirability, ensuring that innovations align with customer needs rather than complicate them. Emerging Technologies and Their Impact on CX The research highlights that CXOs must stay informed about emerging technologies, including edge computing, blockchain, and neuromorphic computing. These innovations have the potential to reshape CX by enabling real-time data processing, enhancing personalization, and providing new ways to understand customer behavior. As experience leaders, CXOs are constantly evaluating whether these advancements improve or hinder customer interactions. Many are approached by startups offering AI-driven solutions such as sentiment analysis and voice recognition. Their challenge is to discern which technologies genuinely enhance CX and which may introduce unnecessary complexity. Overcoming Organizational Resistance Many CXOs encounter frustration due to the slow pace of change within their organizations. Despite their best efforts, progress can be hindered by structural challenges, risk aversion, and competing priorities. However, perseverance remains key. As technology becomes increasingly powerful, so does the influence of executives who understand its impact on human experiences. Organizations that recognize the value of CX will continue to seek leaders who can quantify its business impact, develop strong use cases, and drive transformation. The growing emphasis on CX and AI-driven customer engagement suggests that demand for skilled CXOs will only increase. Those who can navigate the complexities of organizational change while championing human-centered innovation will play a pivotal role in shaping the future of customer experience. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

March 4, 2025in Artificial Intelligence, Data, Technology0 Comments

01Mar

The Evolution of Data

Data’s Evolution: Once Oil Now Compute Power For years, data was dubbed the new oil, a valuable resource that could fuel innovation. However, with the rise of artificial intelligence (AI), compute power has taken center stage as the key driver of business success. Sequoia has not hesitated to outline AI’s transformative potential: “The fields that generative AI addresses — knowledge work and creative work — comprise billions of workers. If generative AI can make these workers at least 10% more efficient and/or creative, they become not only faster and more efficient but more capable than before. Therefore, generative AI has the potential to generate trillions of dollars of economic value.” — Sonya Huang, Pat Grady, Generative AI: A Creative New World While markets continue to assess the validity of this claim, the parallels to past technological revolutions—such as the internet and mobile phones—are undeniable. AI’s influence on knowledge work, particularly in data analytics, is poised to be profound, reshaping how organizations extract value from data. The Rise of Data Analytics and AI Integration The data analytics market is expanding at an unprecedented rate, with a projected CAGR of 27.3% through 2030. A decade ago, data science degrees were rare; professionals often transitioned from related analytical fields like physics, mathematics, accounting, and economics. However, the landscape has shifted dramatically. According to the National Center for Education Statistics, bachelor’s degrees in data science surged by 968% between 2020 and 2022, reflecting the growing demand for analytics expertise. Despite its rapid evolution, modern data analytics presents unique challenges. Unlike software engineering—where code is deterministic—data pipelines evolve based on ingestion processes, requiring a different approach to governance and tooling. AI’s introduction into this space offers a chance to accelerate insights, but only if organizations integrate it strategically into their data frameworks. Modernizing Data Practices: From ETL to ELTL Historically, organizations relied on extract-transform-load (ETL) processes for data management. However, inefficiencies led to the rise of extract-load-transform (ELT), empowering analysts to transform data within cloud-based environments. Developer tools like dbt further streamlined this process, enabling analysts to create data models without relying on centralized data architects. Yet, with increased speed came challenges—primarily, the proliferation of inconsistent data sources. This issue underscores the importance of a final load step (ELTL) that incorporates governance measures to ensure accuracy before presenting data to users. Without proper governance, discrepancies between dashboards can erode trust in analytics. Data Governance and the Role of the Semantic Layer To mitigate inconsistencies, organizations have embraced semantic layers—standardized frameworks that define key business metrics. dbt’s semantic layer and emerging solutions like SQLMesh help enforce governance, ensuring AI-powered analytics operate on reliable data. AI’s effectiveness hinges on a well-documented semantic layer, built on metadata that defines relationships between data objects. Many AI systems utilize retrieval-augmented generation (RAG), which retrieves relevant context before generating responses. However, for AI-driven analytics to succeed, organizations must implement safeguards such as primary key checks and join definitions, ensuring structured and accurate outputs. AI-Powered Precision Analytics The integration of AI into data analytics is driving a shift toward precision analytics—delivering exactly the right data transformation tailored to user needs. This approach mirrors the concept of precision medicine in healthcare, where treatments are customized for individuals. With AI, organizations can move beyond dashboards overloaded with filters and instead provide instant, context-aware insights. Data Objects: A New Paradigm Traditionally, analytics relied on dashboards, but AI enables a more flexible approach through “data objects.” A data object represents a distinct SQL query that answers a user’s prompt. These objects can take three primary forms: This shift allows users to generate and compare data objects dynamically, reducing reliance on static reports and fostering a more agile analytical environment. AI Data Analytics: The Next Frontier Organizations now have the opportunity to revolutionize data analytics through AI. Broadly, AI-driven analytics solutions fall into two categories: Text-to-SQL technology has made significant strides, with execution benchmarks like Spider achieving over 91% accuracy. However, new challenges such as Spider 2.0 highlight the complexity of multi-step analytical workflows. AI systems must incorporate rigorous governance mechanisms to ensure accuracy and reliability. Trust, Governance, and the Human Element Trust is paramount in AI-driven analytics. Without proper governance, AI models risk generating misleading insights, undermining confidence in data-driven decision-making. Organizations must establish clear metadata standards, enforce rigorous validation checks, and maintain a human-in-the-loop approach to AI oversight. An AI-powered data agent should function much like a skilled data analyst—asking clarifying questions, referencing well-documented schemas, and leveraging business glossaries to interpret queries accurately. When implemented correctly, AI-driven analytics can transform data accessibility, making insights available in seconds rather than weeks. The Future of AI-Driven Analytics AI is poised to revolutionize data analytics, offering real-time insights with unparalleled accuracy. However, success hinges on a strong foundation of governance, well-defined data models, and reliable metadata. Organizations that embrace AI with a strategic approach will unlock new efficiencies, reduce bottlenecks, and redefine how data powers decision-making. By integrating AI into analytics responsibly, businesses can move beyond dashboards and embrace true precision analytics—where every insight is timely, relevant, and trustworthy. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

March 1, 2025in Artificial Intelligence, Data, Generative AI, Technology0 Comments

28Feb

Generative AI in Marketing

Generative Artificial Intelligence (GenAI) continues to reshape industries, providing product managers (PMs) across domains with opportunities to embrace AI-focused innovation and enhance their technical expertise. Over the past few years, GenAI has gained immense popularity. AI-enabled products have proliferated across industries like a rapidly expanding field of dandelions, fueled by abundant venture capital investment. From a product management perspective, AI offers numerous ways to improve productivity and deepen strategic domain knowledge. However, the fundamentals of product management remain paramount. This discussion underscores why foundational PM practices continue to be indispensable, even in the evolving landscape of GenAI, and how these core skills can elevate PMs navigating this dynamic field. Why PM Fundamentals Matter, AI or Not Three core reasons highlight the enduring importance of PM fundamentals and actionable methods for excelling in the rapidly expanding GenAI space. 1. Product Development is Inherently Complex While novice PMs might assume product development is straightforward, the reality reveals a web of interconnected and dynamic elements. These may include team dependencies, sales and marketing coordination, internal tooling managed by global teams, data telemetry updates, and countless other tasks influencing outcomes. A skilled product manager identifies and orchestrates these moving pieces, ensuring product growth and delivery. This ability is often more impactful than deep technical AI expertise (though having both is advantageous). The complexity of modern product development is further amplified by the rapid pace of technological change. Incorporating AI tools such as GitHub Copilot can accelerate workflows but demands a strong product culture to ensure smooth integration. PMs must focus on fundamentals like understanding user needs, defining clear problems, and delivering value to avoid chasing fleeting AI trends instead of solving customer problems. While AI can automate certain tasks, it is limited by costs, specificity, and nuance. A PM with strong foundational knowledge can effectively manage these limitations and identify areas for automation or improvement, such as: 2. Interpersonal Skills Are Irreplaceable As AI product development grows more complex, interpersonal skills become increasingly critical. PMs work with diverse teams, including developers, designers, data scientists, marketing professionals, and executives. While AI can assist in specific tasks, strong human connections are essential for success. Key interpersonal abilities for PMs include: Stakeholder management remains a cornerstone of effective product management. PMs must build trust and tailor their communication to various audiences—a skill AI cannot replicate. 3. Understanding Vertical Use Cases is Essential Vertical use cases focus on niche, specific tasks within a broader context. In the GenAI ecosystem, this specificity is exemplified by AI agents designed for narrow applications. For instance, Microsoft Copilot includes a summarization agent that excels at analyzing Word documents. The vertical AI market has experienced explosive growth, valued at .1 billion in 2024 and projected to reach .1 billion by 2030. PMs are crucial in identifying and validating these vertical use cases. For example, the team at Planview developed the AI Assistant “Planview Copilot” by hypothesizing specific use cases and iteratively validating them through customer feedback and data analysis. This approach required continuous application of fundamental PM practices, including discovery, prioritization, and feedback internalization. PMs must be adept at discovering vertical use cases and crafting strategies to deliver meaningful solutions. Key steps include: Conclusion Foundational product management practices remain critical, even as AI transforms industries. These core skills ensure that PMs can navigate the challenges of GenAI, enabling organizations to accelerate customer value in work efficiency, time savings, and quality of life. By maintaining strong fundamentals, PMs can lead their teams to thrive in an AI-driven future. AI Agents on Madison Avenue: The New Frontier in Advertising AI agents, hailed as the next big advancement in artificial intelligence, are making their presence felt in the world of advertising. Startups like Adaly and Anthrologic are introducing personalized AI tools designed to boost productivity for advertisers, offering automation for tasks that are often time-consuming and tedious. Retail brands such as Anthropologie are already adopting this technology to streamline their operations. How AI Agents WorkIn simple terms, AI agents operate like advanced AI chatbots. They can handle tasks such as generating reports, optimizing media budgets, or analyzing data. According to Tyler Pietz, CEO and founder of Anthrologic, “They can basically do anything that a human can do on a computer.” Big players like Salesforce, Microsoft, Anthropic, Google, and Perplexity are also championing AI agents. Perplexity’s CEO, Aravind Srinivas, recently suggested that businesses will soon compete for the attention of AI agents rather than human customers. “Brands need to get comfortable doing this,” he remarked to The Economic Times. AI Agents Tailored for Advertisers Both Adaly and Anthrologic have developed AI software specifically trained for advertising tasks. Built on large language models like ChatGPT, these platforms respond to voice and text prompts. Advertisers can train these AI systems on internal data to automate tasks like identifying data discrepancies or analyzing economic impacts on regional ad budgets. Pietz noted that an AI agent can be set up in about a month and take on grunt work like scouring spreadsheets for specific figures. “Marketers still log into 15 different platforms daily,” said Kyle Csik, co-founder of Adaly. “When brands in-house talent, they often hire people to manage systems rather than think strategically. AI agents can take on repetitive tasks, leaving room for higher-level work.” Both Pietz and Csik bring agency experience to their ventures, having crossed paths at MediaMonks. Industry Response: Collaboration, Not Replacement The targets for these tools differ: Adaly focuses on independent agencies and brands, while Anthrologic is honing in on larger brands. Meanwhile, major holding companies like Omnicom and Dentsu are building their own AI agents. Omnicom, on the verge of merging with IPG, has developed internal AI solutions, while Dentsu has partnered with Microsoft to create tools like Dentsu DALL-E and Dentsu-GPT. Havas is also developing its own AI agent, according to Chief Activation Officer Mike Bregman. Bregman believes AI tools won’t immediately threaten agency jobs. “Agencies have a lot of specialization that machines can’t replace today,” he said. “They can streamline processes, but

February 28, 2025in AI Tools, Artificial Intelligence, Data, Generative AI, Google, Laws, Salesforce, Technology0 Comments

28Feb

The Coalition for Sustainable AI

The Coalition for Sustainable AI: Aligning AI Development with Environmental Responsibility The rapid rise of artificial intelligence (AI) presents both groundbreaking opportunities and significant environmental challenges. Recognizing the need for responsible AI development, France, the United Nations Environment Programme (UNEP), and the International Telecommunication Union (ITU) have established the Coalition for Sustainable AI—a global, multi-stakeholder initiative dedicated to ensuring AI supports sustainability rather than exacerbating environmental harm. A Shared Vision for Sustainable AI The Coalition for Sustainable AI, launched at the Paris AI Action Summit 2025, brings together public and private sector leaders to align AI advancements with environmental goals. The initiative seeks to: Why This Coalition Matters As AI infrastructure becomes as fundamental as water, energy, and transport, its environmental implications must be addressed proactively. AI technologies have the potential to redefine entire industries—just as the Industrial Revolution once did—while offering unprecedented capabilities to tackle climate change, optimize resource management, and enhance environmental decision-making. By bringing together a diverse network of stakeholders, the Coalition recognizes that the digital and AI revolution and the environmental crisis are two defining challenges of our time. Mission and Leadership The Coalition operates under two core principles: Founding Leaders: Driving Global Collaboration The Coalition’s role extends beyond advocacy. It serves as a platform to: This initiative will also maintain momentum through major global forums such as AI Summits, COP conferences, and other international policy discussions, ensuring AI remains at the forefront of sustainability efforts. Industry Leaders Join the Movement The Coalition for Sustainable AI has already attracted a diverse group of corporations, research institutions, NGOs, investors, and public sector organizations committed to this mission. Corporate Members Include: Salesforce, Nvidia, IBM, Hugging Face, Capgemini, Thales, Schneider Electric, Philips, TotalEnergies, Baidu, Orange, L’Oréal Groupe, Mistral AI, AMD, Dassault Systèmes, and more. Research Institutions and NGOs: Stockholm Environment Institute, Mila, Vrije Universiteit Amsterdam, Università di Pavia, Climate Change AI, The Shift Project, Royal Academy of Engineering, and others. Investors and Public Sector Representatives: Ardian, Crédit Agricole, Eurazeo, Mirova, BPI France, the Republic of Serbia’s Ministry of Science, and more. Salesforce’s Commitment to AI Sustainability Boris Gamazaychikov, Head of AI Sustainability at Salesforce, emphasized the importance of this initiative, stating: “I’m proud that Salesforce is one of the initial members, and I hope that many more join on this critical journey. Thanks to the French Government, UNEP, and ITU for organizing this important initiative.” Looking Ahead: The Future of Sustainable AI The Coalition for Sustainable AI marks a critical step toward ensuring that AI serves as a force for climate action, biodiversity preservation, and sustainable development. As AI continues to reshape the global economy, initiatives like this will help balance technological progress with environmental responsibility. With momentum building and more organizations joining the effort, the Coalition aims to drive lasting impact—paving the way for a future where AI and sustainability go hand in hand. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

February 28, 2025in Artificial Intelligence0 Comments

27Feb

The Future of AI in Salesforce

The Future of AI in Salesforce: Smarter, Predictive, and Deeply Integrated Artificial Intelligence (AI) is revolutionizing the Salesforce ecosystem, reshaping customer interactions, automating workflows, and driving revenue growth. As we move into 2025 and beyond, AI within Salesforce will become even more intelligent, predictive, and seamlessly embedded across the platform. Let’s explore the key advancements defining the next era of AI in Salesforce. 1. Next-Gen Einstein AI: A Smarter CRM Assistant Salesforce Einstein continues to evolve, equipping businesses with powerful AI-driven capabilities: 2. AI-Powered Revenue Intelligence & Forecasting AI is transforming revenue intelligence, helping sales teams make data-driven decisions: 3. AI-Driven Sales & Service Automation AI-powered automation will streamline workflows and improve efficiency: 4. Hyper-Personalization with AI & Data Cloud Salesforce Data Cloud and AI will power personalized customer experiences at scale: 5. AI-Optimized Lead Generation & Marketing Automation AI will continue to enhance lead generation and marketing strategies: 6. AI & Low-Code/No-Code Innovation Salesforce is democratizing AI with accessible low-code and no-code tools: 7. Ethical AI & Governance: Building Trust in AI Salesforce remains committed to ethical, transparent, and bias-free AI: Conclusion As AI becomes deeply embedded in every Salesforce cloud, businesses will experience faster automation, smarter decision-making, and hyper-personalized customer engagement. From AI-powered sales forecasting to generative AI-driven content, the future of Salesforce AI is set to redefine CRM strategies in 2025 and beyond. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

February 27, 2025in AI Tools, Artificial Intelligence, Data, Generative AI, Marketing Automation, Salesforce, Salesforce Data Cloud, Salesforce Einstein0 Comments

24Feb

Salesforce and Google Announcement

Salesforce (NYSE:CRM) has entered into a deal with Google (NASDAQ:GOOGL) to offer its customer relations management software, Agentforce artificial intelligence assistants, and Data Cloud offerings through Google Cloud, the companies announced today. Google and Salesforce already have many of the same clients, and this new deal will allow for more product integration between Google Workspace and Salesforce’s customer relationship management and AI offerings. Salesforce already uses Amazon (AMZN) Web Services for much of its cloud computing. “Our mutual customers have asked us to be able to work more seamlessly across Salesforce and Google Cloud, and this expanded partnership will help them accelerate their AI transformations with agentic AI, state-of-the-art AI models, data analytics, and more,” said Thomas Kurian, CEO of Google Cloud. The deal is expected to total $2.5B over the next seven years, according to a report by Bloomberg. Salesforce and Google today announced a major expansion of their strategic partnership, delivering choice in the models and capabilities businesses use to build and deploy AI-powered agents. In today’s constantly evolving AI landscape, innovations like autonomous agents are emerging so quickly that businesses struggle to keep pace. This expanded partnership provides crucial flexibility, empowering customers to develop tailored AI solutions that meet their specific needs, rather than being locked into a single model provider. Google Cloud is at the forefront of enterprise AI innovation with millions of developers building with Google’s cutting-edge Gemini models and on Google Cloud’s AI-optimized infrastructure. This expanded partnership will empower Salesforce customers to build Agentforce agents using Gemini and to deploy Salesforce on Google Cloud. This is an expansion of the existing partnership that allows customers to use data from Data Cloud and Google BigQuery bi-directionally via zero-copy technology—further equipping customers with the data, AI, trust, and actions they need to bring autonomous agents into their businesses. Additionally, this integration empowers Agentforce agents with the ability to reference up-to-the-minute data, news, current events, and credible citations, substantially enhancing their contextual awareness and ability to deliver accurate, evidence-backed responses. For example, in supply chain management and logistics, an agent built with Agentforce could track shipments and monitor inventory levels in Salesforce Commerce Cloud and proactively identify potential disruptions using real-time data from Google Search, including weather conditions, port congestion, and geopolitical events. Availability is expected in the coming months. AI: Unlocking the Power of Choice and Flexibility with Gemini and Agentforce Businesses need the freedom to choose the best models for their needs rather than be locked into one vendor. In 2025, Google’s Gemini models will also be available for prompt building and reasoning directly within Agentforce. With Gemini and Agentforce, businesses will benefit from: For example, an insurance customer can submit a claim with photos of the damage and an audio voicemail from a witness. Agentforce, using Gemini, can then help the insurance provider deliver better customer experiences by processing all these inputs, assessing the claim’s validity, and even using text-to-speech to contact the customer with a resolution, streamlining the traditionally lengthy claims process. Availability is expected this year. Trust: Salesforce Platform deployed on Google Cloud Customers will be able to use Salesforce’s unified platform (Agentforce, Data Cloud, Customer 360) on Google Cloud’s highly secure, AI-optimized infrastructure, benefiting from features like dynamic grounding, zero data retention, and toxicity detection provided by the Einstein Trust Layer. Once Salesforce products are available on Google Cloud, customers will also have the ability to procure Salesforce offerings through the Google Cloud Marketplace, opening up new possibilities for global businesses to optimize their investments across Salesforce and Google Cloud and benefiting thousands of existing joint customers. Action: Enhanced Employee Productivity and Customer Service with AI-Powered Integrations Millions use Salesforce and Google Cloud daily. This partnership prioritizes choice and flexibility, enabling seamless cross-platform work. New and deeper connections between platforms like Salesforce Service Cloud and Google Cloud’s Customer Engagement Suite, as well as Slack and Google Workspace, will empower AI agents and service representatives with unified data access, streamlined workflows, and advanced AI capabilities, regardless of platform. Salesforce and Google Cloud are deeply integrating their customer service platforms—Salesforce Service Cloud and Google Cloud’s Customer Engagement Suite—to create a seamless and intelligent support experience. Expected later this year, this unified approach empowers AI agents in Service Cloud with: Salesforce and Google Cloud are also exploring deeper integrations between Slack and Google Workspace, boosting productivity and creating a more cohesive digital workspace for teams and organizations. The companies are currently exploring use cases such as: Expanding Partnership Capabilities and Integrations This partnership goes beyond core product integrations to deliver a more connected and intelligent data foundation for businesses. Expected availability throughout 2025: This landmark partnership between Salesforce and Google represents a strategic paradigm shift in enterprise AI deployment, emphasizing infrastructure innovation, AI capability enhancement, and enterprise value. The integration of Google Search grounding provides a unique competitive advantage, offering real-time, factual responses backed by the world’s most comprehensive search engine. The companies are committed to ongoing innovation and deeper collaboration to empower businesses with even more powerful solutions. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

February 24, 2025in Agentic AI, Artificial Intelligence, Data, Enterprise, Google, Salesforce, Salesforce Commerce Cloud, Salesforce Service Cloud, Technology0 Comments

24Feb

Neuro-symbolic AI

Neuro-Symbolic AI: Bridging Neural Networks and Symbolic Processing for Smarter AI Systems Neuro-symbolic AI integrates neural networks with rules-based symbolic processing to enhance artificial intelligence systems’ accuracy, explainability, and precision. Neural networks leverage statistical deep learning to identify patterns in large datasets, while symbolic AI applies logic and rules-based reasoning common in mathematics, programming languages, and expert systems. The Balance Between Neural and Symbolic AIThe fusion of neural and symbolic methods has revived debates in the AI community regarding their relative strengths. Neural AI excels in deep learning, including generative AI, by distilling patterns from data through distributed statistical processing across interconnected neurons. However, this approach often requires significant computational resources and may struggle with explainability. Conversely, symbolic AI, which relies on predefined rules and logic, has historically powered applications like fraud detection, expert systems, and argument mining. While symbolic systems are faster and more interpretable, their reliance on manual rule creation has been a limitation. Innovations in training generative AI models now allow more efficient automation of these processes, though challenges like hallucinations and poor mathematical reasoning persist. Complementary Thinking ModelsPsychologist Daniel Kahneman’s analogy of System 1 and System 2 thinking aptly describes the interplay between neural and symbolic AI. Neural AI, akin to System 1, is intuitive and fast—ideal for tasks like image recognition. Symbolic AI mirrors System 2, engaging in slower, deliberate reasoning, such as understanding the context and relationships in a scene. Core Concepts of Neural NetworksArtificial neural networks (ANNs) mimic the statistical connections between biological neurons. By modeling patterns in data, ANNs enable learning and feature extraction at different abstraction levels, such as edges, shapes, and objects in images. Key ANN architectures include: Despite their strengths, neural networks are prone to hallucinations, particularly when overconfident in their predictions, making human oversight crucial. The Role of Symbolic ReasoningSymbolic reasoning underpins modern programming languages, where logical constructs (e.g., “if-then” statements) drive decision-making. Symbolic AI excels in structured applications like solving math problems, representing knowledge, and decision-making. Algorithms like expert systems, Bayesian networks, and fuzzy logic offer precision and efficiency in well-defined workflows but struggle with ambiguity and edge cases. Although symbolic systems like IBM Watson demonstrated success in trivia and reasoning, scaling them to broader, dynamic applications has proven challenging due to their dependency on manual configuration. Neuro-Symbolic IntegrationThe integration of neural and symbolic AI spans a spectrum of techniques, from loosely coupled processes to tightly integrated systems. Examples of integration include: History of Neuro-Symbolic AIBoth neural and symbolic AI trace their roots to the 1950s, with symbolic methods dominating early AI due to their logical approach. Neural networks fell out of favor until the 1980s when innovations like backpropagation revived interest. The 2010s saw a breakthrough with GPUs enabling scalable neural network training, ushering in today’s deep learning era. Applications and Future DirectionsApplications of neuro-symbolic AI include: The next wave of innovation aims to merge these approaches more deeply. For instance, combining granular structural information from neural networks with symbolic abstraction can improve explainability and efficiency in AI systems like intelligent document processing or IoT data interpretation. Neuro-symbolic AI offers the potential to create smarter, more explainable systems by blending the pattern-recognition capabilities of neural networks with the precision of symbolic reasoning. As research advances, this synergy may unlock new horizons in AI capabilities. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, 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

February 24, 2025in Artificial Intelligence, Data, Generative AI, Google0 Comments

Generative AI Prompts with Retrieval Augmented Generation

22Feb

AI Prompts for Small Businesses

How AI Prompts Can Help Small Businesses Win More Customers Getting new customers can be a challenge for small businesses. You may be eager to explore artificial intelligence (AI) but unsure where to begin. The answer? AI prompts—a simple yet powerful way to automate and optimize sales efforts. This guide explores five AI prompts designed to enhance your sales process, from personalized outreach to lead generation. Let’s dive in! What Is an AI Prompt? An AI prompt is a specific instruction or question given to an AI tool to generate responses or perform tasks. The more precise the prompt, the better the results. For small businesses, AI prompts can: Why AI Matters for Small Business Sales AI is a game-changer for small business sales. It provides insights into customer behavior, streamlines processes, and enhances decision-making. Unlike enterprise AI applications, SMB-focused AI helps automate repetitive tasks, allowing sales teams to focus on relationship-building and closing deals. A strong starting point? AI-powered CRM tools. Integrating AI with your CRM unlocks predictive analytics, automation, and smarter customer engagement. In fact, small businesses using Salesforce AI have reported: AI Prompts vs. Traditional Sales Methods AI-Powered Prompts Traditional Sales Methods Automated lead generation Manual lead hunting Personalized sales emails Generic mass emails Instant follow-ups Delayed responses AI-generated sales scripts Improvised pitches Smart objection handling Reactive responses 5 AI Prompts to Supercharge Your Sales 1. Lead Generation Prompt Objective: Identify potential leads quickly. AI Prompt: “Generate a list of 10 potential leads based on [industry, location, company size].” How It Helps: AI scans data to find ideal customers, saving time and improving outreach accuracy. Example Output: 2. Sales Email Drafting Prompt Objective: Craft compelling emails that boost click rates. AI Prompt: “Write a persuasive sales email to [target] highlighting our [product/service] and inviting them to a demo.” How It Helps: AI generates tailored emails that resonate with prospects, improving open and response rates. Example Output: Subject: Transform Your Operations with Our CRMHi [First Name],I noticed your business is growing rapidly in [industry]. Our CRM can streamline operations and boost efficiency. Let’s schedule a quick demo this week—let me know your availability![Your Name] 3. Customer Follow-Up Prompt Objective: Keep potential customers engaged. AI Prompt: “Write a follow-up email to [customer] who expressed interest in our [product/service], including a gentle reminder and any new updates.” How It Helps: AI ensures timely, professional follow-ups, maintaining engagement without being pushy. Example Output: Subject: Following Up on Our ConversationHi [First Name],I wanted to check in on our discussion about [product/service]. We recently introduced [new feature], which could be a great fit for you. Let me know if you’d like to reconnect.Thanks,[Your Name] 4. Sales Pitch Script Prompt Objective: Develop a persuasive pitch. AI Prompt: “Create a 2-minute sales pitch for our [product/service] emphasizing key benefits and unique selling points.” How It Helps: A well-structured pitch increases confidence and improves conversion rates. Example Output: “Hello! My name is [Your Name] from [Company Name]. We specialize in [product/service]. What sets us apart is [unique benefit]. Our solution has helped companies like yours achieve [specific results]. Interested in learning more?” 5. Objection Handling Prompt Objective: Overcome sales objections effectively. AI Prompt: “List two common objections about our [product/service] and provide persuasive responses.” How It Helps: Prepares sales teams with effective responses to common objections, increasing deal closures. Example Output: Objection: “It’s too expensive.”Response: “Our solution pays for itself within months through increased efficiency.” Objection: “We’re happy with our current provider.”Response: “That’s great! Many of our clients felt the same until they saw how much more they could achieve with our features.” Unlock Growth with AI-Powered Sales Using AI prompts for sales isn’t just an experiment—it’s a proven way to boost efficiency, personalization, and success. Businesses that embrace AI-driven strategies will outpace competitors and scale faster. Ready to transform your sales game? Start using AI prompts today! Contact Tectonic. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

February 22, 2025in Artificial Intelligence, Data, Enterprise, Salesforce0 Comments

15Feb

Salesforce Life Sciences Cloud and Honeywell

Honeywell’s TrackWise® Quality solution suite, in combination with Salesforce Life Sciences Cloud, will provide a comprehensive software platform for pharmaceutical and medical technology companies. Honeywell has expanded its strategic partnership with Salesforce, Inc. to introduce an integrated software platform designed for the life sciences industry. This platform, which includes Honeywell’s TrackWise Quality, Salesforce Life Sciences Cloud, Agentforce, and other solutions, aims to help pharmaceutical and medical technology companies accelerate the delivery of critical medications and healthcare devices with enhanced safety and cost efficiency. By digitizing and automating quality and compliance processes, Honeywell’s TrackWise Quality enables companies to manufacture products more efficiently, mitigate risks, reduce production costs, and bring safer products to market faster. Salesforce Life Sciences Cloud consolidates diverse data sources—such as clinical test results and medical records—to optimize interactions between pharmaceutical and medtech organizations, healthcare professionals, partners, and patients. This collaboration underscores the shared commitment of Honeywell and Salesforce to advancing automation and efficiency in the life sciences sector. “Our ongoing commitment to providing innovative software to manufacturers of critical life-supporting treatments centers around fostering trust between providers and patients,” said Frank Defesche, SVP & GM, Life Sciences at Salesforce. “The long-standing collaboration between Salesforce and Honeywell strengthens the life sciences industry by enhancing quality interactions and products, ultimately leading to improved patient outcomes.” “Partnering with Salesforce allows us to leverage our combined expertise in life sciences to drive operational efficiency, improve quality, and enhance patient-centric solutions through technology,” said Sunil Pandita, Vice President & General Manager of Honeywell Life Sciences. “From assisting patients in finding life-changing clinical trials to detecting anomalies in pharmaceutical manufacturing, Honeywell and Salesforce technologies are enabling better patient care.” Honeywell’s TrackWise Quality fosters a proactive approach to quality management by integrating industry best practices, advanced tracking and analytics, and artificial intelligence (AI) capabilities. This empowers life sciences companies to harness data more effectively, optimize performance, and enhance decision-making processes. Salesforce Life Sciences Cloud serves as a comprehensive, AI-powered engagement platform for pharmaceutical and medtech companies. Built on Sales Cloud and Service Cloud, it streamlines processes such as automating pharmacy benefit verification and expediting the screening of clinical trial candidates. These enhancements improve patient access, medication adherence, and accelerate diverse patient recruitment while reducing trial attrition. Additional Salesforce solutions within the platform include Agentforce, Data Cloud, and Analytics. As part of its continued commitment to life sciences customers, Honeywell has joined the Salesforce Agentforce Partner Network, a global ecosystem of partners developing solutions for regulatory-compliant training materials for life sciences professionals. To learn more about Honeywell’s industry-leading technologies for life sciences, visit: www.honeywell.com/us/en/industries/life-sciences. Salesforce, Salesforce Life Sciences Cloud, Agentforce, Data Cloud, and other names are trademarks of Salesforce, Inc. About Honeywell Honeywell is a diversified operating company serving a wide range of industries worldwide. Its business aligns with three key megatrends—automation, the future of aviation, and energy transition—supported by the Honeywell Accelerator operating system and Honeywell Forge IoT platform. As a trusted partner, Honeywell helps organizations address complex global challenges, delivering innovative solutions through its Aerospace Technologies, Industrial Automation, Building Automation, and Energy and Sustainability Solutions segments. For more news and updates on Honeywell, visit www.honeywell.com/newsroom. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

February 15, 2025in Artificial Intelligence, Data, Life Sciences Cloud, Salesforce, Technology0 Comments

10Feb

AI and Related Tools Boost Holiday Sales

AI Drives Holiday Sales in 2024: A Record-Breaking Shopping Season with Rising Returns Artificial intelligence (AI) played a transformative role in shaping the 2024 holiday shopping season, with Salesforce reporting that AI-powered tools influenced $229 billion, or 19%, of global online sales. Based on data from 1.5 billion global shoppers and 1.6 trillion page views, AI tools such as product recommendations, targeted promotions, and customer service significantly boosted sales, marking a 6% year-over-year increase in engagement. Generative AI features, including conversational agents, saw a 25% surge in usage during the holiday period compared to earlier months, further highlighting their role in shaping consumer behavior. Mobile commerce amplified AI’s influence, with nearly 70% of global online sales being placed via smartphones. On Christmas Day alone, mobile orders accounted for 79% of transactions, showcasing the shift toward mobile-first shopping. “Retailers who have embraced AI and conversational agents are already reaping the benefits, but these tools will become even more critical in the new year as retailers aim to minimize revenue losses from returns and reengage with shoppers,” said Caila Schwartz, Salesforce’s Director of Consumer Insights. Record-Breaking Sales and Rising Returns Online sales hit .2 trillion globally and 2 billion in the U.S. during the holiday season, but returns surged to $122 billion globally—a 28% increase compared to 2023. Salesforce attributed this spike to evolving shopping habits like bracketing (buying multiple sizes to ensure fit) and try-on hauls (bulk purchasing for social media content), which have become increasingly common. The surge in returns presents a challenge to retailers, potentially eroding profit margins. To address this, many are turning to AI-powered solutions for streamlining returns processes. According to Salesforce, 75% of U.S. shoppers expressed interest in using AI agents for returns, with one-third showing strong enthusiasm for such tools. The Role of AI in Enhancing the Holiday Shopping Experience AI-powered chatbots saw a 42% year-over-year increase in usage during the holiday season, supporting customers with purchases, returns, and product inquiries. These conversational agents, combined with AI-driven loyalty programs and targeted promotions, were instrumental in engaging customers and increasing conversion rates. AI’s influence extended to social commerce, with platforms like TikTok Shop and Instagram driving 20% of global holiday sales. Personalized recommendations and advertisements, powered by AI algorithms, significantly boosted social media referral traffic, which grew by 8% year-over-year. Mobile Commerce and AI Synergy Mobile devices were the dominant force in holiday shopping, generating 2 billion in global online sales and 5 billion in the U.S. Orders placed via smartphones peaked on Christmas Day, with mobile accounting for 79% of all transactions. This mobile-first trend highlights the growing importance of integrating AI into mobile commerce to enhance the shopping experience. AI Integration Expands Across Retail Operations In the UK, retailers are increasingly leveraging AI to optimize operations and improve personalization. A study by IMRG and Scurri revealed that 57% of UK online retailers used generative AI for content creation in 2024, while 31% implemented AI-informed product search tools. By 2025, 75% of UK retailers plan to adopt AI for marketing efforts, and 42% aim to use AI-powered product information management systems to streamline processes. Tesco, for example, uses AI to analyze Clubcard data, enabling tailored product recommendations, healthier purchasing choices, and waste reduction. Meanwhile, Must Have Ideas, a homeware retailer, has launched an AI-driven TV shopping channel powered by proprietary software, Spark, which automates programming schedules based on real-time stock levels and market trends. Looking Ahead The 2024 holiday season underscored the transformative potential of AI in retail. While AI-powered tools drove record sales and engagement, the rise in returns presents a challenge that retailers must address to protect their bottom line. As AI continues to evolve, its role in shaping consumer behavior, streamlining operations, and enhancing customer experiences will become even more integral in the retail landscape. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

February 10, 2025in AI Tools, Artificial Intelligence, Data, Generative AI, Salesforce0 Comments

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