AI Tools Archives - gettectonic.com - Page 12

AI Agents Are the Next Wave of Generative Technology

How AI Agents Are the Next Wave of Generative Technology The rise of agentic technology marks a pivotal evolution in artificial intelligence, signaling a shift from mere assistive tools to autonomous agents capable of complex, multi-step tasks. While excitement abounds, this new wave of AI also raises questions about its practical application and long-term impact. AI agents—autonomous tools designed to perform tasks independently—are rapidly gaining traction across industries. Vendors and developers are positioning them as the future of generative AI, enabling organizations to streamline workflows and unlock new efficiencies. However, concerns remain regarding the scope of tasks assigned to these agents and their return on investment (ROI). A Growing Presence at Industry Events The growing curiosity and cautious optimism surrounding AI agents were palpable at the recent AI Summit conference. “AI agents are here, and they’re scaling,” said Tim Cotten, CEO and founder of Scripted Inc., a generative AI platform for game development powered by autonomous agents. Speaking during a session on AI in game development, Cotten predicted that a third of companies present at the summit would likely adopt agent-based solutions in the near future. Cotten emphasized the transformative potential of AI agents: “Agents allow you to spread your influence while you’re asleep. They can do the job for you, generate new ideas, and even create additional agents to tackle emerging needs.” However, he also highlighted a critical challenge: ensuring agents remain focused on well-defined tasks. Overloading agents with responsibilities beyond their scope can lead to inefficiencies and diminished outcomes. Specialized Agents vs. General-Purpose Agents The debate between using specialized agents versus general-purpose agents continues to shape the discussion around agentic AI. According to Atif Khan, Chief Artificial Intelligence Officer at Semantex: “If you have a large application with different components, it’s better to deploy specialized agents for each task. For example, one agent could handle search, another documentation, and others for accounting or customer service.” Khan advocated for modular, independent agents that can be trained and refined individually, rather than a single, all-encompassing agent. This approach not only improves efficiency but also reduces the risk of “hallucinations,” or inaccuracies, that can arise when agents attempt to manage overly complex workloads. Mitigating Risks and Maintaining Oversight Despite their autonomy, AI agents still require oversight to ensure accuracy and compliance. Drayton Wade, COO at Kognitos, stressed the importance of human validation: “Organizations must determine where human review is necessary, especially in high-stakes environments like finance, where agents operate at scale and speed.” Logging agent activities and involving humans in critical decision-making processes can mitigate risks and create accountability, Wade added. Agentforce for Sales: Unlocking New Possibilities One of the most promising applications of agentic technology is in sales, where AI agents can significantly boost productivity and enhance customer experiences. Agentforce for Sales is a cutting-edge suite of tools designed to create and deploy both autonomous and assistive sales agents within Salesforce environments. These agents handle tasks such as lead qualification, pipeline building, case deflection, and sales coaching, allowing sales reps to focus on high-value activities. Types of Agentforce Sales Agents The Future of Agentic Technology AI agents are ushering in a new era of generative technology, enabling businesses to scale operations and optimize workflows. However, their success depends on thoughtful deployment, clear task delineation, and ongoing human oversight. By embracing solutions like Agentforce for Sales, organizations can maximize the potential of agentic AI, transforming how teams work and paving the way for continued innovation. The question is no longer whether to adopt AI agents but how to deploy them effectively to achieve lasting impact. 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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December 8, 2024in Artificial Intelligence, Data, Generative AI, Salesforce, Technology
ai arms race
05Dec

AI Arms Race

AI Arms Race: Providers Catching Up to Payers in Claims Review The healthcare sector is in the midst of an escalating AI arms race as providers adopt the same artificial intelligence technologies payers are leveraging for claims review. Insurers currently lead this race, using AI to streamline processes such as prior authorizations, but experts predict providers will soon narrow the gap. Insurers’ AI Advantage Leading payers, including UnitedHealth, Humana, and Cigna, have integrated algorithmic decision tools to assess claims and determine coverage eligibility. These technologies allow insurers to flag services that fall outside plan criteria, ostensibly increasing efficiency. This trend is expanding, as evidenced by Blue Shield of California’s announcement of a partnership with Salesforce to pilot claims automation technology in early 2025. The nonprofit insurer claims this initiative will reduce prior authorization decision times from weeks or days to mere seconds, benefiting providers and patients alike. However, provider experiences paint a more contentious picture. Reports from lawmakers and healthcare executives suggest AI-driven claims processes lead to a surge in denials. For example, Providence CFO Greg Hoffman revealed that AI adoption by payers resulted in a 50% increase in underpayments and initial denials over two years, forcing providers to significantly increase manual interventions to resolve claims. A Battle for Balance The imbalance in AI adoption has prompted providers to take action. Experts like Jeffrey Cribbs, a vice president analyst at Gartner, see this as a forced “arms race” in which both sides are continually refining their tools. While payers focus on flagging potential exceptions, providers are working to develop systems for more efficient claims submissions and dispute resolution. Providence’s strategy includes outsourcing revenue cycle management to R1, a 10-year partnership designed to quickly address rising claims denials. Hoffman explained that building equivalent AI systems internally would take years, making partnerships essential for staying competitive in the short term. Collaboration Among Providers On the provider side, executives like Sara Vaezy, EVP and Chief Strategy Officer at Providence, emphasize the need for collaboration. She advocates for coalitions to share data and establish AI standards, which would allow providers to compete more effectively. Panelists at HLTH echoed this sentiment. Amit Phull, Chief Physician Experience Officer at Doximity, argued that AI could eventually “level the playing field” for providers by reducing the time required for claims documentation. Deloitte principal consultant Bill Fera added that AI would allow providers to quickly analyze policies and determine whether a patient qualifies for coverage under plan terms. The Road Ahead Despite the current disparity, experts believe AI will eventually equalize the claims review process. Providers are beginning to invest in tools that will help them handle vast amounts of data efficiently, offering clarity in disputes and cutting down documentation time. “It’s still early innings,” Phull said, “but the technology is going to go a long way toward leveling that playing field.” For now, however, insurers maintain the upper hand. As providers navigate the complexities of AI adoption, partnerships and collaboration may prove critical in ensuring they remain competitive in this rapidly evolving landscape. 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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December 5, 2024in Artificial Intelligence, Data, Salesforce, Technology
computer vision models
05Dec

Computer Vision Models

Introduction to Visual Language Models (VLMs): The Future of Computer Vision Achieving a 28% Boost in Multimodal Image Search Accuracy with VLMs Until recently, artificial intelligence models were narrowly focused—either excelling in understanding text or interpreting images, but rarely both. This siloed approach limited their potential to process and connect different types of data. The development of general-purpose language models, like GPTs, marked a significant leap forward. These models transitioned AI from task-specific systems to powerful, versatile tools capable of handling a wide range of language-driven tasks. Yet, despite their advancements, language models and computer vision systems evolved separately, like having the ability to hear without seeing—or vice versa. Visual Language Models (VLMs) bridge this gap, combining the capabilities of language and vision to create multimodal systems. In this article, we’ll explore VLMs’ architecture, training methods, challenges, and transformative potential in fields like image search. We’ll also examine how implementing VLMs revolutionized an AI-powered search engine. What Are Visual Language Models (VLMs)? VLMs represent the next step in the evolution of AI: multimodal models capable of processing multiple data types, including text, images, audio, and video. Why Multimodal Models? The rise of general-purpose approaches has outpaced narrow, specialized systems in recent years. Here’s why: At their core, VLMs receive input in the form of images and text (called “instructs”) and produce textual responses. Their range of applications includes image classification, description, interpretation, and more. For example: These tasks showcase VLMs’ ability to tackle a variety of challenges, including zero-shot and one-shot scenarios, where minimal prior training is required. How Do VLMs Work? Core Components VLMs typically consist of three main components: Workflow Adapters: The Key to Multimodality Adapters facilitate interaction between the image encoder and LLM. There are two primary types: Training VLMs Pre-Training VLMs are built on pre-trained LLMs and image encoders. Pre-training focuses on linking text and image modalities, as well as embedding world knowledge from visual data. Three types of data are used: Alignment Alignment fine-tunes VLMs for high-quality responses. This involves: Quality Evaluation Evaluating VLM performance involves: Revolutionizing Image Search with VLMs Incorporating VLMs into search engines transforms user experience by integrating text and image inputs. Previous Pipeline VLM-Powered Pipeline With VLMs, image and text inputs are processed together, creating a streamlined, accurate system that outperforms traditional pipelines by 28%. Conclusion Visual Language Models are a game-changer for AI, breaking down barriers between language and vision. From multimodal search engines to advanced problem-solving, VLMs unlock new possibilities in computer vision and beyond. The future of AI is multimodal—and VLMs are leading the way. 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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December 5, 2024in Artificial Intelligence, Data, Technology
rise of digital workers
04Dec

Rise of Digital Workers

The Rise of Digital Workers: Unlocking a New Era of Opportunity Over the past two years, advancements in artificial intelligence have sparked a revolution in how humans work, live, and connect. While impressive generative AI models have garnered significant attention, a new paradigm of autonomous AI agents is emerging, promising transformative changes to industries and societies alike. Unlike traditional “predictive AI,” which analyzes data for recommendations, and “generative AI,” which creates content based on learned patterns, autonomous AI agents go a step further. These agents operate independently, executing tasks, making decisions, and even negotiating with other agents. This evolution introduces an intelligent digital workforce capable of scaling operations, reducing costs, and enhancing productivity. Consider a large retailer during the holiday season. Instead of relying on human workers or pre-programmed software to address customer inquiries or update inventory, autonomous agents can seamlessly manage customer interactions, monitor stock levels, reorder items, and coordinate shipping—all without human intervention. This level of automation represents a groundbreaking shift, enabling businesses to operate on an unprecedented scale. Expanding the Reach of Digital Labor Autonomous AI agents are breaking traditional barriers of human availability and physical constraints, enabling businesses to scale globally and more efficiently. These digital workers are not limited by geography, opening opportunities previously restricted to specific locations. However, this shift comes with challenges. Ensuring trust, accountability, and transparency in AI systems is critical. Equally important is investing in human-centric skills such as creativity, critical thinking, and adaptability, which remain uniquely human. Sustainability is another concern, as AI-driven technologies place increasing demands on energy and resources. By addressing these issues, societies can unlock the full potential of digital labor while safeguarding the planet and human values. Transforming Everyday Lives Beyond businesses, autonomous agents are poised to transform personal lives. Personalized agents can act as tutors for students, guiding them through their learning journeys. For individuals, these agents can manage everyday tasks, from scheduling appointments to coordinating complex logistics. In healthcare, AI agents are already alleviating administrative burdens on providers. For example, intelligent agents can handle patient communications, monitor progress, and schedule follow-ups, freeing doctors and nurses to focus on complex cases. Such innovations hold the potential to revolutionize patient care and improve outcomes across the board. Navigating Disruption and Change Like any transformative technology, the rise of autonomous agents will bring disruptions. Some industries will struggle to adapt, and jobs will inevitably evolve—or, in some cases, disappear. History shows, however, that technological revolutions often create far more opportunities than they displace. For example, the U.S. workforce grew by over 100 million jobs between 1950 and 2020, many in industries that didn’t exist before. The key lies in preparing workers for new roles through education and training. Autonomous agents are essential in addressing global challenges such as labor shortages and stagnant productivity growth. They amplify human capabilities, driving innovation and boosting economic output. For example, in the third quarter of 2024, U.S. productivity rose by 2.2%, fueled in part by AI advancements. Driving Innovation and Collaboration AI agents are also fostering innovation, sparking the creation of new companies and industries. More than 5,000 AI-focused startups have emerged in the past decade in the U.S. alone. This trend mirrors the technological revolutions driven by past innovations like microchips, the internet, and smartphones. However, effectively harnessing agentic AI requires collaboration among governments, businesses, nonprofits, and academia. Initiatives like the G7’s framework for AI accountability and the Bletchley Declaration emphasize transparency, safety, and data privacy, offering critical guardrails as AI adoption accelerates. A Vision for the Future Autonomous agents represent a powerful force for change, offering unprecedented opportunities for businesses and individuals alike. By leveraging these technologies responsibly and investing in human potential, societies can ensure a future of abundance and progress. As Marc Benioff, CEO of Salesforce, emphasizes, “AI has the potential to elevate every company, fuel economic growth, uplift communities, and lead to a future of abundance. If trust is our north star, agents will empower us to make a meaningful impact at an unprecedented scale.” 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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December 4, 2024in Agentforce Platform, Artificial Intelligence, Data, Generative AI, Salesforce, Technology
Enhancing OR Efficiency with Ambient Sensor Technology
01Dec

Enhancing OR Efficiency with Ambient Sensor Technology

Implementing ambient sensors in ORs can be challenging, as clinicians may feel uneasy about being recorded. Schwartz noted that emphasizing the benefits of the technology—such as improved accuracy and streamlined communication—has been essential in gaining clinician acceptance. DeDominico highlighted that the AI’s ability to send clinicians relevant updates, such as when a patient is ready for surgery, has increased clinician satisfaction by reducing unnecessary waiting.

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December 1, 2024in AI Tools, Data, Technology
Agentforce Custom AI Agents
29Nov

Agentforce Custom AI Agents

Salesforce Introduces Agentforce: A New AI Platform to Build Custom Digital Agents Salesforce has unveiled Agentforce, its latest AI platform designed to help companies build and deploy intelligent digital agents to automate a wide range of tasks. Building on Salesforce’s generative AI advancements, Agentforce integrates seamlessly with its existing tools, enabling businesses to enhance efficiency and decision-making through automation. Agentforce Custom AI Agents. With applications like generating reports from sales data, summarizing Slack conversations, and routing emails to the appropriate departments, Agentforce offers businesses unprecedented flexibility in automating routine processes. The Problem Agentforce Solves Salesforce’s journey in AI began in 2016 with the launch of Einstein, a suite of AI tools for its CRM software. While Einstein automated some tasks, its capabilities were largely predefined and lacked the flexibility to handle complex, dynamic scenarios. The rapid evolution of generative AI opened new doors for improving natural language understanding and decision-making. This led to innovations like Einstein GPT and later Einstein Copilot, which laid the foundation for Agentforce. With Agentforce, businesses can now create prebuilt or fully customizable agents that adapt to unique business needs. Agentforce Custom AI Agents “We recognized that our customers want to extend the agents we provide or build their own,” said Tyler Carlson, Salesforce’s Vice President of Business Development. How Agentforce Works At the heart of Agentforce is the Atlas Reasoning Engine, a proprietary technology developed by Salesforce. It leverages advanced techniques like ReAct prompting, which allows AI agents to break down problems into steps, reason through them, and iteratively refine their actions until they meet user expectations. Key Features: Ensuring Security and Compliance Given the potential risks of integrating third-party LLMs, Salesforce has implemented robust safeguards, including: AI in Action: Real-World Applications One notable use case of Agentforce is its collaboration with Workday to develop an AI Employee Service Agent. This agent helps employees find answers to HR-related questions using a company’s internal policies and documents. Another example involves agents autonomously managing general email inboxes by analyzing message intent and forwarding emails to relevant teams. “These agents are not monolithic or tied to a single LLM,” Carlson explained. “Their versatility lies in combining different models and technologies for better outcomes.” Measuring Success Salesforce gauges Agentforce’s success through client outcomes and platform adoption. For example, some users report that Agentforce resolves up to 90% of customer inquiries autonomously. Looking ahead, Salesforce aims to expand the Agentforce ecosystem significantly. “By next year, we want thousands of agent skills and topics available for customers to leverage,” Carlson added. A Platform for the Future of AI Agentforce represents Salesforce’s vision of creating autonomous AI agents that empower businesses to work smarter, faster, and more efficiently. With tools like Agentbuilder and integrations across its ecosystem, Salesforce is positioning Agentforce as a cornerstone of AI-led innovation, helping businesses stay ahead in a rapidly evolving technological landscape. 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 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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November 29, 2024in Agentforce Platform, AI Tools, Data, Generative AI, Google, Salesforce, Technology
AI is Revolutionizing Salesforce
29Nov

AI is Revolutionizing Salesforce

AI is Revolutionizing Salesforce: Transforming Sales Teams in the Era of AI Artificial Intelligence (AI) is reshaping the sales landscape, disrupting traditional processes, and redefining how businesses interact with customers. The rapid adoption of AI-native systems is altering how data is captured, how sales teams engage, and how the entire sales cycle is structured. The shift toward AI-driven solutions is fueling unprecedented opportunities for sales organizations to achieve more by doing less manual work. Success in this transformation will favor those who use AI to make smarter, data-driven decisions, shifting the focus from activities to meaningful achievements. From Rolodex to Real-Time Insights: The Evolution of Sales The history of sales is one of continual evolution. From the bartering days of ancient commerce to the introduction of Rolodexes in the mid-20th century, and later to early CRM tools like Act! and Siebel Systems, the industry has always innovated to meet changing customer needs. Salesforce’s arrival in 1999 brought CRM to the cloud, empowering sales teams with unparalleled accessibility. Yet, all these systems had one thing in common—they relied on human input. Logging calls, updating lead statuses, and noting feedback all depended on sales reps’ diligence. That dependency is now being disrupted by AI, which captures and processes data autonomously. AI-Native Systems: Capturing Context Without Human Input AI-native systems represent a seismic shift. Unlike traditional CRMs, these systems capture data in real-time without relying on human intervention. From emails and Slack messages to Zoom calls and social media interactions, AI aggregates unstructured data into actionable insights. This creates a rich, context-driven record of customer behavior, reducing reliance on manual entry and unlocking deeper understanding. Automating the Mundane: Eliminating Data Entry AI is erasing the inefficiencies of manual processes. Sales development representatives (SDRs) once spent countless hours cold-calling, sending follow-ups, and updating records—a monotonous grind that yielded limited value. Today, AI automates these tasks, enabling SDRs to focus on high-impact activities like relationship-building and deal-closing. This automation, often referred to as intelligent pipeline management, identifies prospects, crafts personalized outreach, and schedules meetings—effortlessly managing the early stages of the sales funnel. AI as a Partner: Voice Agents and Real-Time Coaching AI is not just automating tasks; it’s enhancing human performance. AI-powered voice agents can now assist sales reps during live calls by offering real-time coaching. When a prospect raises an objection, the AI provides instant suggestions based on historical data, empowering salespeople to respond more effectively. This real-time guidance helps sales teams navigate complex conversations with confidence, boosting close rates and accelerating results. Personalization at Scale: Tailored Engagement Across Pipelines Personalization has long been a cornerstone of effective sales, but AI has made it scalable. AI tools analyze customer behaviors and preferences, allowing sales teams to tailor messages, proposals, and outreach at an individual level—even for thousands of prospects. From detecting website visits to auto-generating customized content, AI enables hyper-relevant interactions that build stronger connections with leads and customers. Breaking Down Silos: Unifying Sales, Marketing, and Customer Success AI is bridging organizational divides. Historically, sales, marketing, and customer success operated in silos, each pursuing independent goals. AI aligns these functions around a shared understanding of the customer, fostering collaboration and a unified go-to-market strategy. By consolidating data from every customer touchpoint into a single system of record, AI empowers teams to work together seamlessly, ensuring a consistent and coordinated customer experience. Systems of Record for the AI Age: The Importance of Context Unlike traditional CRMs that rely on structured fields, AI-powered systems excel at capturing unstructured data—conversations, social media mentions, and survey responses. These systems provide the context sales teams need to make better decisions. This rich contextual data benefits not just sales but also product development, marketing, and customer success teams, enabling them to refine strategies and create more responsive organizations. Redefining Metrics: From Activities to Achievements Traditional sales metrics often emphasized activity—calls made, emails sent, meetings booked. AI is shifting the focus to outcomes. By tracking the quality and impact of interactions, rather than the volume, sales leaders can better understand what drives success and optimize their strategies accordingly. The Future of Sales: Empowered by AI AI is not replacing salespeople; it’s empowering them. By automating repetitive tasks and delivering actionable insights, AI frees up teams to focus on building relationships, solving problems, and closing deals. To thrive in this new era, organizations must embrace AI as a core part of their strategy. The question for sales leaders is no longer whether to adopt AI but how quickly they can leverage it to gain a competitive edge. Embrace the future of sales—where intelligent systems drive outcomes, and human ingenuity takes center stage. AI is revolutionizing Salesforce by helping businesses improve customer relationships, streamline operations, and make better decisions: 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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November 29, 2024in AI Tools, Artificial Intelligence, Data, Google, Salesforce
26Nov

Salesforce Agents are Transforming Internal Workflows

How Salesforce Agents are Transforming Internal Workflows Salesforce CIO and Executive Vice President Juan Perez, with three decades of IT leadership experience, is leading the charge in deploying generative AI solutions like Agentforce within Salesforce. Perez’s approach reflects lessons learned during his tenure at UPS, where he oversaw IT operations for a global enterprise. His strategies emphasize scalability, data strategy, and modernization to support growth, with AI now playing a pivotal role. UPS Lessons Applied to Salesforce Perez draws on his UPS experience in managing IT at scale to navigate Salesforce’s needs as a growing enterprise. At UPS, he managed a complex, global IT organization supporting diverse operations, from running an airline to ensuring timely package delivery. Similarly, Salesforce’s IT strategy prioritizes scalable solutions, robust data strategies, and AI integration. “Salesforce intelligently realized the importance of leveraging its own technologies, including AI, to modernize and support growth,” Perez explains. Generative AI’s Transformative Potential Perez views generative AI (GenAI) as a transformative force on par with the internet’s emergence in the 1990s. By reducing the time spent on data analysis and decision-making, AI enables teams to focus on actions that improve productivity and customer service. While GenAI isn’t a solution in itself, Perez sees it as an enabler that amplifies human efforts. Evaluating and Integrating AI in Salesforce’s Stack Salesforce adopts a rigorous, multi-step approach to evaluate new technologies, including large language models (LLMs) and generative AI tools. Perez outlines a “filtering mechanism” for implementation: This structured approach ensures AI investments are both impactful and sustainable. Measuring AI’s ROI To quantify the impact of AI, Salesforce evaluates metrics like lines of code generated using AI tools and time saved through automation. In one example, approximately 26% of production-ready code in a recent deployment was AI-generated. This efficiency is factored into planning and budgeting, allowing resources to be reallocated to other initiatives. Mitigating “Shadow AI” Risks Perez warns against “shadow AI,” where decentralized or unmanaged AI implementations can lead to security, data privacy, and investment inefficiencies. He stresses the need for visibility and governance to prevent these risks. To address this, Salesforce has established an AI Council that is evolving into an Agentforce Center of Excellence. This body ensures responsible development, aligns projects with organizational goals, and maintains oversight of AI implementations across the enterprise. Responsible and Scalable AI Adoption Salesforce’s commitment to using its own products extends to Agentforce, a generative AI suite designed to streamline internal workflows. With a focus on governance, scalability, and measurable impact, Salesforce sets a benchmark for AI adoption. As Perez explains, “We ensure our AI solutions are safe, effective, and capable of driving significant value while remaining aligned with our strategic goals.” By combining rigorous evaluation, measurable outcomes, and proactive governance, Salesforce demonstrates how AI can transform workflows while mitigating risks. 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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November 26, 2024in AI Tools, Data, Enterprise, Generative AI, Salesforce, Technology
Tools to Liberate Salesforce Data
25Nov

Salesforce Chose a Human-First Approach to Promote AI

Why Salesforce Chose a Human-First Approach to Promote AI Salesforce won Gold in the Use of GenAI category at The Drum Awards for Advertising by creatively addressing AI-related concerns while demonstrating the power of responsible AI adoption. Here’s a look at the award-winning campaign. Salesforce Chose a Human-First Approach to Promote AI. The Challenge The rapid adoption of AI last year triggered widespread anxiety. Many professionals felt their jobs were at risk, and concerns grew over AI’s trustworthiness, ethical implications, and potential to replace human talent. Businesses needed to address this apprehension while showcasing the transformative potential of AI in a responsible manner. The Strategy Amid the rising uncertainty, Salesforce saw an opportunity to lead the conversation by aligning the campaign with one of its core values: innovation. Rather than positioning AI as an independent solution, Salesforce sought to show that its true power lies in the hands of creative humans who apply it thoughtfully. The campaign aimed to demonstrate that AI isn’t inherently good or bad—it’s a tool, and its impact depends on how it’s used. Salesforce’s creative and production teams integrated generative AI as an assistant, ensuring that AI enhanced human creativity rather than replacing it. This approach positioned Salesforce as a leader in responsible AI adoption, both within the creative industry and across broader business applications. The Campaign Execution Salesforce embraced a “walk the walk” approach to responsible AI by using generative AI tools to assist, not replace, its human creatives. The result was a campaign that resonated deeply with Salesforce’s target audience of business decision-makers, sparking conversations around trust and innovation. The Results The Ask More of AI campaign achieved exceptional outcomes across various metrics: Salesforce Chose a Human-First Approach to Promote AI By adopting a balanced approach—leveraging AI to enhance human creativity without replacing it—Salesforce successfully addressed AI-related fears while positioning itself as a trusted innovator. The campaign not only elevated Salesforce’s brand but also set a benchmark for responsible AI use in marketing. Through “Ask More of AI,” Salesforce demonstrated that trust and purpose are the cornerstones of unlocking AI’s potential. 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 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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November 25, 2024in AI Tools, Generative AI
human centered ai
23Nov

Human-Centered AI

Be the change you want to see in the artificial intelligence world. Or scramble to catch up. Hope Is Not Lost for Human-Centered AIHow designers can lead the charge in creating AI that truly benefits humanity. The rapid proliferation of Artificial Intelligence (AI) brings with it a range of ethical and societal concerns. From inherent biases in datasets to fears of widespread job displacement, these challenges often feel like inevitable trade-offs as AI becomes deeply embedded in our lives. However, hope remains. Human-centered AI—designed to be fair, transparent, and genuinely beneficial—is not only possible but achievable when crafted with intentionality. For UX professionals, this is an opportunity to drive the creation of AI systems that empower rather than overshadow human capabilities. A Quick Note on AI Literacy To make meaningful contributions to AI product development, designers need a foundational understanding of how AI works. While a PhD in machine learning isn’t necessary, being an informed practitioner is essential. Think of learning about AI like learning to invest. At first, it seems daunting—what even is an ETF? But with time, the jargon and processes become familiar. Similarly, while you don’t need to be a machine-learning expert to work with AI, understanding its basics is critical. AI refers broadly to a computer’s ability to mimic human thought, while machine learning (ML)—a subset of AI—enables systems to learn from data. Unlike traditional programming, where explicit instructions are coded line by line, ML models identify patterns within training datasets. These models then function as “black boxes,” generating outputs based on user inputs—though the inner workings are often opaque. Understanding these fundamentals empowers designers to bridge the gap between AI’s technical potential and its real-world application. Design-Led AI Ideally, designers are involved from the very beginning of AI product development—during the discovery phase. Here, we evaluate whether AI is the right solution for a given problem, ensuring user needs drive decisions rather than the allure of flashy tech. Key questions to ground AI solutions in user needs include: Basic AI literacy allows designers to make informed judgments and collaborate effectively with engineers. Engaging early ensures that AI solutions are designed to adapt to users—not the other way around. But what happens when design isn’t brought in until after AI decisions have been made? Design-Guarded AI Even when AI is a foregone conclusion, designers can still shape outcomes by focusing on the two areas where users interact directly with AI: inputs and outputs. Input Design Whether inputs involve transaction data, images, or text prompts, the method of collection must be intuitive and user-friendly. Established design principles, such as affordances, help ensure clarity and simplicity. For example: Frequent user testing ensures input methods align with real workflows and pain points. The result? Streamlined, user-centric experiences that reduce friction and save time. Output Design Designing outputs requires a focus on transparency and mitigating automation bias—the tendency to over-rely on AI. Users must understand that AI is fallible. For instance: AI should act as a collaborator, not an authority. Outputs must empower users to make informed choices while supporting their next steps within a seamless workflow. Ethics Must Take Center Stage No discussion of human-centered AI is complete without addressing ethics. Designers must champion transparency, inclusivity, and fairness throughout the product lifecycle. Questions around bias, privacy, and unintended consequences must be raised early and revisited often. While ethical considerations may sometimes conflict with short-term business goals, prioritizing them is essential for building AI that serves humanity in the long term. These conversations won’t always be easy—but they are necessary. As designers, we have the tools and responsibility to ensure AI remains a force for good. By advocating for human-centered design principles, we can help shape an AI-powered future that enhances human potential rather than undermining it. 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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November 23, 2024in Artificial Intelligence, Data, Generative AI
Value-Based Care Technologies
22Nov

Value-Based Care Technologies

Essential Technologies for Value-Based Care Success As healthcare providers increasingly adopt value-based care, they must invest in the right technologies and resources to succeed in this model, which incentivizes high-quality, cost-effective care. Value-Based Care Technologies tie reimbursement to care quality, making providers accountable for patient outcomes while providing resources to enhance care. As of 2021, nearly 60% of healthcare payments were already tied to value-based models, according to the Health Care Payment Learning and Action Network (HCP LAN). While partnerships can initiate value-based care, providers must invest in the right technology to fully achieve the intended outcomes. Health Information Exchange (HIE) A robust health information exchange (HIE) is fundamental to value-based care, as it enables providers and payers to access high-quality data seamlessly. HIE allows healthcare professionals to share patients’ medical information electronically across organizations, promoting care coordination by giving providers a comprehensive view of patient needs. For patients, HIE enables more informed involvement in their care by making their health data accessible across specialists, labs, and pharmacies. While joining an HIE may involve new technology investments and workflow adjustments, it ultimately enhances provider access to critical health data. Population Health Management Tools Population health management tools help providers assess health outcomes within groups rather than focusing on individuals alone. These tools aggregate and analyze data, allowing practices to identify high-risk patients and create targeted interventions. This not only enhances health outcomes but can also reduce costs by avoiding expensive treatments. Patient engagement tools, such as telehealth and remote patient monitoring, are essential in population health management, especially for monitoring high-risk patients when in-person care is not feasible. Digital surveys integrated within patient portals can provide insights into social determinants of health, adding a broader context to patient needs. Data Analytics Data analytics transform healthcare data into actionable insights across four types: descriptive, diagnostic, predictive, and prescriptive. Providers can use these analytics to reduce hospital readmissions, predict diseases, and identify chronic illnesses. Data integration and risk stratification capabilities are especially valuable in value-based care, enabling providers to track patient health outcomes effectively and prioritize high-risk cases. Artificial Intelligence & Machine Learning AI and machine learning support many data analytics functions, helping identify patient needs and easing administrative burdens. Given staffing shortages and burnout—reported by 63% of physicians in 2021, according to the American Medical Association (AMA)—AI can automate tasks like documentation, charting, and scheduling, allowing providers to focus more on patient care. Additionally, AI-driven automation in revenue cycle management tasks, such as billing and coding, can reduce the administrative workload associated with value-based care. Price Transparency Technology Price transparency empowers patients to seek cost-effective care, a core principle of value-based models. When providers comply with transparency regulations, patients can better understand their costs and make informed decisions. For providers, leveraging price transparency tools ensures compliance and facilitates partnerships with payers by enabling more effective negotiation, which supports the overall goals of value-based care. As healthcare continues shifting to value-based models, investing in these technologies is critical for providers aiming for long-term success. While these tools rdo equire substantial investment, they are essential for improving patient outcomes, optimizing care quality, and ensuring sustainability in value-based care. When evaluating and choosing healthcare technology tools, contact Tectonic for help. 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 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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November 22, 2024in AI Tools, Artificial Intelligence, Data, Life Sciences Cloud, Salesforce, Salesforce Development Services, Salesforce Foundations, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Salesforce Implementation Services, Salesforce Optimization, Technology
AI Inference vs. Training
21Nov

AI Inference vs. Training

AI Inference vs. Training: Key Differences and Tradeoffs AI training and inference are the foundational phases of machine learning, each with distinct objectives and resource demands. Optimizing the balance between the two is crucial for managing costs, scaling models, and ensuring peak performance. Here’s a closer look at their roles, differences, and the tradeoffs involved. Understanding Training and Inference Key Differences Between Training and Inference 1. Compute Costs 2. Resource and Latency Considerations Strategic Tradeoffs Between Training and Inference Key Considerations for Balancing Training and Inference As AI technology evolves, hardware advancements may narrow the gap in resource requirements between training and inference. Nonetheless, the key to effective machine learning systems lies in strategically balancing the demands of both processes to meet specific goals and constraints. 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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November 21, 2024in AI Tools, Data, Generative AI, Technology
AI Productivity Paradox
20Nov

AI Productivity Paradox

The AI Productivity Paradox: Why Aren’t More Workers Using AI Tooks Like ChatGPT?The Real Barrier Isn’t Technical Skills — It’s Time to Think Despite the transformative potential of tools like ChatGPT, most knowledge workers aren’t utilizing them effectively. Those who do tend to use them for basic tasks like summarization. Less than 5% of ChatGPT’s user base subscribes to the paid Plus version, indicating that a small fraction of potential professional users are tapping into AI for more complex, high-value tasks. Having spent over a decade building AI products at companies such as Google Brain and Shopify Ads, the evolution of AI has been clearly evident. With the advent of ChatGPT, AI has transitioned from being an enhancement for tools like photo organizers to becoming a significant productivity booster for all knowledge workers. Most executives are aware that today’s buzz around AI is more than just hype. They’re eager to make their companies AI-forward, recognizing that it’s now more powerful and user-friendly than ever. Yet, despite this potential and enthusiasm, widespread adoption remains slow. The real issue lies in how organizations approach work itself. Systemic problems are hindering the integration of these tools into the daily workflow. Ultimately, the question executives need to ask isn’t, “How can we use AI to work faster? Or can this feature be built with AI?” but rather, “How can we use AI to create more value? What are the questions we should be asking but aren’t?” Real-world ImpactRecently, large language models (LLMs)—the technology behind tools like ChatGPT—were used to tackle a complex data structuring and analysis task. This task would typically require a cross-functional team of data analysts and content designers, taking a month or more to complete. Here’s what was accomplished in just one day using Google AI Studio: However, the process wasn’t just about pressing a button and letting AI do all the work. It required focused effort, detailed instructions, and multiple iterations. Hours were spent crafting precise prompts, providing feedback, and redirecting the AI when it went off course. In this case, the task was compressed from a month-long process to a single day. While it was mentally exhausting, the result wasn’t just a faster process—it was a fundamentally better and different outcome. The LLMs uncovered nuanced patterns and edge cases within the data that traditional analysis would have missed. The Counterintuitive TruthHere lies the key to understanding the AI productivity paradox: The success in using AI was possible because leadership allowed for a full day dedicated to rethinking data processes with AI as a thought partner. This provided the space for deep, strategic thinking, exploring connections and possibilities that would typically take weeks. However, this quality-focused work is often sacrificed under the pressure to meet deadlines. Ironically, most people don’t have time to figure out how they could save time. This lack of dedicated time for exploration is a luxury many product managers (PMs) can’t afford. Under constant pressure to deliver immediate results, many PMs don’t have even an hour for strategic thinking. For many, the only way to carve out time for this work is by pretending to be sick. This continuous pressure also hinders AI adoption. Developing thorough testing plans or proactively addressing AI-related issues is viewed as a luxury, not a necessity. This creates a counterproductive dynamic: Why use AI to spot issues in documentation if fixing them would delay launch? Why conduct further user research when the direction has already been set from above? Charting a New Course — Investing in PeopleProviding employees time to “figure out AI” isn’t enough; most need training to fully understand how to leverage ChatGPT beyond simple tasks like summarization. Yet the training required is often far less than what people expect. While the market is flooded with AI training programs, many aren’t suitable for most employees. These programs are often time-consuming, overly technical, and not tailored to specific job functions. The best results come from working closely with individuals for brief periods—10 to 15 minutes—to audit their current workflows and identify areas where LLMs could be used to streamline processes. Understanding the technical details behind token prediction isn’t necessary to create effective prompts. It’s also a myth that AI adoption is only for those with technical backgrounds under 40. In fact, attention to detail and a passion for quality work are far better indicators of success. By setting aside biases, companies may discover hidden AI enthusiasts within their ranks. For example, a lawyer in his sixties, after just five minutes of explanation, grasped the potential of LLMs. By tailoring examples to his domain, the technology helped him draft a law review article he had been putting off for months. It’s likely that many companies already have AI enthusiasts—individuals who’ve taken the initiative to explore LLMs in their work. These “LLM whisperers” could come from any department: engineering, marketing, data science, product management, or customer service. By identifying these internal innovators, organizations can leverage their expertise. Once these experts are found, they can conduct “AI audits” of current workflows, identify areas for improvement, and provide starter prompts for specific use cases. These internal experts often better understand the company’s systems and goals, making them more capable of spotting relevant opportunities. Ensuring Time for ExplorationBeyond providing training, it’s crucial that employees have the time to explore and experiment with AI tools. Companies can’t simply tell their employees to innovate with AI while demanding that another month’s worth of features be delivered by Friday at 5 p.m. Ensuring teams have a few hours a month for exploration is essential for fostering true AI adoption. Once the initial hurdle of adoption is overcome, employees will be able to identify the most promising areas for AI investment. From there, organizations will be better positioned to assess the need for more specialized training. ConclusionThe AI productivity paradox is not about the complexity of the technology but rather how organizations approach work and innovation. Harnessing AI’s potential is simpler than “AI influencers” often suggest, requiring only

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November 20, 2024in AI Tools, Data, Einstein 1 Platform, Generative AI, Google, Technology
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