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Agentforce Aids Distribution

Good360’s manual processes slowed their ability to distribute essential goods to communities in crisis. Agentforce will speed up distribution and automate donation matching for greater impact. About Good360 Good360 is a nonprofit organization on a mission to close the need gap. They work with corporate donors and nonprofit partners to put essential items like clothing and household items to great use. The Challenge for Good360 Manual processes hinder Good360’s efforts to get product donations to the people who need them. Good360 has been bridging the gap between surplus and demand since 1983, when it was founded to help distribute million worth of donated office equipment to nonprofits. What started as a single act of generosity has grown into a nationwide operation that’s distributed more than $18 billion in essential products, helped more than 100 million people, and kept all those excess goods out of landfills in the process. With millions of people in the U.S. living in poverty and natural disasters increasing in frequency and intensity, demand for their services will only continue to grow. From distributing emergency supplies to communities devastated by Hurricane Helene in Georgia to bringing comfort to NICU families in Florida, Good360’s impact is widely varied and deeply felt. Their challenge isn’t typically a lack of donations, rather, it’s ensuring that donations reach the right people at the right time. When a corporation notifies Good360 that a donation is ready, the matching team must manually search their network of tens of thousands of nonprofit partners to assess which ones have an urgent need for that type of donation. Then, they have to calculate possible shipping distances, contact the nonprofit to check whether the donation is still essential, and verify the shipping destination. Good360’s work plays a significant role in disaster recovery where every second counts. Even though this work is urgent, only two employees are dedicated to coordinating disaster-related donation matching. “The people who join Good360 are motivated to make a difference, they’re laser-focused on furthering our mission” said Stephane Moulec, Good360’s Chief Technology Officer. “Operations are part of what we do, but anything that streamlines admin so our employees can spend more time on building relationships with nonprofit partners and affected communities is a huge win.” With thousands of truckloads of goods coming in every year, this laborious matching process constrained the number of donations they were able to accept and distribute. “Globally, a significant amount of goods that could be matched to disaster survivors end up going to the landfill,” said Moulec. “Good360 is here to change that.” Good360 is determined to maximize every donation while reducing their carbon footprint and keeping operational costs low. They knew that with the right solution, they could increase the number of donations they’re able to accept, streamline distribution, and ensure critical supplies reach people faster. How Salesforce Helps Good360 Agentforce-powered resource matching is expected to triple Good360’s disaster recovery impact. Good360 is taking their mission to the next level with Agentforce — the agentic layer of the Salesforce Platform. To get goods to disaster-affected communities faster, they’re building a resource-matching agent that automates the donation routing process. Agentforce prioritizes communities that could use the donation most while recommending the nearest location, to reduce fuel consumption. Powered by Data Cloud, which harmonizes data from Nonprofit Cloud and third-party systems like NetSuite, Agentforce will instantly analyze donor, partner nonprofit, community, and logistics data to generate a curated list of top matches for each donation. Nonprofit Cloud unifies data for incoming donations, nonprofit profiles, and fundraising, while the prebuilt connection with NetSuite streamlines inventory, procurement, and business transactions — which will give Agentforce access to critical operational and financial data. Plus, Agentforce’s deep integration with Nonprofit Cloud ensures every donation is properly cataloged and placed where it makes the most sense, considering everything from travel distance and storage to cause alignment. “It was so fast and easy to ground our agents in the right data and test as we went,” said Lashowna Dukes, Good360’s Senior Salesforce Administrator. “We were confident in the logic of the outputs.” For example, if a sportswear company donates 15,000 pairs of unworn children’s shoes to a post-hurricane recovery effort, Agentforce will compile a list of nearby nonprofit partners that supply clothing to children. Instead of manually sorting through their network of tens of thousands of partner nonprofits, the matching team can immediately start outreach based on Agentforce’s recommendations. Once a nonprofit is selected, Agentforce will automatically update Nonprofit Cloud records, schedule shipments with third-party transportation vendors, and provide real-time email updates through Nonprofit Cloud to both the donor and recipient nonprofit. Its integration with Salesforce Maps allows Good360 to visualize the locations of donated products and partner nonprofits, making it easier to optimize routes and reduce transportation emissions. “With resource-matching agents, we’ll transform how we allocate and ship donations, reduce waste, cut our carbon footprint, and deliver disaster relief,” said Moulec. “We estimate this will save our employees over 1,000 hours annually, allowing them to focus on critical frontline response.” With Agentforce, Good360 will be able to connect disaster-affected communities with essential supplies up to three times faster with a goal of reducing its carbon footprint by 20%. It was so fast and easy to ground our agents in the right data and test as we went. We knew we could trust the outputs. Lashowna Dukes Senior Salesforce Administrator, Good360 AI agents will give Good360 the power to scale their impact without stretching their staff. Optimized resource matching is just the start. Good360 sees big potential for Agentforce to support fundraising by handling research, data collection, and impact analysis — freeing up staff to focus on building relationships with donors and nonprofit partners. With hundreds of corporate donors and tens of thousands of nonprofit partners, Agentforce can help Good360 tap into their full network more consistently — something that isn’t possible with manual processes. For example, it can turn unstructured inputs like chats and emails with donors and nonprofit partners into insights for better

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April 9, 2025in Data, Salesforce, Salesforce for Nonprofits, Technology
09Apr

AI Agents Explained

AI agents represent a transformative technological advancement that is reshaping business dynamics, going beyond simple automation to address more complex challenges. This insight provides an in-depth exploration of AI agents, covering their functions, operations, and types, such as reflex, goal-based, utility-based, and learning agents. The commercial advantages of AI agents, including cost-effectiveness, scalability, and efficiency, are highlighted, with examples and applications across various industries to demonstrate their impact on business operations and customer experiences. What Are AI Agents? AI agents are sophisticated computer programs designed to autonomously make decisions based on inputs, enabling them to execute tasks independently. These agents are particularly adept at managing operations in uncertain environments, positioning them as critical steps toward artificial general intelligence—where machines can perform any intellectual task comparable to humans. Modern AI agents offer flexible solutions that significantly enhance business efficiency and customer service. How AI Agents Operate AI agents function as more than just tools; they are dynamic participants redefining how organizations interact with both digital and physical environments. Their core functions include learning, reasoning, and planning, which empower them to make informed decisions and take actions in complex scenarios. For companies aiming to fully leverage these capabilities, AI agents are indispensable. Components of AI Agents AI agents consist of several key components that enable them to function effectively in their environments. These components are crucial for developing intelligent agents capable of operating independently across various contexts: Types of AI Agents Understanding the different types of AI agents is crucial for businesses to select the most appropriate agent for their specific needs: Benefits of AI Agents for Businesses Incorporating AI agents into business operations can deliver numerous benefits, significantly impacting the bottom line. AI agents are revolutionizing corporate operations by enhancing customer experiences and operational efficiency, helping businesses thrive and stay competitive in today’s economy. Key benefits include: Applications of AI Agents AI agents are versatile tools with applications across various sectors: Examples of AI Agents AI agents are revolutionizing various industries with specialized applications: Future Trends in AI Agents The evolution of AI agents continues to shape industries, with future trends expected to redefine their capabilities and applications: AI Agents Transforming Customer Experience (CX) AI agents are key drivers in transforming customer experience (CX), offering more personalized, efficient, and seamless interactions. The integration of natural language processing (NLP) in AI agents enhances automation and personalization in customer engagements. Chatbots and voice assistants provide quick, accurate responses, strengthening brand presence and customer loyalty. AI agents also gather and analyze customer data to offer tailored services, predict customer needs, and provide proactive support. Conclusion AI agents are powerful tools for businesses, offering numerous benefits and applications across industries. They enhance customer experiences, streamline operations, and enable intelligent decision-making. Organizations should stay informed about the different types, benefits, applications, and examples of AI agents to fully leverage their potential for growth and innovation. Tectonic, a leading AI development company, provides customized solutions to meet the unique needs of clients across various industries. Their expertise includes integrating AI-powered chatbots, implementing predictive analytics, and exploring generative AI for creative content generation. Businesses can partner with Tectonic to embark on their AI journey and unlock new opportunities for success. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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April 9, 2025in Agentforce Platform, Agentic AI, Data, Generative AI
AI Agents Are About to Disrupt Your Marketing Channels
01Apr

AI Agents Are About to Disrupt Your Marketing Channels

AI Agents Are About to Disrupt Your Marketing Channels—Here’s How to Adapt The Future of Marketing Isn’t Human-Centric—It’s Agent-Driven AI agents are poised to revolutionize how brands and consumers interact. These autonomous systems don’t just assist—they research, decide, and transact on behalf of users, fundamentally altering the role of traditional marketing channels. Google knows this. That’s why it’s replacing traditional search with Gemini, an AI agent that delivers answers, not just links. Meta is integrating AI across WhatsApp and Messenger, enabling two-way, large-scale brand interactions. Soon, every channel—email, social, loyalty programs, even your website—will become an AI-powered research and transaction hub. The question isn’t if this will impact your marketing strategy—it’s how soon. What Are AI Agents—And Why Should Marketers Care? AI agents are the next evolution of autonomous AI, combining:✅ Generative AI (content creation, personalization)✅ Predictive AI (data-driven decision-making)✅ Complex task execution (end-to-end customer journeys) Today’s challenge? Most companies struggle to move from AI experimentation to real-world impact. Agents change that—they bridge the gap between hype and execution, turning AI potential into measurable business results. 3 Ways to Future-Proof Your Channel Strategy 1. Build a Bulletproof Data Foundation (Now) AI agents won’t just use data—they’ll demand it to make decisions for customers. 🔹 Example: A customer asks an agent, “Find me the best CRM for small businesses.”🔹 Without structured data: The agent may overlook your product.🔹 With optimized data: Your CRM appears as a top recommendation, complete with pricing, features, and a seamless sign-up link. Action Step: Audit your product data, pricing, and USPs. Ensure they’re machine-readable and easily accessible to AI-driven platforms. 2. Rethink “Channels” as AI Conversation Hubs Traditional marketing funnels (search → browse → convert) will collapse. Instead: Action Step: Optimize for AI-native experiences—structured FAQs, API-accessible pricing, and instant conversion paths. 3. Prepare for AI-to-AI Negotiation B2B and high-consideration purchases (e.g., SaaS, automotive, real estate) will see AI agents negotiating deals on behalf of users. 🔹 Example: A corporate procurement AI evaluates your software against competitors, automatically requesting discounts or custom terms.🔹 Winners will be brands that enable AI-friendly decision-making (clear pricing, comparison data, instant approvals). Action Step: Develop agent-friendly sales collateral—dynamic pricing tables, competitor comparisons, and API-driven contract automation. The Bottom Line: Adapt or Get Displaced The shift to agent-driven marketing isn’t gradual—it’s exponential. Companies that wait will find themselves invisible to AI intermediaries shaping customer decisions. Your roadmap: The future belongs to marketers who design for AI-first experiences. The time to act is now. “AI agents won’t just change marketing—they’ll redefine it. The brands that win will be those that engineer their systems for machines, not just people.”—Salesforce AI Research, 2024 Ready to future-proof your strategy? Contact Tectonic. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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April 1, 2025in Agentforce Platform, Data, Generative AI, Google, Salesforce, Salesforce Implementation Services, Salesforce Marketing Automation
Tectonic Salesforce Integrations
25Mar

Digital Experience and Ecommerce Dictionary

Essential Ecommerce and Digital Experience Terminology The world of ecommerce and digital experiences is constantly evolving, bringing with it a steady stream of new buzzwords and concepts. While this list isn’t exhaustive, it serves as an excellent introduction to some of the latest trends and terminology shaping online shopping experiences. Accessibility Ensuring that web content is accessible across all devices is only part of the equation—compliance with international regulations is crucial to supporting individuals with disabilities. Here are key terms related to digital accessibility: Content Marketing Content marketing has evolved beyond catalogs and newsletters. It’s now an integral part of the shopping experience, helping customers discover and engage with brands in meaningful ways. Data-Driven Strategies Leading brands leverage data-driven ecommerce to deliver personalized, seamless shopping experiences. This ever-evolving space includes key concepts such as: Mobile Commerce Consumers expect to shop seamlessly across multiple devices. Mobile commerce trends ensure a smooth experience, no matter where they browse. Omnichannel Experiences Shoppers today expect a cohesive experience whether they shop online, on social media, or in-store. Here are key omnichannel concepts: Tech & Digital Experience Platforms To meet changing consumer expectations, retailers are adopting advanced technologies that streamline operations and enhance user experiences. Social Commerce Social commerce enables direct purchases through social media platforms, streamlining the shopping journey from discovery to checkout. Security & Privacy Data privacy is a growing concern among consumers. Here are key regulations shaping ecommerce security: Search Search functionality is crucial for delivering relevant results and optimizing user experience. Emerging trends include: Final Thoughts The ecommerce webscape is always changing, with new technologies and trends shaping the way consumers shop online. By staying abreast of these key terms, businesses can enhance customer experiences and remain competitive in the digital marketplace. Explore these topics further with our resources and insights! Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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March 25, 2025in Data, Laws, Technology
B2B Customer Service with Agentforce
24Mar

Agents are the Future of Customer Engagement

Agentic Customer Engagement is Here There was a time when customer service meant going into a brick and mortar building and talking to a person face to face. It was time consuming and did not guarantee a solution. The mail order business brought on the need for the 800 number to contact a merchant. The dot com boom brought customer engagement opportunities directly to our homes. Ios and Android apps brought customer engagement to our fingertips. Yet we still were dependent upon the availability of humans or at least chatbots. Customer service often repressed customer engagement, not enhanced it. Agents, like Salesforce Agentforce, brought 24 7 customer engagement to us no matter where we are, when it is, or how complicated our issue is. And agents improved customer service! What’s next? Robots and drones who deliver our items and answer our questions? Who knows. AI bots are transforming client relationships and customer service. To achieve unparalleled efficiency, these intelligent systems plan and automate difficult activities, make deft decisions, and blend in seamlessly with current workflows. Yes, it’s widely believed that AI agents will play a crucial role in the future of customer engagement, offering personalized, efficient, and consistent experiences across various channels.  Here’s why AI agents are poised to be a key driver in customer engagement: AI agents are becoming smarter every day, using machine learning and natural language processing to predict customer needs, handle complex queries with empathy and offer real-time, personalized assistance. How AI Agents Are Redefining Customer Engagement Marketing is undergoing a seismic transformation. Tectonic shift, if you will. The past decade was dominated by complex tech stacks and data integration—now, AI is shifting the focus back to what truly matters: crafting impactful content and campaigns. Welcome to the era of agentic customer engagement and marketing. The Rise of Marketing Agents Unlike traditional customer service agents handling one-to-one interactions, marketing agents amplify human expertise to engage audiences at scale—whether targeting broad segments or hyper-personalized personas. They ensure consistent, high-quality messaging across every channel while automating the intricate backend work of delivering the right content to the right customer at the right time. This shift is powered by rapid AI advancements: How Agentic Engagement Amplifies Marketing Marketing agents don’t replace human creativity—they extend it. Once strategists set guidelines, approve messaging, and define brand voice, agents execute with precision across channels. At Typeface, for example, AI securely learns brand tones and styles to generate on-brand imagery, text, and videos—ensuring every asset aligns with the company’s identity. Key Capabilities of Marketing Agents The Human-Agent Partnership AI agents don’t replace marketers—they empower them. Humans bring creativity, emotional intelligence, and strategic decision-making; agents handle execution, data processing, and scalability. Marketers will evolve into “agent wranglers”, setting objectives, monitoring performance, and ensuring alignment with business goals. Meanwhile, agents will work in interconnected ecosystems—where a content agent’s blog post triggers a social agent’s promotion, while a performance agent optimizes distribution, and a brand agent tracks reception. Preparing for the Agent Era To stay ahead, businesses should:✅ Start small, think big – Pilot agents in low-risk areas before scaling.✅ Train teams – Ensure marketers understand agent management.✅ Build governance frameworks – Define oversight and intervention protocols.✅ Strengthen data infrastructure – Clean, structured data fuels agent effectiveness.✅ Maintain human oversight – Regularly audit agent outputs for quality and alignment. Work with a Salesforce partner like Tectonic to prepare for the Agent Era. The Future is Agentic The age of AI-driven marketing isn’t coming—it’s here. Companies that embrace agentic engagement will unlock unprecedented efficiency, personalization, and impact. The question isn’t if you’ll adopt AI agents—it’s how soon. Ready to accelerate your strategy? Discover how Agentforce (Salesforce’s agentic layer) can cut deployment time by 16x while boosting accuracy by 70%. The future of marketing isn’t just automated—it’s autonomous, adaptive, and agentic. Are you prepared? The Future of Customer Experience: AI-Driven Efficiency and Innovation Businesses have long understood the connection between operational efficiency and superior customer experience (CX). However, the rapid advancement of AI-powered technologies, including next-generation hardware and virtual agents, is transforming this connection into a measurable driver of value creation. Increasingly well-documented use cases for generative AI (GenAI) demonstrate that companies can simultaneously deliver a vastly superior customer experience at a significantly lower cost-to-serve, resulting in substantial financial gains. From Customer Journeys to Autonomous Customer Missions To achieve this ideal balance, companies are shifting from traditional customer journeys—where users actively manage their own experiences via apps—to a more comprehensive approach driven by trusted autonomous agents. These agents are designed to complete specific tasks with minimal human involvement, creating an entirely new paradigm for customer engagement. While early implementations may be rudimentary, the convergence of hardware and AI will lead to sophisticated, seamless experiences far beyond current capabilities. AI-Enabled Internal and External Transformation AI is already driving transformation both internally and externally. Internally, it streamlines processes, enhances employee experiences, and significantly boosts productivity. In customer service operations, for example, GenAI has driven productivity improvements of 15% to 30%, with some companies targeting up to 80% efficiency gains. Externally, AI is reshaping customer interactions, making them more personalized, efficient, and intuitive. Virtual co-pilots assist customers by answering inquiries, processing returns, and curating tailored offers—freeing human employees to focus on complex issues that require nuanced decision-making. Linking Operational Efficiency to Customer Experience Leading organizations are demonstrating how AI-driven efficiencies translate into enhanced CX. Despite these gains, companies must raise the bar even further to fully capitalize on AI’s potential. The convergence of next-generation hardware with AI-driven automation presents an unprecedented opportunity to redefine customer engagement. From App-Driven Experiences to Autonomous Agents At Dreamforce 2024, Salesforce CEO Marc Benioff highlighted that service employees waste over 40% of their time on repetitive, low-value tasks. Similarly, customers face friction in making significant purchases or planning events. Google research indicates that travelers may engage in over 700 digital touchpoints when planning a trip—a fragmented and often frustrating experience. Imagine instead a network of proprietary and third-party agents seamlessly executing customer missions—such as purchasing a car or planning a vacation—without requiring constant user input. These AI agents

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March 24, 2025in Agentforce Platform, Agentic AI, Data, Generative AI, Google, Salesforce

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. Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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March 17, 2025in Agentic AI, Artificial Intelligence, Data, Generative AI, Salesforce Hospitality Travel and Transportation, Technology
13Mar

Why Its Good to be Data-Driven

The Power of Data-Driven Decision Making Success in business hinges on the ability to make informed decisions. Every operational aspect, from minor choices like office furniture selection to critical investments such as multi-million-dollar marketing campaigns, is shaped by a series of interrelated decisions. While instinct and intuition may play a role, most business choices rely on relevant data—covering aspects such as objectives, pricing, technology, and potential risks. However, excess irrelevant data can be just as detrimental as insufficient accurate data. Why Its Good to be Data-Driven organization… The Evolution of Data-Driven Decision Making Organizations that prioritize data-driven strategies rely on accurate, relevant, complete, and timely data. Simply amassing large volumes of information does not equate to better decision-making; companies must democratize data access, ensuring it is available to all employees rather than limited to data analysts. The practice of using data to inform business decisions gained traction in the mid-20th century when researchers identified decision-making as dynamic, complex, and often ambiguous. Early techniques like decision trees and prospect theory emerged in the 1970s alongside computer-aided decision-making models. The 1980s saw the rise of commercial decision support systems, and by the early 21st century, data warehousing and data mining revolutionized analytics. However, without clear governance and organizational policies, these vast data stores often fell short of their potential. Today, the goal of data-driven decision-making is to combine automated decision models with human expertise, creativity, and critical thinking. This approach requires integrating data science with business operations, equipping managers and employees with powerful decision-support tools. Characteristics of a Data-Driven Organization A truly data-driven organization understands the value of its data and maximizes its potential through structured alignment with business objectives. To safeguard and leverage data assets effectively, businesses must implement governance frameworks ensuring compliance with privacy, security, and integrity standards. Key challenges in establishing a data-driven infrastructure include: The Benefits of a Data-Driven Approach Businesses recognize that becoming data-driven requires more than just investing in technology; success depends on strategy and execution. According to KPMG, four critical factors contribute to the success of data-driven initiatives: A data-driven corporate culture accelerates decision-making, enhances employee engagement, and increases overall business value. Integrating ethical considerations into data usage is crucial for mitigating biases and maintaining data integrity. Transitioning to a Data-Driven Business With the rapid advancement of generative AI, data-driven organizations are poised to unlock trillions of dollars in economic value. McKinsey estimates that AI-driven decision-making could add between .6 trillion and .4 trillion annually across key sectors, including customer operations, marketing, software engineering, and R&D. To successfully transition into a data-driven organization, companies must: By embracing a data-driven model, organizations enhance their ability to make automated yet strategically sound decisions. With seamless data integration across CRM, ERP, and business applications, companies empower human decision-makers to apply their expertise to high-quality, actionable insights—driving innovation and competitive advantage in a rapidly evolving marketplace. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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March 13, 2025in Data, Generative AI, Technology
Aligning Strategy and Goals
07Mar

Aligning Strategy and Goals

Aligning Strategy and Goals: Bridging the Gap Between Data and Business Success Aligning data strategy with business goals is critical—but easier said than done. 41% of business leaders report that their data strategy is only partially or not at all aligned with their objectives. Here’s how to close the gap and make data a true driver of business success. 1. Define Your Business Goals Collaboration between business and IT stakeholders is essential. Start by identifying and prioritizing objectives that drive success, such as revenue growth, customer satisfaction, cost reduction, and market expansion. Business Goal How Data Supports It Revenue Growth Use analytics to identify high-value customers and optimize marketing strategies for higher conversions. Customer Satisfaction Leverage trusted customer data to personalize experiences and improve engagement. Cost Reduction Analyze operational data to streamline processes and improve efficiency. Market Expansion Use market and customer insights to identify new growth opportunities. 2. Determine Key Metrics Once goals are clear, define key performance indicators (KPIs) to measure progress. Business Goal Key Metric Revenue Growth Conversion Rate: Measures the percentage of leads converted into paying customers. Customer Satisfaction Retention Rate: Tracks the percentage of returning customers over time. Cost Reduction Operational Efficiency Ratio: Compares operational costs to revenue. Market Expansion Customer Acquisition Rate: Measures the rate of new customer growth. 3. Assess Resources and Budget Evaluate whether you have the systems, tools, and budget needed to support your goals. If customer personalization is a priority, you may need solutions like Data Cloud to unify and leverage customer insights. A strong CRM or data analytics platform may also be required to track specific KPIs. 4. Build a Data-Driven Culture Data maturity is not just about tools—it’s about people. Empower teams with the skills, training, and mindset to leverage data effectively. Change management initiatives and ongoing education will help integrate data into daily decision-making. See how F5 is building a data-driven culture with Tableau:“Data has been transforming our corporate culture right before our eyes. Every day, I wake up learning something new about data.”— Amie Bright, Former RVP of Enterprise Data Strategy and Insights, F5 5. Align Teams for Success Use this handy checklist to ensure alignment across your organization: ✅ Collaborate with business and IT teams to define and prioritize objectives.✅ Develop key data KPIs in partnership with internal stakeholders.✅ Survey team leaders to assess the tools, systems, and budgets needed.✅ Invest in training and change management to build a data-driven culture.✅ Join a data leadership community to gain insights and best practices. Want to accelerate your data strategy? Reach out to Tectonic to get started today. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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March 7, 2025in Data, Enterprise, Salesforce Data Cloud, Technology
Data Data Data
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 Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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March 1, 2025in Artificial Intelligence, Data, Generative AI, Technology
The Coalition for Sustainable AI
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 Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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February 28, 2025in Artificial Intelligence
Salesforce Unified Knowledge
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 Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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February 26, 2025in Data, Salesforce, Technology
The Event-Driven Paradigm for Next-Generation AI Agents
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 Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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February 24, 2025in Data, Enterprise, Technology
Build Launch and Track Campaigns
20Feb

How to Create Professional Meeting Minutes Without MS Co-Pilot

Ever wondered how to draft professional meeting minutes without relying on MS Co-Pilot? While tools like Microsoft Teams can record meetings and generate transcripts, they often come with limitations. For instance, MS Teams requires an MS Co-Pilot subscription to analyze transcripts and create meeting minutes, and even with that, crafting effective prompts for such tools is essential for generating useful outputs. Recently, a colleague sent a meeting recording—without a transcript—and asked us to create the minutes. Here’s how we accomplished this task, step by step. Step 1: Transcribing the Meeting Recording Since AI models cannot directly process audio or video, the first step was to generate a text transcript of the recording. I used Microsoft Word’s Dictate → Transcribe feature, but encountered a roadblock: the recording exceeded the tool’s 300MB file size limit (it was 550MB). To bypass this, I extracted the audio from the video using VLC Media Player, a versatile media tool: With the audio file ready, I returned to Microsoft Word. This time, the smaller file successfully transcribed into a 45-page text document of decent quality. Step 2: Crafting a Prompt for Meeting Minutes Creating effective meeting minutes with an AI model requires a detailed, structured prompt. Think of it as giving precise instructions to a chef—vagueness leads to unsatisfactory results. I started with a simple XML-style prompt for ChatGPT (GPT-4), using tags to organize key elements: plaintextCopyEditYou are an expert in creating meeting minutes from a given transcript. Analyze the provided transcript and generate professional meeting minutes with the specified structure. <transcript> {{meeting_transcript.docx}} </transcript> <structure> – Main Points Discussed – Decisions, Resolutions, and Agreements – Summary of Differing Opinions (if any) – Action Items: Tasks assigned, responsible parties, and deadlines – Follow-Ups: Topics to revisit in future meetings </structure> <instructions> – Stick strictly to the transcript content. – Do not invent or infer information. – Keep the minutes objective, factual, and concise. – Ensure clarity and self-containment for future reference. </instructions> This prompt acted as a baseline, providing clarity and structure for the model to extract and summarize relevant details from the transcript. Step 3: Refining the Prompt Using Anthropic’s Workbench To improve the clarity and effectiveness of the prompt, I used Anthropic’s Workbench, which offers an automatic prompt enhancement tool. The goal was to refine the structure and optimize the instructions. Here’s the improved version generated by Anthropic: plaintextCopyEditYou are an expert in creating professional meeting minutes from transcripts. Analyze the provided transcript and organize the information systematically before drafting the minutes. <meeting_transcript> {{meeting_transcript.docx}} </meeting_transcript> <analysis_structure> 1. Main Points Discussed: – Key topics with relevant quotes from the transcript. 2. Decisions and Agreements: – Summary of resolutions with supporting quotes. 3. Differing Opinions (if any): – Notable disagreements or alternative viewpoints. 4. Action Items: – Tasks, responsible parties, and deadlines. 5. Follow-Up Topics: – Issues or items to revisit in future meetings. </analysis_structure> <guidelines> – Follow the analysis structure before drafting the final minutes. – Use clear, concise language and a professional tone. – Avoid unnecessary details and stick to transcript content. – Ensure the minutes are self-contained and explanatory. </guidelines> This enhanced prompt incorporated a “chain-of-thought” methodology, guiding the model to analyze and organize the information step by step before drafting the final minutes. Exploring Other Tools: OpenAI’s Prompt Improver I also tested OpenAI’s Prompt Improver in its Chat Playground, which generated a similarly refined prompt: plaintextCopyEditCreate professional meeting minutes from the provided transcript. Use the following structure and guidelines to ensure accuracy and clarity: **Transcript:** – File: {{meeting_transcript.docx}} **Structure:** – Main Points Discussed – Decisions and Agreements – Differing Opinions (if any) – Action Items – Follow-Up Topics **Instructions:** – Maintain objectivity and stick to the transcript content. – Use concise yet explanatory language. – Adhere strictly to the structure for clarity and reference. – Avoid unnecessary embellishments or personal insights. **Output Format:** – Use bullet points for clarity, with no more than one level of indentation. – Ensure the minutes are self-contained and useful for future reference. While effective, OpenAI’s output lacked the chain-of-thought methodology and example formatting provided by Anthropic’s tool, which resulted in less structured meeting minutes. Key Takeaways By following this approach, you can produce professional meeting minutes efficiently—no MS Co-Pilot subscription required. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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February 20, 2025in AI Tools, Data, Enterprise
Navigating the CRM Split for Drugmakers
20Feb

Navigating the CRM Split for Drugmakers

Salesforce vs. Veeva: Navigating the CRM Split for Drugmakers The long-standing partnership between Salesforce and Veeva is coming to an end, forcing pharmaceutical companies to decide which platform best suits their evolving needs. A Strategic Decision, Not Just an IT Shift As the contract between the two companies expires this September, drugmakers have until 2030 to choose their path. While some view the shift as a simple migration, industry leaders warn that this decision carries deeper strategic implications. “Sometimes this is being seen as just an IT migration—but no, if you’re just migrating, you’re missing the strategic importance of this,” said Nancy Phelan, SVP and Head of Customer Engagement at Trinity Life Sciences. “Leaders are realizing this is a much bigger decision, requiring thoughtful consideration of timing, approach, and long-term business impact.” A Messy Divorce? In some ways, the split has turned into a battle, with both companies scrambling to win over clients. By the end of December, Salesforce had reportedly poached several major customers from Veeva, which currently holds around 80% market share in life sciences. Both companies are adapting to drastic changes in the healthcare landscape, including an explosion of data, increasingly complex therapies, and evolving customer needs. From what Phelan has observed, drugmakers aren’t gravitating toward one side or the other based on company size, pipeline, or core focus. Instead, both platforms offer distinct advantages that could shape the user experience in different ways. Why the Split? Veeva’s decision to leave the Salesforce platform stems from mounting limitations and risks that made a standalone approach more appealing. According to a report by Everest Group, the separation will shrink Salesforce’s footprint in life sciences, but its broader market presence may fuel faster development of next-generation technologies. Veeva, on the other hand, is doubling down on its industry-specific capabilities, aiming to enhance its tailored solutions for pharma and biotech companies. A Changing Landscape For nearly two decades, Salesforce and Veeva have been intertwined, with Veeva building its life sciences CRM on Salesforce’s platform. Now, both companies are introducing new solutions, reflecting shifts in the pharmaceutical business model. “Companies like Pfizer or Novartis last made this decision more than 15 years ago,” Phelan noted. “Back then, specialty pharmacy complexities, field reimbursement challenges, and patient affordability concerns weren’t as prominent as they are today.” Additionally, the rise of AI and big data analytics has transformed the role of CRM platforms, making the Salesforce-Veeva decision more complex than ever. Two Roads, Two Strategies The key difference between the platforms moving forward will be how they align with drugmakers’ priorities: What’s Next for Pharma? As the transition nears, both Veeva and Salesforce are putting their best foot forward. Fortunately, pharma companies still have time to evaluate their options. “How we’re advising companies is, you’ve got a window of time and a future that is radically different from the last time you made this decision,” Phelan said. “You need to strategically assess the pieces that are important to you.” With the deadline approaching, drugmakers must determine which path aligns best with their long-term vision. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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February 20, 2025in Data, Life Sciences Cloud, Salesforce
gettectonic.com