Technology Archives - gettectonic.com

04Mar

Salesforce Marketing Cloud Growth Edition for Personalization

Why Personalized Marketing Matters Personalized marketing is a game-changer for inceased customer engagement and business growth. According to a recent study, it can boost ROI by up to 20%. Yet, many marketers struggle to execute effective personalization due to fragmented data and lack of alignment across teams. To overcome these challenges, marketers must build a solid data foundation, leverage AI-driven segmentation, and utilize dynamic content strategies. This Tectonic insight outlines the essential steps to achieving personalized marketing at scale. Step 1: Build a Unified Data Foundation Marketers today have access to more data than ever before—from CRM and sales data to digital advertising, ERP, transactional, and accounting data. The challenge is unifying these sources to create a single operational customer profile. Without this, analyzing customer behavior and delivering personalized experiences is nearly impossible. How to Get Your Data in Order: Centralize customer data across teams using Marketing Cloud Growth and Advanced Editions, built on the Salesforce platform. Leverage AI-powered insights to optimize real-time personalization. Establish a “single source of truth” to align marketing, sales, and service teams. A strong data foundation not only fuels better AI-driven personalization but also enhances analytics and campaign performance. Step 2: Use AI to Segment with Precision Segmentation is the backbone of personalized marketing, allowing marketers to tailor campaigns based on customer behavior, demographics, and engagement levels. Traditionally, segmentation was a manual, time-consuming process. However, AI has revolutionized this process by enabling natural language-based segment creation. How AI Improves Segmentation: Eliminates the need for SQL expertise, making segmentation accessible to all marketers. Generates precise audience segments based on engagement and fit scores. Leverages harmonized operational profiles to build complex, data-driven segments. With AI-driven segmentation, marketers can create hyper-targeted campaigns that resonate with their audience, increasing engagement and conversion rates. Step 3: Implement Engagement and Fit Scoring One of the most effective ways to enhance personalization is by scoring leads and contacts based on their engagement and fit with your ideal customer profile. This approach helps prioritize high-value prospects and ensures marketing efforts are focused on the right audience. Understanding Engagement Scoring: Engagement Scoring evaluates how actively leads and contacts interact with your marketing campaigns. This includes actions like email clicks, form submissions, and website visits. Key Metrics Used in Engagement Scoring: Email Engagement (e.g., Click, Unsubscribe) Website Engagement (e.g., Form Submission, Button Click) Message Engagement (e.g., Subscribe, Unsubscribe) Understanding Fit Scoring: Fit Scoring assesses how well a lead or contact aligns with your ideal customer profile. This includes demographic, firmographic, and behavioral data. Key Fit Scoring Criteria: Demographics: Age, gender, income level Firmographics: Industry, company size, revenue Behavioral Patterns: Purchase history, product usage Geographics: Location-based targeting Technographics: Technology stack compatibility Step 4: Use Dynamic Content Blocks for Hyper-Personalization Once your data is structured and segments are refined, the next step is delivering personalized content. Marketing Cloud Growth and Advanced Editions offer powerful tools like Cross-Object Merge Fields and Rule-Based Dynamic Content. How to Leverage These Tools: Cross-Object Merge Fields: Pull data from operational customer profiles to create more personalized messages (e.g., referencing a webinar attended or service case status). R ule-Based Dynamic Content: Display targeted text and images based on complex customer attributes (e.g., showing a specific image to customers in New York who attended a liveevent). These tools allow marketers to go beyond basic personalization, creating highly relevant and engaging experiences for their audience. Step 5: Combine Engagement and Fit Scoring for Optimal Results By integrating Engagement and Fit Scoring, marketers can create an overall marketing score that prioritizes the most valuable prospects. This score considers both interaction levels and alignment with the ideal customer profile. How to Implement Overall Scoring: Default weighting assigns 50% to Engagement Score and 50% to Fit Score. Scores range from 0 to 100, ensuring a balanced approach to prioritization. Customization allows businesses to adjust weighting based on their specific needs. This holistic scoring model ensures marketing efforts are directed toward leads that are both highly engaged and a great fit for your business. The Future of Personalized Marketing Personalization is no longer optional—it’s essential. Businesses that embrace advanced personalization techniques see a 10-30% increase in sales and customer retention rates. By implementing the strategies outlined in this guide, marketers can: Build a strong data foundation for AI-driven personalization. Use engagement and fit scoring to prioritize high-value leads. Deliver hyper-personalized content with dynamic marketing tools. With the right foundation, tools, and AI-driven insights, marketers can unlock the full potential of personalized marketing, driving stronger customer relationships and higher ROI. Now is the time to take action and position your business for sustained success in an increasingly competitive marketplace. incorporate this. Meet the all-new Salesforce Marketing Cloud Growth edition! Recently unveiled, this platform is designed specifically to empower small businesses by seamlessly integrating CRM, AI, and data within Salesforce’s trusted customer platform – Einstein. It promises to redefine marketing automation across sales, service, and commerce. Let’s explore how Marketing Cloud Growth is set to revolutionize the way small businesses approach marketing, enabling them to drive growth and efficiency like never before. Salesforce Marketing Cloud Growth Edition On February 20, 2024, Salesforce launched the Marketing Cloud Growth edition, aimed at empowering small businesses specifically with B2B marketing needs. With advanced marketing automation tools, powered by Einstein’s Generative AI and Data Cloud, this new edition simplifies marketing tasks, allowing businesses to concentrate on strategic growth. Salesforce Marketing Cloud Growth Edition integrates seamlessly with the core Salesforce platform, known as the Einstein 1 platform to provide users with easy access to all the robust features of Marketing Cloud within the broader Salesforce ecosystem. With the Marketing Cloud Growth edition, teams can efficiently: Unify various data sources via Data Cloud Create and deliver compelling content with Digital Experiences Automate complex customer journeys using Flow Builder Improve decision-making using Einstein’s intelligent analytics and predictions Marketing Cloud Growth edition is user-friendly and built directly on the Salesforce platform, eliminating the need for external connectors. However, it does not include some advanced features found in Marketing

March 4, 2025in AI Tools, Data, Einstein 1 Platform, Enterprise, Generative AI, Marketing Automation, Salesforce, Salesforce Flow, Salesforce Marketing Cloud, Technology0 Comments

03Mar

It is Time to Implement Data Cloud

With Salesforce Data Cloud you can: With incomplete data your 360-degree customer view is limited and often leads to multiple sales reps working on the same lead. Slow access to the right leads at the right time leads to missed opportunties and delayed closings. If your team cannot trust the data due to siloes and inaccuracies, they avoid using it. It is Time to Implement Data Cloud. Unified Connect and harmonize data from all your Salesforce applications and external data systems. Then activate your data with insights and automation across every customer touchpoint. Powerful With Data Cloud and Agentforce, you can create the most intelligent agents possible, giving them access to the exact data they need to deliver any employee or customer experience. Secure Securely connect your data to any large language model (LLM) without sacrificing data governance and security thanks to the Einstein 1 trust layer. Open Data Cloud is fully open and extensible – bring your own data lake or model to reduce complexity and leverage what’s already been built. Plus, share out to popular destinations like Snowflake, Google Ads, or Meta Ads. Salesforce Data Cloud is the only hyperscale data engine native to Salesforce. It is more than a CDP. It goes beyond a data lake. You can do more with Data Cloud. Your Agentforce journey begins with Data Cloud. Agents need the right data to work. With Data Cloud, you can create the most intelligent agents possible, giving them access to the exact data they need to deliver any employee or customer experience. Use any data in your organization with Agentforce in a safe and secure manner thanks to the Einstein 1 Trust Layer. Datablazers are Salesforce community members who are passionate about driving business growth with data and AI powered by Data Cloud. Sign up to join a growing group of members to learn, connect, and grow with Data Cloud. Join today. The path to AI success begins and ends with quality data. Business, IT, and analytics decision makers with high data maturity were 2x more likely than low-maturity leaders to have the quality data needed to use AI effectively, according to our State of Data and Analytics report. “What’s data maturity?” you might wonder. Hang tight, we’ll explain in chapter 1 of this guide. Data-leading companies also experience: Your data strategy isn’t just important, it’s critical in getting you to the head of the market with new AI technology by your side. That’s why this Salesforce guide is based on recent industry findings and provides best practices to help your company get the most from your data. Tectonic will be sharing a focus on the 360 degree customer view with Salesforce Data Cloud in our insights. Stay tuned. It is Time to Implement Data Cloud 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 3, 2025in Data, Einstein 1 Platform, Einstein Insights, Google, Salesforce, Salesforce Data Cloud, Salesforce Implementation Services, Snowflake, 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, Technology

27Feb

Agentforce and Commerce Cloud

SharkNinja, a global product design and technology company, is implementing Salesforce’s Agentforce and Commerce Cloud to enhance its global customer service operations. The company, known for its Shark and Ninja brands of household products, aims to scale support across more than 30 markets using autonomous agents. Agentforce will create an AI-powered digital workforce available 24/7 to assist customers with buying processes, product inquiries, troubleshooting, and returns management. This implementation will allow human agents to focus on high-impact interactions while providing tailored support based on customer data and purchase history. The integration of Commerce Cloud will enable SharkNinja to consolidate customer data from multiple sources into a unified view, facilitating more personalized shopping experiences and better tracking of customer engagement across their global customer base. Salesforce (NYSE: CRM), the world’s #1 AI CRM, today announced that SharkNinja, a global product design and technology company, is implementing Agentforce and other Salesforce products, including Commerce Cloud, to drive global growth by scaling its personalized customer service approach with autonomous agents. SharkNinja is a global leader in indoor and outdoor household products, transforming how people cook, clean, and live in homes around the world. As the innovation powerhouse behind two multi-billion-dollar brands — Shark and Ninja — SharkNinja is renowned for its diversified portfolio of cutting-edge products, including Shark vacuum cleaners and beauty tools, as well as Ninja kitchen appliances, such as blenders, air fryers, and ice cream makers. To support its rapid, global growth, SharkNinja is embracing solutions that will scale support and service more efficiently across more than 30 markets while delivering a seamless consumer shopping experience. Agentforce, a new layer on the Salesforce Platform, will enable SharkNinja to easily build and deploy AI agents that can autonomously take action across any business function. With Agentforce, SharkNinja will have an always-on, digital workforce available 24/7 to guide customers through the buying process, answer product questions, troubleshoot issues, and manage returns — streamlining human agent workloads so they can focus on meaningful, high-impact interactions. “Innovation is the driver behind every product SharkNinja creates across our vast portfolio, so it was really important to find a tool that could give us the capabilities needed to be just as innovative across every consumer interaction,” said Velia Carboni, CIO, SharkNinja. “We believe Agentforce is this key to helping us build a community that keeps consumers coming back as we continue to grow and develop new problem-solving innovations that positively impact people’s lives in homes around the world.” “SharkNinja prioritizes quality, innovation, and an exceptional customer experience,” said Adam Evans, EVP & GM of Salesforce AI Platform. “By integrating customer data with service and support functions, Agentforce enables SharkNinja to deliver an exceptional experience at every touchpoint — building customer loyalty and keeping them coming back time and time again.” Agentforce will also help SharkNinja enhance brand loyalty through tailored support interactions that deliver targeted solutions and recommendations based on insights from customer data from previous purchases and service history. SharkNinja will also leverage Commerce Cloud, enabling the company to consolidate customer data from multiple sources into a single, unified view. This integration will enable the delivery of more personalized shopping experiences for each customer. At the same time, having unified touchpoints will allow SharkNinja to more effectively track engagement across its global customer base. 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 Agentforce Platform, Data, Salesforce, Salesforce Commerce Cloud, Technology

27Feb

Is it Real or is it Gen-r-X?

The Rise of AI-Generated Content: A Double-Edged Sword It began with a viral deepfake video of a celebrity singing an unexpected tune. Soon, political figures appeared to say things they never uttered. Before long, hyper-realistic AI-generated content flooded the internet, blurring the line between reality and fabrication. While AI-driven creativity unlocks endless possibilities, it also raises an urgent question: How can society discern truth in an era where anything can be convincingly fabricated? Enter SynthID, Google DeepMind’s pioneering solution designed to embed imperceptible watermarks into AI-generated images, offering a reliable method to verify authenticity. What Is SynthID, and Why Does It Matter? At its core, SynthID is an AI-powered watermarking tool that embeds and detects digital signatures in AI-generated images. Unlike traditional watermarks, which can be removed or altered, SynthID’s markers are nearly invisible to the human eye but detectable by specialized AI models. This innovation represents a significant step in combating AI-generated misinformation while preserving the integrity of creative AI applications. How SynthID Works SynthID’s technology operates in two critical phases: This method ensures that even if an image is slightly edited, resized, or filtered, the SynthID watermark remains intact—making it far more resilient than conventional watermarking techniques. SynthID for AI-Generated Text Large language models (LLMs) generate text one token at a time, where each token may represent a single character, word, or part of a phrase. The model predicts the next most likely token based on preceding words and probability scores assigned to potential options. For example, given the phrase “My favorite tropical fruits are __,” an LLM might predict tokens like “mango,” “lychee,” “papaya,” or “durian.” Each token receives a probability score. When multiple viable options exist, SynthID can adjust these probability scores—without compromising output quality—to embed a detectable signature. (Source: DeepMind) SynthID for AI-Generated Music SynthID converts an audio waveform—a one-dimensional representation of sound—into a spectrogram, a two-dimensional visualization of frequency changes over time. The digital watermark is embedded into this spectrogram before being converted back into an audio waveform. This process leverages audio properties to ensure the watermark remains inaudible to humans, preserving the listening experience. The watermark is robust against common modifications such as noise additions, MP3 compression, or tempo changes. SynthID can also scan audio tracks to detect watermarks at different points, helping determine if segments were generated by Lyria, Google’s advanced AI music model. (Source: DeepMind) The Urgent Need for Digital Watermarking in AI AI-generated content is already disrupting multiple industries: In this chaotic landscape, SynthID serves as a digital signature of truth, offering journalists, artists, regulators, and tech companies a crucial tool for transparency. Real-World Impact: How SynthID Is Being Used Today SynthID is already integrated into Google’s Imagen, a text-to-image AI model, and is being tested across industries: By embedding SynthID into digital content pipelines, these industries are fostering an ecosystem where AI-generated media is traceable, reducing misinformation risks. Challenges & Limitations: Is SynthID Foolproof? While groundbreaking, SynthID is not without challenges: Despite these limitations, SynthID lays the foundation for a future where AI-generated content can be reliably traced. The Future of AI Content Verification Google DeepMind’s SynthID is just the beginning. The battle against AI-generated misinformation may involve: As AI reshapes the digital world, tools like SynthID ensure innovation does not come at the cost of authenticity. The Thin Line Between Trust & Deception AI is a powerful tool, but without safeguards, it can become a weapon of misinformation. SynthID represents a bold step toward transparency, helping society navigate the blurred boundaries between real and artificial content. As the technology evolves, businesses, policymakers, and users must embrace solutions like SynthID to ensure AI enhances reality rather than distorting it. The next time an AI-generated image appears, one might ask: Is it real, or does it carry the invisible signature of SynthID? 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 27, 2025in AI Tools, Artificial Intelligence, Generative AI Tools, Google, Technology

26Feb

AI Through a Unified Platform

Maximizing AI Potential Through a Unified Platform In today’s interconnected business landscape, siloed teams are a thing of the past—especially for organizations aiming to harness the full power of AI. With businesses leveraging an average of 991 applications across various platforms, disconnected systems are all too common, resulting in suboptimal experiences for both employees and customers. A unified platform eliminates these barriers, enabling teams to collaborate effortlessly across departments. By centralizing data on one platform, organizations unlock AI’s true potential, delivering more meaningful and actionable results. But what does a unified platform approach look like? And how does it enable AI agents to transform business processes? Let’s dive in. How AI Agents Perform Better on Unified Platforms Traditional rule-based systems often bombard teams with irrelevant updates triggered by static field values, causing critical information to be overlooked. By contrast, AI agents, guided by human-defined frameworks, operate based on context rather than rigid rules, making them more effective at managing complex workflows. However, for AI agents to succeed, they require seamless access to data and systems. Without a unified platform, they can’t function cohesively across departments. This is where Salesforce’s platform approach stands out—it allows businesses to create both custom and out-of-the-box AI solutions that integrate effortlessly, thanks to a shared metadata foundation. Real-World AI Applications: Smarter Hiring with AI Agents Let’s consider a scenario where a company builds a custom AI agent for its recruiting app to streamline hiring: This approach highlights the value of connecting systems. By enabling seamless data sharing across HR, Legal, and Finance, the AI agent ensures an efficient, collaborative hiring process. Unlike rule-based automation, which follows rigid “if-then” logic, AI agents incorporate context and intelligence into workflows. This makes them more adaptive and impactful, driving real results for businesses. Extending AI’s Reach with Platform Login Licenses While AI agents can transform workflows, not every team member needs full-time access to them. Some employees use these tools daily, while others engage with them occasionally. Platform Login Licenses offer a cost-effective solution by granting temporary, on-demand access. For instance, an employee eligible for a referral bonus might only need to check the HR platform occasionally to track their status. A Platform Login License lets them interact with the AI agent when necessary, without requiring a full-time account. This flexibility ensures companies can extend AI-driven productivity to more users without overspending on unused licenses. Why a Unified Platform Is Essential Salesforce’s unified platform provides the consistency and scalability organizations need, whether using custom-built AI agents or ready-made solutions. By sharing a common metadata foundation, these tools access richer data, leading to smarter decision-making and improved workflows. Seamless data flow between agents and apps enhances collaboration and unlocks the full potential of AI. Teams can work smarter, not harder, with tools tailored to their specific needs, whether they use them daily or occasionally. Start Building Your AI-Driven Future Today AI’s transformative potential lies not just in the technology but in the foundation it’s built upon. A unified platform equips businesses to create powerful, interconnected AI solutions that streamline processes and foster collaboration. With Salesforce’s Platform Login Licenses, you can extend the benefits of AI agents to the right people at the right time—without unnecessary costs. Discover AgentforceLearn how Agentforce AI agents provide continuous support to employees and customers, helping your organization succeed. Explore the Benefits 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 26, 2025in Data, Salesforce, Technology

25Feb

What is Salesforce Marketing Cloud?

Salesforce has strategically expanded its marketing technology tools over the years, integrating a range of powerful technology into its CRM platform. These acquisitions have empowered businesses to manage the full customer journey, enabling digital engagement at scale. Salesforce Marketing Cloud consists of five core product offerings: Let’s dig in to these products, their capabilities, and how they help marketers drive more personalized, data-driven engagement. Marketing Cloud Engagement (MCE) MCE is a robust, cross-channel marketing automation platform designed to create personalized customer journeys. It empowers marketers with AI-driven insights, enabling them to activate customer data and deliver dynamic content across multiple touchpoints, including email, SMS, WhatsApp, push notifications, digital ads, and the web. Built for scalability, MCE provides enterprise-grade reliability and deep integration capabilities. While often associated with B2C use cases, it’s flexible enough to support various business models. With its AI-powered automation and open architecture, MCE helps marketers drive customer lifetime value while optimizing campaign efficiency. Marketing Cloud Account Engagement (MCAE) MCAE, formerly Pardot, is Salesforce’s B2B marketing automation solution, aligning sales and marketing teams with a shared customer view. It streamlines lead management with AI-powered scoring, real-time campaign insights, and automation tools that help businesses prioritize leads and accelerate sales cycles. This platform enables businesses to nurture prospects effectively through email campaigns, landing pages, and web tracking while optimizing engagement and revenue growth. By unifying marketing and sales efforts, MCAE helps teams drive higher lead conversion and sales efficiency. Marketing Cloud Growth/Advanced Editions Designed for small to mid-sized businesses (SMBs), Marketing Cloud Growth and Advanced Editions provide AI-powered marketing capabilities tailored to organizations using Salesforce Sales or Service Cloud Enterprise/Unlimited editions. These editions leverage Salesforce Data Cloud and AI to simplify campaign creation, personalize customer interactions, and offer real-time marketing insights. Key features include Einstein Co-Create for asset generation, automated segmentation, and AI-driven email engagement optimization. As these editions continue to evolve, they will likely bridge the gap between B2B and B2C marketing automation, offering an integrated experience that scales with business needs. Marketing Cloud Personalization (MCP) MCP enables real-time, AI-powered personalization across customer interactions. It uses behavioral data and a unified customer profile to deliver tailored content, offers, and experiences at the right moment. MCP’s capabilities include: By reacting instantly to customer actions, MCP helps businesses increase conversion rates, order values, and customer loyalty. While it integrates with MCE, it operates as a standalone solution to enhance customer engagement strategies. Marketing Cloud Intelligence (MCI) MCI is Salesforce’s marketing analytics solution, designed to unify data across platforms and provide a single source of truth for marketing performance. With MCI, marketers can: MCI eliminates data silos, helping marketers make data-driven decisions that maximize ROI and customer engagement. It complements Salesforce’s analytics tools like CRM Analytics (CRMA) and Tableau but is specifically designed for marketing performance measurement. Marketing Cloud & Data Cloud: A Powerful Combination All Salesforce Marketing Cloud products connect with Salesforce Data Cloud, allowing businesses to enrich customer profiles with engagement data. This integration enables highly targeted segmentation, AI-driven personalization, and automated journey orchestration. By continuously refining customer insights, businesses can create a feedback loop that optimizes engagement and improves marketing effectiveness. This dynamic data sharing allows for real-time personalization at scale, fulfilling the promise of AI-driven marketing. Solving Today’s Marketing Challenges with Marketing Cloud Marketers today face significant challenges, including: Salesforce Marketing Cloud provides a flexible, AI-powered ecosystem that addresses these challenges. Businesses can combine different Marketing Cloud products based on their specific needs, such as using MCE or MCAE for automated journeys, MCP for AI-driven personalization, and MCI for unified performance tracking. By integrating these solutions, marketers can achieve higher engagement, improved ROI, and more efficient campaign execution. Salesforce Marketing Cloud Consulting with Tectonic Optimized marketing begins with unified data and AI-driven insights. At Tectonic, we specialize in configuring Salesforce Marketing Cloud in every flavor to align with your business objectives, ensuring success through: With deep expertise in Salesforce Data Cloud, AI, and marketing automation, Tectonic helps businesses unlock the full potential of Salesforce Marketing Cloud. Learn more about our consulting services or contact us to get started today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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 25, 2025in Agentforce Platform, Data, Enterprise, Marketing Automation, Salesforce, Salesforce Data Cloud, Salesforce Marketing Automation, Salesforce Marketing Cloud, Salesforce Marketing Cloud Account Engagement (Pardot), Salesforce Marketing Cloud Advertising, Salesforce Personalization, Technology

24Feb

The Event-Driven Paradigm for Next-Generation AI Agents

The Infrastructure Imperative for AI Evolution The enterprise landscape stands at an inflection point where AI agents promise autonomous decision-making and adaptive workflows at scale. However, the critical barrier to realizing this potential isn’t model sophistication—it’s architectural. True agentic systems require: These requirements fundamentally represent an infrastructure challenge that demands event-driven architecture (EDA) as the foundational framework for agent deployment and scaling. The Three Waves of AI Evolution First Wave: Predictive Models Characterized by: These deterministic systems excelled at specialized tasks but proved rigid and unscalable across business functions. Second Wave: Generative Models Marked by breakthroughs in: However, these models remained constrained by: Third Wave: Agentic Systems Emerging capabilities include: This evolution shifts focus from model architecture to system architecture, where EDA becomes the critical enabler. The Compound AI Advantage Modern agent systems combine multiple architectural components: This compound approach overcomes the limitations of standalone models through: Event-Driven Architecture: The Nervous System for Agents Core EDA Principles for AI Systems Implementation Benefits Architectural Patterns for Agentic Systems 1. Reflective Processing <img src=”reflection-pattern.png” width=”400″ alt=”Reflection design pattern diagram”> Agents employ meta-cognition to: 2. Dynamic Tool Orchestration <img src=”tool-use-pattern.png” width=”400″ alt=”Tool use design pattern diagram”> Capabilities include: 3. Hierarchical Planning <img src=”planning-pattern.png” width=”400″ alt=”Planning design pattern diagram”> Features: 4. Collaborative Multi-Agent Systems <img src=”multi-agent-pattern.png” width=”400″ alt=”Multi-agent collaboration diagram”> Enables: The Enterprise Integration Challenge Critical Success Factors Implementation Roadmap Phase 1: Foundation Phase 2: Capability Expansion Phase 3: Optimization The Competitive Imperative Enterprise readiness data reveals: Early adopters of event-driven agent architectures gain: The transition to agentic operations represents not just technological evolution but fundamental business transformation. Organizations that implement EDA foundations today will dominate the AI-powered enterprise landscape of tomorrow. Those failing to adapt risk joining the legacy systems they currently maintain—as historical footnotes in the annals of digital transformation. 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 Data, Enterprise, Technology

24Feb

Physicians Embrace AI for Documentation

Physicians Embrace AI for Documentation, But Diagnostic Concerns Persist A new survey reveals growing physician adoption of AI for clinical documentation and administrative tasks, yet lingering skepticism remains about its role in medical decision-making. According to Athenahealth’s fourth annual Physician Sentiment Survey, conducted by The Harris Poll, 68% of physicians using AI in their practice report increased reliance on the technology for clinical documentation over the past year. The top AI applications they value include: Shifting Perceptions of AI in Healthcare Physician attitudes toward AI are evolving: However, significant concerns persist: Interoperability Remains a Key Challenge While 91% of physicians agree that better data exchange between systems would improve patient outcomes, more than half report weekly frustrations accessing patient records from other providers. Additionally: Patient Portals: A Double-Edged Sword Though widely used, patient portals contribute to physician burnout: Job Satisfaction Shows Modest Gains Despite ongoing challenges, physician morale appears to be improving: The Path Forward: Balancing Innovation and Trust Dr. Nele Jessel, Chief Medical Officer at Athenahealth, noted: “AI is moving from hype to reality, and physicians are seeing real benefits in reducing administrative burdens. But we must ensure these tools are designed to support—not replace—clinical judgment, while improving efficiency across practices of all sizes.” As AI adoption grows, addressing diagnostic reliability, interoperability gaps, and workload pressures will be critical to sustaining physician trust and improving patient care. Key Takeaways:✅ AI use is rising for documentation, but diagnostic trust lags.⚠️ Interoperability frustrations persist, driving physician stress.📈 Job satisfaction is improving, yet burnout risks remain.🔮 Future success hinges on human-AI collaboration and better system integration. 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 24, 2025in AI Tools, Data, Life Sciences Cloud, Salesforce, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Technology

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, Technology

Amadeus and Salesforce Expand Partnership

Amadeus and Salesforce Expand Partnership to Transform Hotel Service Centers Amadeus is deepening its collaboration with Salesforce to develop a next-generation hotel service center, designed to tackle key challenges in hospitality reservations and customer service. Currently in development, this innovative solution integrates Salesforce Service Cloud with Amadeus’ Central Reservations Systems and Guest Interaction solutions, targeting the global hospitality market. Enhancing the Guest Experience and Driving Revenue At its core, the new service center will elevate the Amadeus Central Reservations System (ACRS™) and iHotelier® by streamlining booking experiences and transforming how call center agents assist travelers. Key benefits include: By leveraging intelligent automation and real-time guest insights, hotels can enhance customer interactions, drive incremental revenue, and deliver more tailored experiences. A Flexible, Scalable Solution for All Hotel Operators The new service center is designed for maximum adaptability, enabling hoteliers to seamlessly integrate with Salesforce Service Cloud while scaling from entry-level solutions to advanced implementations. Capabilities range from basic booking and guest profile management to advanced features such as: ✔ Agent task automation for improved efficiency.✔ Ongoing case management to ensure seamless guest support.✔ Omnichannel communications for a unified guest experience. From boutique hotels to global chains, operators can now access enterprise-grade technology tailored to their needs, boosting both service quality and operational efficiency. Leveraging AI and Automation to Empower Hotel Agents Recognizing the potential of Agentforce to enhance productivity, Amadeus is exploring AI-driven automation and intelligent case management to further streamline workflows and optimize customer service operations. Brian Landsman, EVP, Global Business Development and Partnerships at Salesforce, stated: “Building on the success of Amadeus Delphi® on Salesforce, Amadeus has chosen the Salesforce Platform and Agentforce to scale its new Service Center offering. This collaboration empowers customer service representatives with the combined power of Salesforce Service Cloud and Amadeus’ ACRS and iHotelier solutions. We see incredible potential in continuing to bring innovations to our mutual customers.” Peter Waters, Executive Vice President, Hotel IT Solutions at Amadeus, added: “We’re thrilled to expand our partnership with Salesforce to deliver an end-to-end solution that enhances hotel guest services while driving bookings and revenue. By optimizing guest management and service workflows, this next-generation service center will redefine hospitality operations.” Tectonic has additional implemented Salesforce Marketing Cloud Engagement with Amadeus for marketing automation. About Amadeus Amadeus powers personalized, seamless travel experiences, helping hospitality providers attract, serve, and retain guests. With over 30 years of expertise, Amadeus develops cutting-edge, open software solutions that drive operational efficiency and customer satisfaction. With a presence in 175+ countries, Amadeus is committed to enabling hotels to create unforgettable guest experiences while maximizing revenue opportunities. 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 21, 2025in Data, Enterprise, Salesforce, Salesforce Service Cloud, Technology

$15 Million to AI Training for U.S. Government Workforce

19Feb

AI Adoption in the Federal Government

AI Adoption in the Federal Government: A New Era Under the Trump Administration With a new administration in Washington and a $500 billion AI infrastructure initiative underway, the U.S. federal government may be entering a phase of accelerated AI adoption. Federal AI Expansion AI adoption grew under the Biden administration, with agencies leveraging it for fraud detection, workflow automation, and data analysis. However, experts predict that the Trump administration will further expand federal AI use. “Trump and his advisers have spoken about ‘unleashing AI,’ signaling a push for broader adoption within government agencies,” said Darrell West, a senior fellow at the Brookings Institution’s Center for Technology Innovation. As the administration scales back AI safety regulations and deepens ties with major tech firms, federal AI usage is expected to rise. However, ensuring transparency and educating the public remain crucial for building trust in government AI applications. AI Governance Framework The foundation for federal AI governance was established under Trump’s first term, with executive orders EO 13859 (2019) and EO 13960 (2020). EO 13960 mandated an annual AI use case inventory, significantly expanding under Biden—from 710 cases in 2023 to 2,133 in 2024. Reggie Townsend, VP of Data Ethics at SAS and a National AI Advisory Committee (NAIAC) member, emphasized the importance of this transparency: “The inventory was a crucial first step in building public trust.” Biden’s EO 14110 (2023) introduced stronger AI guardrails, requiring agencies to designate chief AI officers, disclose safety-related AI use cases, and implement risk management guidelines. However, on his first day in office, Trump rescinded EO 14110, signaling a shift toward deregulation. AI Applications in Government The 2024 federal AI inventory reported 2,133 AI use cases across 41 agencies. The Department of Health and Human Services (HHS) led with 271 cases, reflecting a 66% increase from the previous year. Key applications include: Harvard Kennedy School adjunct lecturer Bruce Schneier anticipates even broader AI integration in government, from automating reports to drafting legislation and conducting audits. Despite growing interest, the federal government lags behind the private sector in AI adoption, especially for generative AI, due to concerns over bias, reliability, and transparency. AI Under a Second Trump Term Trump’s return to office in 2025 signals an AI policy shift favoring reduced oversight and enhanced global AI leadership. “Federal AI adoption will accelerate under Trump,” West said, citing efforts to integrate major tech figures into federal initiatives. Notably, Trump appointed xAI owner Elon Musk to lead the newly rebranded Department of Government Efficiency, formerly the U.S. Digital Service. This agency is tasked with modernizing federal technology, reducing costs, and driving deregulation. With EO 14110 rescinded, the scope of AI governance under Trump remains uncertain. “Will he eliminate all guardrails, or keep some protections? That’s something to watch,” West noted. Big Tech’s Role in Federal AI Trump’s inauguration underscored tech industry influence, with Elon Musk, Mark Zuckerberg, Jeff Bezos, and Sundar Pichai in attendance. Major tech firms, including Amazon, Google, and Microsoft, each contributed $1 million to the event, while OpenAI CEO Sam Altman made a personal $1 million donation. Some companies are aligning with the administration’s stance on AI and content moderation. Meta, for instance, has replaced its fact-checking services with a community-driven model similar to X’s Community Notes and relaxed its moderation policies. A deregulated AI landscape could benefit big tech, particularly in areas like AI safety standards and data copyright issues, while advancing the administration’s vision for U.S. AI dominance. AI’s Future in Government On his second day in office, Trump announced a $500 billion AI infrastructure investment, forming Stargate—a coalition of OpenAI, SoftBank, MGX, and Oracle—to expand AI infrastructure nationwide. “This will be the largest AI infrastructure project in history,” Trump declared, emphasizing the need for AI leadership against global competitors like China. However, West warned that accelerated adoption must be managed carefully: “It’s critical that AI is implemented fairly, with privacy and security safeguards in place.” Building AI Literacy Effective AI deployment requires education within federal agencies. “Many government workers lack AI expertise, making it difficult to procure and implement AI solutions effectively,” West said. NAIAC’s Townsend advocates for structured AI training, tailored to different federal roles. Public AI literacy is also crucial, with initiatives like the National AI Research Resource (NAIRR) promoting equitable access to AI education and development. “The public must be informed enough to hold the government accountable on AI issues,” Townsend concluded. As AI adoption accelerates, striking a balance between innovation, oversight, and public trust will define the next phase of federal AI policy. 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 19, 2025in Data, Generative AI, Google, Public Sector Solutions, Technology

18Feb

Gen AI Trust Layers

Addressing the Generative AI Production Gap with Trust Layers Despite the growing excitement around generative AI, only a small percentage of projects have successfully moved into production. A key barrier is the persistent concern over large language models (LLMs) generating hallucinations—responses that are inconsistent or completely disconnected from reality. To address these issues, organizations are increasingly adopting AI trust layers to enhance reliability and mitigate risk. Understanding the Challenge Generative AI models, like LLMs, are powerful tools trained on vast amounts of unstructured data, enabling them to answer questions and complete tasks based on text, documents, recordings, images, and videos. This capability has revolutionized the creation of chatbots, co-pilots, and even semi-autonomous agents. However, these models are inherently non-deterministic, meaning they don’t always produce consistent outputs. This lack of predictability leads to the infamous phenomenon of hallucination—what the National Institute of Standards and Technology (NIST) terms “confabulation.” While hallucination is a byproduct of how generative models function, its risks in mission-critical applications cannot be ignored. Implementing AI Trust Layers To address these challenges, organizations are turning to AI trust layers—frameworks designed to monitor and control generative AI behavior. These trust layers vary in implementation: Galileo: Building AI Trust from the Ground Up Galileo, founded in 2021 by Yash Sheth, Atindriyo Sanyal, and Vikram Chatterji, has emerged as a leader in developing AI trust solutions. Drawing on his decade of experience at Google building LLMs for speech recognition, Sheth recognized early on that non-deterministic AI systems needed robust trust frameworks to achieve widespread adoption in enterprise settings. The Need for Trust in Mission-Critical AI “Sheth explained: ‘Generative AI doesn’t give you the same answer every time. To mitigate risk in mission-critical tasks, you need a trust framework to ensure these models behave as expected in production.’ Enterprises, which prioritize privacy, security, and reputation, require this level of assurance before deploying LLMs at scale. Galileo’s Approach to Trust Layers Galileo’s AI trust layer is built on its proprietary foundation model, which evaluates the behavior of target LLMs. This approach is bolstered by metrics and real-time guardrails to block undesirable outcomes, such as hallucinations, data leaks, or harmful outputs. Key Products in Galileo’s Suite Sheth described the underlying technology: “Our evaluation foundation models are dependable, reliable, and scalable. They run continuously in production, ensuring bad outcomes are blocked in real time.” By combining these components, Galileo provides enterprises with a trust layer that gives them confidence in their generative AI applications, mirroring the reliability of traditional software systems. From Research to Real-World Impact Unlike vendors who quickly adapted traditional machine learning frameworks for generative AI, Galileo spent two years conducting research and developing its Generative AI Studio, launched in August 2023. This thorough approach has started to pay off: A Crucial Moment for AI Trust Layers As enterprises prepare to move generative AI experiments into production, trust layers are becoming essential. These frameworks address lingering concerns about the unpredictable nature of LLMs, allowing organizations to scale AI while minimizing risk. Sheth emphasized the stakes: “When mission-critical software starts becoming infused with AI, trust layers will define whether we progress or regress to the stone ages of software. That’s what’s holding back proof-of-concepts from reaching production.” With Galileo’s innovative approach, enterprises now have a path to unlock the full potential of generative AI—responsibly, securely, and at scale. 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 18, 2025in Data, Enterprise, Generative AI, Google, Salesforce, Technology

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, Technology

Technology

Generative AI in Marketing

The Event-Driven Paradigm for Next-Generation AI Agents

Physicians Embrace AI for Documentation

AI Adoption in the Federal Government

Gen AI Trust Layers

Recent Posts

Digital-First Auto Expectations

Optimizing AI Agent Performance Through Strategic Data Management

Customer Relationship Management

Salesforce’s Vision for the Future of Service Cloud & Contact Center Integration

Benefits of Salesforce Essentials for Small Businesses

Contact Us

Be in touch today — and start your business on a path to success.

Category

Archives

Technology

Recent Posts

Contact Us

Be in touch today — and start your business on a path to success.

Category

Tags

Archives

Subscribe to our mailing list. Join our mail list to receive our newsletter