Generative AI Archives - gettectonic.com - Page 7
New Service Cloud Tools
23Dec

Service Cloud for HR

Salesforce has expanded its Service Cloud capabilities to include a new HR-focused solution, Employee Service, designed to streamline employee support and enhance productivity. Employee Service introduces a dedicated HR service console paired with an employee portal. This portal acts as a centralized hub for staff to access HR resources, offering instant answers via Generative AI (GenAI), direct communication with HR specialists across multiple channels, and self-service options for tasks like requesting paid time off (PTO). For HR teams, the service console consolidates employee data, case details, and a company’s knowledge base into a unified workspace. It leverages AI-driven tools to resolve cases faster, automate routine tasks, and deliver seamless employee experiences. Salesforce’s Agentforce customers can integrate AI agents into Employee Service to further automate processes, saving time and reducing repetitive workloads. In a LinkedIn announcement, Kishan Chetan, EVP and GM for Service Cloud, highlighted the solution’s potential: “This new solution unifies employee data, case details, and a company’s corporate knowledge base all in one workspace that gives HR teams a 360-degree view of each employee and the ability to manage employee support cases with built-in AI and productivity tools. HR teams can efficiently resolve employee issues using Agentforce to quickly search, respond, summarize, and close cases, extending teams to get work done faster.” Salesforce’s broader goal is to eliminate the reliance on fragmented HR tools and reduce the need for employees to navigate disparate platforms like email, internal systems, and collaboration tools to complete HR-related tasks. By doing so, Salesforce aims to simplify HR processes, minimize manual effort, and enhance overall productivity. Early adopters of Employee Service are already reporting significant results. According to Sherin Sunny, Sr. Director of Product Management at Salesforce, customers have observed a 31% increase in employee productivity. This aligns with broader trends: Recognizing the need for a unified HR ecosystem, Salesforce includes a prebuilt MuleSoft integration with Workday and configurable connectors to other Human Capital Management (HCM) systems. These integrations establish a centralized HR data foundation, reducing inefficiencies caused by siloed tools. Looking ahead, Beth Schultz, VP of Research & Principal Analyst at Metrigy, emphasized the importance of integrating Employee Service with Slack, Salesforce’s collaboration platform: “We’ll be particularly watching how Salesforce’s multifaceted plans for bringing [Employee Service] into Slack play out as Slack evolves into a fully connected, collaborative workspace.” Slack itself is undergoing a transformation, with Salesforce Co-Founder Patrick Harris returning to revamp the platform as a core part of the Salesforce ecosystem. Meanwhile, Salesforce continues to expand Service Cloud’s offerings beyond Employee Service. Recent developments include a revamped CCaaS (Contact Center as a Service) integration program and a new product discovery tool. Still, Agentforce remains a key focus for Salesforce’s marketing efforts, showcasing its potential to redefine how businesses deploy autonomous AI agents across use cases like HR and beyond. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 23, 2024in Data, Generative AI, Mulesoft, Salesforce
Standards in Healthcare Cybersecurity
21Dec

Deploying Large Language Models in Healthcare

Study Identifies Cost-Effective Strategies for Deploying Large Language Models in Healthcare Efficient deployment of large language models (LLMs) at scale in healthcare can streamline clinical workflows and reduce costs by up to 17 times without compromising reliability, according to a study published in NPJ Digital Medicine by researchers at the Icahn School of Medicine at Mount Sinai. The research highlights the potential of LLMs to enhance clinical operations while addressing the financial and computational hurdles healthcare organizations face in scaling these technologies. To investigate solutions, the team evaluated 10 LLMs of varying sizes and capacities using real-world patient data. The models were tested on chained queries and increasingly complex clinical notes, with outputs assessed for accuracy, formatting quality, and adherence to clinical instructions. “Our study was driven by the need to identify practical ways to cut costs while maintaining performance, enabling health systems to confidently adopt LLMs at scale,” said Dr. Eyal Klang, director of the Generative AI Research Program at Icahn Mount Sinai. “We aimed to stress-test these models, evaluating their ability to manage multiple tasks simultaneously and identifying strategies to balance performance and affordability.” The team conducted over 300,000 experiments, finding that high-capacity models like Meta’s Llama-3-70B and GPT-4 Turbo 128k performed best, maintaining high accuracy and low failure rates. However, performance began to degrade as task volume and complexity increased, particularly beyond 50 tasks involving large prompts. The study further revealed that grouping tasks—such as identifying patients for preventive screenings, analyzing medication safety, and matching patients for clinical trials—enabled LLMs to handle up to 50 simultaneous tasks without significant accuracy loss. This strategy also led to dramatic cost savings, with API costs reduced by up to 17-fold, offering a pathway for health systems to save millions annually. “Understanding where these models reach their cognitive limits is critical for ensuring reliability and operational stability,” said Dr. Girish N. Nadkarni, co-senior author and director of The Charles Bronfman Institute of Personalized Medicine. “Our findings pave the way for the integration of generative AI in hospitals while accounting for real-world constraints.” Beyond cost efficiency, the study underscores the potential of LLMs to automate key tasks, conserve resources, and free up healthcare providers to focus more on patient care. “This research highlights how AI can transform healthcare operations. Grouping tasks not only cuts costs but also optimizes resources that can be redirected toward improving patient outcomes,” said Dr. David L. Reich, co-author and chief clinical officer of the Mount Sinai Health System. The research team plans to explore how LLMs perform in live clinical environments and assess emerging models to determine whether advancements in AI technology can expand their cognitive thresholds. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 21, 2024in Data, Generative AI, Technology
AI Agents Set to Break Through in 2025
20Dec

AI Agents Set to Break Through in 2025

2025: The Year AI Agents Transform Work and Life Despite years of hype around artificial intelligence, its true disruptive impact has so far been limited. However, industry experts believe that’s about to change in 2025 as autonomous AI agents prepare to enter and reshape nearly every facet of our lives. Since OpenAI’s ChatGPT took the world by storm in late 2022, billions of dollars have been funneled into the AI sector. Big tech and startups alike are racing to harness the transformative potential of the technology. Yet, while millions now interact with AI chatbots daily, turning them into tools that deliver tangible business value has proven challenging. A recent study by Boston Consulting Group revealed that only 26% of companies experimenting with AI have progressed beyond proof of concept to derive measurable value. This lag reflects the limitations of current AI tools, which serve primarily as copilots—capable of assisting but requiring constant oversight and remaining prone to errors. AI Agents Set to Break Through in 2025 The status quo, however, is poised for a radical shift. Autonomous AI agents—capable of independently analyzing information, making decisions, and taking action—are expected to emerge as the industry’s next big breakthrough. “For the first time, technology isn’t just offering tools for humans to do work,” Salesforce CEO Marc Benioff wrote in Time. “It’s providing intelligent, scalable digital labor that performs tasks autonomously. Instead of waiting for human input, agents can analyze information, make decisions, and adapt as they go.” At their core, AI agents leverage the same large language models (LLMs) that power tools like ChatGPT. But these agents take it further, acting as reasoning engines that develop step-by-step strategies to execute tasks. Armed with access to external data sources like customer records or financial databases and equipped with software tools, agents can achieve goals independently. While current LLMs still face reasoning limitations, advancements are on the horizon. New models like OpenAI’s “o1” and DeepSeek’s “R1” are specialized for reasoning, sparking hope that 2025 will see agents grow far more capable. Big Tech and Startups Betting Big Major players are already gearing up for this new era. Startups are also eager to carve out their share of the market. According to Pitchbook, funding deals for agent-focused ventures surged by over 80% in 2024, with the median deal value increasing nearly 50%. Challenges to Overcome Despite the enthusiasm, significant hurdles remain. 2025: A Turning Point Despite these challenges, many experts believe 2025 will mark the mainstream adoption of AI agents. A New World of Work No matter the pace, it’s clear that AI agents will dominate the industry’s focus in 2025. If the technology delivers on its promise, the workplace could undergo a profound transformation, enabling entirely new ways of working and automating tasks that once required human intervention. The question isn’t if agents will redefine the way we work—it’s how fast. By the end of 2025, the shift could be undeniable. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 20, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Google, Salesforce, Technology
Meta Joins the Race to Reinvent Search with AI
18Dec

Meta Joins the Race to Reinvent Search with AI

Meta Joins the Race to Reinvent Search with AI Meta, the parent company of Facebook, Instagram, and WhatsApp, is stepping into the evolving AI-driven search landscape. As vendors increasingly embrace generative AI to transform search experiences, Meta aims to challenge Google’s dominance in this space. The company is reportedly developing an AI-powered search engine designed to provide conversational, AI-generated summaries of recent events and news. These summaries would be delivered via Meta’s AI chatbot, supported by a multiyear partnership with Reuters for real-time news insights, according to The Information. AI Search: A Growing Opportunity The push comes as generative AI reshapes search technology across the industry. Google, the long-standing leader, has integrated AI features such as AI Overviews into its search platform, offering users summarized search results, product comparisons, and more. This feature, now available in over 100 countries as of October 2024, signals a shift in traditional search strategies. Similarly, OpenAI, the creator of ChatGPT, has been exploring its own AI search model, SearchGPT, and forging partnerships with media organizations like the Associated Press and Hearst. However, OpenAI faces legal challenges, such as a lawsuit from The New York Times over alleged copyright infringement. Meta’s entry into AI-powered search aligns with a broader trend among tech giants. “It makes sense for Meta to explore this,” said Mark Beccue, an analyst with TechTarget’s Enterprise Strategy Group. He noted that Meta’s approach seems more targeted at consumer engagement than enterprise solutions, particularly appealing to younger audiences who are shifting away from traditional search behaviors. Shifting User Preferences Generational changes in search habits are creating opportunities for new players in the market. Younger users, particularly Gen Z and Gen Alpha, are increasingly turning to platforms like TikTok for lifestyle advice and Amazon for product recommendations, bypassing traditional search engines like Google. “Recent studies show younger generations are no longer using ‘Google’ as a verb,” said Lisa Martin, an analyst with the Futurum Group. “This opens the playing field for competitors like Meta and OpenAI.” Forrester Research corroborates this trend, noting a diversification in search behaviors. “ChatGPT’s popularity has accelerated this shift,” said Nikhil Lai, a Forrester analyst. He added that these changes could challenge Google’s search ad market, with its dominance potentially waning in the years ahead. Meta’s AI Search Potential Meta’s foray into AI search offers an opportunity to enhance user experiences and deepen engagement. Rather than pushing news content into users’ feeds—an approach that has drawn criticism—AI-driven search could empower users to decide what content they see and when they see it. “If implemented thoughtfully, it could transform the user experience and give users more control,” said Martin. This approach could also boost engagement by keeping users within Meta’s ecosystem. The Race for Revenue and Trust While AI-powered search is expected to increase engagement, monetization strategies remain uncertain. Google has yet to monetize its AI Overviews, and OpenAI’s plans for SearchGPT remain unclear. Other vendors, like Perplexity AI, are experimenting with models such as sponsored questions instead of traditional results. Trust remains a critical factor in the evolving search landscape. “Google is still seen as more trustworthy,” Lai noted, with users often returning to Google to verify AI-generated information. Despite the competition, the conversational AI search market lacks a definitive leader. “Google dominated traditional search, but the race for conversational search is far more open-ended,” Lai concluded. Meta’s entry into this competitive space underscores the ongoing evolution of search technology, setting the stage for a reshaped digital landscape driven by AI innovation. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 18, 2024in Enterprise, Generative AI, Google, Laws, Salesforce, Technology
Artificial Intelligence (AI) is significantly transforming threat detection by enabling faster, more accurate identification of potential security breaches through its ability to analyze vast amounts of data in real-time, detect anomalies and patterns that might indicate a threat, even when those threats are new or previously unknown, thus providing a proactive approach to cybersecurity compared to traditional rule-based systems.
16Dec

AI is Transforming Threat Detection

Artificial Intelligence (AI) is significantly transforming threat detection by enabling faster, more accurate identification of potential security breaches through its ability to analyze vast amounts of data in real-time, detect anomalies and patterns that might indicate a threat, even when those threats are new or previously unknown, thus providing a proactive approach to cybersecurity compared to traditional rule-based systems.

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December 16, 2024in AI Tools, Data, Enterprise, Generative AI, Salesforce, Technology
Transforming the Role of Data Science Teams
15Dec

Transforming the Role of Data Science Teams

GenAI: Transforming the Role of Data Science Teams Challenges, Opportunities, and the Evolving Responsibilities of Data Scientists Generative AI (GenAI) is revolutionizing the AI landscape, offering faster development cycles, reduced technical overhead, and enabling groundbreaking use cases that once seemed unattainable. However, it also introduces new challenges, including the risks of hallucinations and reliance on third-party APIs. For Data Scientists and Machine Learning (ML) teams, this shift directly impacts their roles. GenAI-driven projects, often powered by external providers like OpenAI, Anthropic, or Meta, blur traditional lines. AI solutions are increasingly accessible to non-technical teams, but this accessibility raises fundamental questions about the role and responsibilities of data science teams in ensuring effective, ethical, and future-proof AI systems. Let’s explore how this evolution is reshaping the field. Expanding Possibilities Without Losing Focus While GenAI unlocks opportunities to solve a broader range of challenges, not every problem warrants an AI solution. Data Scientists remain vital in assessing when and where AI is appropriate, selecting the right approaches—whether GenAI, traditional ML, or hybrid solutions—and designing reliable systems. Although GenAI broadens the toolkit, two factors shape its application: For example, incorporating features that enable user oversight of AI outputs may prove more strategic than attempting full automation with extensive fine-tuning. Differentiation will not come from simply using LLMs, which are widely accessible, but from the unique value and functionality they enable. Traditional ML Is Far from Dead—It’s Evolving with GenAI While GenAI is transformative, traditional ML continues to play a critical role. Many use cases, especially those unrelated to text or images, are best addressed with ML. GenAI often complements traditional ML, enabling faster prototyping, enhanced experimentation, and hybrid systems that blend the strengths of both approaches. For instance, traditional ML workflows—requiring extensive data preparation, training, and maintenance—contrast with GenAI’s simplified process: prompt engineering, offline evaluation, and API integration. This allows rapid proof of concept for new ideas. Once proven, teams can refine solutions using traditional ML to optimize costs or latency, or transition to Small Language Models (SMLs) for greater control and performance. Hybrid systems are increasingly common. For example, DoorDash combines LLMs with ML models for product classification. LLMs handle cases the ML model cannot classify confidently, retraining the ML system with new insights—a powerful feedback loop. GenAI Solves New Problems—But Still Needs Expertise The AI landscape is shifting from bespoke in-house models to fewer, large multi-task models provided by external vendors. While this simplifies some aspects of AI implementation, it requires teams to remain vigilant about GenAI’s probabilistic nature and inherent risks. Key challenges unique to GenAI include: Data Scientists must ensure robust evaluations, including statistical and model-based metrics, before deployment. Monitoring tools like Datadog now offer LLM-specific observability, enabling teams to track system performance in real-world environments. Teams must also address ethical concerns, applying frameworks like ComplAI to benchmark models and incorporating guardrails to align outputs with organizational and societal values. Building AI Literacy Across Organizations AI literacy is becoming a critical competency for organizations. Beyond technical implementation, competitive advantage now depends on how effectively the entire workforce understands and leverages AI. Data Scientists are uniquely positioned to champion this literacy by leading initiatives such as internal training, workshops, and hackathons. These efforts can: The New Role of Data Scientists: A Strategic Pivot The role of Data Scientists is not diminishing but evolving. Their expertise remains essential to ensure AI solutions are reliable, ethical, and impactful. Key responsibilities now include: By adapting to this new landscape, Data Scientists will continue to play a pivotal role in guiding organizations to harness AI effectively and responsibly. GenAI is not replacing them; it’s expanding their impact. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 15, 2024in Data, Generative AI
Generative AU and the Future of UCD
13Dec

Generative AU and the Future of UCD

Generative AI and the Future of UCD: Adapting to New Challenges Discussions about generative AI seem endless—and while the topic may feel saturated, revisiting it in the context of user-centered design (UCD) and service delivery reveals critical opportunities and challenges worth exploring. The Current Landscape of Generative AI Generative AI is being increasingly evaluated for its potential to enhance research and public services. At the Ministry of Justice, for example, teams are exploring how generative AI can streamline user journeys, reduce duplication, and improve access to information—key pillars of effective service design. While enthusiasm and investment in generative AI are high, the reality is more cautious. Most projects remain in the proof-of-concept phase, and feedback often reflects attitudes rather than real-world behaviors. Public trust in AI is low, and many people lack an understanding of how it works or how they might interact with it. In government and public services, unresolved questions about risk tolerance, error management, and human oversight signal that AI integration is still in its early stages. Instead of declaring generative AI as the solution to user problems—or worrying about AI replacing jobs—it’s more productive to focus on adapting UCD practices to harness AI responsibly and effectively. The Risk of ‘Solutionizing’ in UCD Generative AI introduces a familiar challenge for UCD professionals: the risk of “solutionizing.” Many projects prioritize developing AI solutions, even before confirming they meet user needs. While experimentation is vital for exploring AI’s potential, there’s a danger in stakeholders prematurely assuming these proofs-of-concept validate AI as the ultimate solution. This underscores the enduring importance of UCD in the “age of AI.” UCD professionals must ensure that user needs remain central, educating stakeholders not just about AI’s capabilities but also about why user-centered design leads to better outcomes. To achieve this, UCD teams must prioritize ongoing user research and create opportunities for solution-agnostic ideation. Avoiding the “innovation trap”—assuming that the newest technologies inherently produce the best outcomes—requires openly acknowledging biases and finding creative ways to test assumptions. By doing so, decision-making becomes more transparent and adaptable, enabling cost-effective course corrections when needed. How UCD Will Evolve While the foundations of UCD will remain intact, generative AI will require adjustments to specific practices. For example, traditional usability testing might not fully address the variability of AI responses, which can differ even for identical user inputs. This unpredictability challenges conventional testing methods and demands new approaches. Collaboration between UCD teams, data scientists, and AI developers will be essential. By working closely, these teams can better understand how generative AI interacts with users, ensuring its capabilities are leveraged effectively. Rethinking Design Thinking Generative AI might also shift how design thinking is applied within UCD. The traditional double diamond model emphasizes deep discovery and iterative solution exploration. However, when incorporating generative AI, it may be beneficial to experiment with AI’s capabilities earlier in the discovery phase, blending user problem exploration with rapid technical experimentation. This approach would require guardrails to ensure user needs remain the priority, but it could lead to more innovative and practical solutions by aligning technical feasibility with user-centered insights from the outset. Conclusion Generative AI isn’t ready to replace jobs, but it does demand that UCD professionals evolve their practices. By adapting methods, increasing AI literacy, and holding innovation accountable to user needs, UCD teams can ensure that generative AI enhances, rather than detracts from, effective service design. How do you see UCD adapting to the challenges and opportunities of generative AI? What other considerations should we anticipate? Let’s continue the conversation! Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 13, 2024in Data, Generative AI
google agentspace
13Dec

Google Agentspace

Google Agentspace: Boosting Productivity with AI-Powered Agents Google has unveiled Agentspace, a cutting-edge tool designed to revolutionize workplace productivity by combining the power of AI agents, Google Gemini 2.0, and its advanced search capabilities. This tool aims to streamline workflows, enhance information discovery, and empower enterprises to unlock the full potential of their data. What is Google Agentspace? Google Agentspace is an enterprise-focused productivity platform that simplifies complex tasks involving planning, research, and content generation. By integrating AI-powered tools like NotebookLM Plus, it enables employees to uncover insights, interact with unstructured and structured data, and make informed decisions—all in one centralized platform. Key features include: Core Benefits of Google Agentspace 1. Streamlined Information Discovery Employees often waste hours sifting through fragmented data in emails, documents, and spreadsheets. Agentspace serves as a centralized knowledge hub, offering conversational assistance, proactive suggestions, and actionable insights from both unstructured and structured data sources. With pre-built connectors for tools like Google Drive, Jira, Microsoft SharePoint, and ServiceNow, Agentspace ensures seamless integration with existing systems, providing employees with relevant information faster. 2. Enhanced Multimodal Capabilities Agentspace leverages Google’s search expertise and Gemini 2.0 to provide advanced reasoning capabilities. Employees can query in multiple formats (text, audio, video), translate information into different languages, and generate audio summaries, enhancing productivity and accessibility. 3. Task Automation Across Departments Agentspace empowers teams across various functions to automate repetitive tasks, such as: 4. Scalable AI for Enterprises Agentspace offers a low-code visual tool for creating custom AI agents tailored to specific business needs. These agents can automate multi-step workflows, conduct in-depth research, and assist with data-driven content generation, enabling enterprises to scale AI adoption effortlessly. Security and Responsible AI Google Agentspace is built on Google Cloud’s secure-by-design infrastructure, ensuring that enterprises can deploy AI tools with confidence. Key Security Features Google is also addressing responsible AI concerns with tools for evaluation, content moderation, and bias mitigation, ensuring ethical and explainable AI use in the workplace. Use Cases Google Agentspace provides solutions tailored to various enterprise needs: Challenges and Future Directions Despite its potential, Agentspace faces hurdles such as employee training and adoption. Organizations must ensure that employees understand how to incorporate the tool into their daily workflows effectively. Moreover, Google’s approach to responsible AI will be closely scrutinized. Addressing issues like explainability, bias prevention, and robust data infrastructure will be crucial for building trust and driving adoption. Early Access and the Road Ahead Google is offering early access to Agentspace, allowing enterprises to explore its potential and provide feedback. As AI continues to reshape the workplace, tools like Agentspace position Google as a leader in productivity-enhancing solutions for businesses. For enterprises looking to harness AI to unlock creativity, improve decision-making, and automate workflows, Agentspace is the next step in digital transformation. Sign up for early access today to bring the future of work to your organization. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 13, 2024in AI Tools, Data, Enterprise, Generative AI, Google
Transforming Fundraising for Nonprofits
11Dec

Leverage AI to Enhance Customer Retention

Leverage AI to Enhance Customer Retention and Reduce Churn Customer churn is among the most expensive challenges businesses face—and one of the hardest to tackle. Predictive and generative AI technologies offer an immediate opportunity to boost retention rates. When applied strategically, these tools can revolutionize how customer service and support teams operate, creating measurable improvements in retention and overall customer satisfaction. A recent McKinsey & Company study highlights the impact of AI in customer service. One company reported a 14% increase in issue resolution and a 9% reduction in issue handling time with generative AI. Requests to escalate to a manager dropped by 25%, and employee retention in service roles improved. When every percentage point matters, AI’s ability to engage and retain customers (and employees) can significantly affect your bottom line and business success. The Cost of Poor Customer Service on Retention Retaining existing customers is far more cost-effective than acquiring new ones. Happy, long-term customers are also more likely to purchase additional products or services, making upselling and cross-selling efforts easier. However, poor customer service experiences—such as lengthy hold times, repeating information, or unhelpful chatbot interactions—can damage customer relationships and lead to churn. As Salesforce points out, these four signs indicate broken customer service: To address these challenges, a seamless, data-driven approach to customer service is essential. Prevent Churn with CRM + AI Customer data spans multiple touchpoints, from website visits to call center interactions. Without a unified view, even the most skilled service teams struggle to deliver exceptional experiences. A solution like Salesforce Service Cloud, enhanced by AI tools such as Agentforce Service Agents, empowers teams to: By combining predictive analytics with a unified customer experience platform, businesses can deliver personalized, proactive service that fosters loyalty. Retention Agent: The AI Solution for Customer Retention Retention Agent, part of Tectonic’s Agentforce suite, leverages AI to identify at-risk customers and equip sales, service, and support teams with actionable insights. It provides recommendations for re-engagement strategies, personalized offers, and targeted communications to prevent costly churn. Here’s how Retention Agent works: By integrating AI into customer service operations, businesses can stay ahead of churn, improve satisfaction, and build stronger, longer-lasting customer relationships. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 11, 2024in AI Tools, Data, Generative AI, Salesforce, Salesforce Service Cloud

Empowering LLMs with a Robust Agent Framework

PydanticAI: Empowering LLMs with a Robust Agent Framework As the Generative AI landscape evolves at a historic pace, AI agents and multi-agent systems are expected to dominate 2025. Industry leaders like AWS, OpenAI, and Microsoft are racing to release frameworks, but among these, PydanticAI stands out for its unique integration of the powerful Pydantic library with large language models (LLMs). Why Pydantic Matters Pydantic, a Python library, simplifies data validation and parsing, making it indispensable for handling external inputs such as JSON, user data, or API responses. By automating data checks (e.g., type validation and format enforcement), Pydantic ensures data integrity while reducing errors and development effort. For instance, instead of manually validating fields like age or email, Pydantic allows you to define models that automatically enforce structure and constraints. Consider the following example: pythonCopy codefrom pydantic import BaseModel, EmailStr class User(BaseModel): name: str age: int email: EmailStr user_data = {“name”: “Alice”, “age”: 25, “email”: “[email protected]”} user = User(**user_data) print(user.name) # Alice print(user.age) # 25 print(user.email) # [email protected] If invalid data is provided (e.g., age as a string), Pydantic throws a detailed error, making debugging straightforward. What Makes PydanticAI Special Building on Pydantic’s strengths, PydanticAI brings structured, type-safe responses to LLM-based AI agents. Here are its standout features: Building an AI Agent with PydanticAI Below is an example of creating a PydanticAI-powered bank support agent. The agent interacts with customer data, evaluates risks, and provides structured advice. Installation bashCopy codepip install ‘pydantic-ai-slim[openai,vertexai,logfire]’ Example: Bank Support Agent pythonCopy codefrom dataclasses import dataclass from pydantic import BaseModel, Field from pydantic_ai import Agent, RunContext from bank_database import DatabaseConn @dataclass class SupportDependencies: customer_id: int db: DatabaseConn class SupportResult(BaseModel): support_advice: str = Field(description=”Advice for the customer”) block_card: bool = Field(description=”Whether to block the customer’s card”) risk: int = Field(description=”Risk level of the query”, ge=0, le=10) support_agent = Agent( ‘openai:gpt-4o’, deps_type=SupportDependencies, result_type=SupportResult, system_prompt=( “You are a support agent in our bank. Provide support to customers and assess risk levels.” ), ) @support_agent.system_prompt async def add_customer_name(ctx: RunContext[SupportDependencies]) -> str: customer_name = await ctx.deps.db.customer_name(id=ctx.deps.customer_id) return f”The customer’s name is {customer_name!r}” @support_agent.tool async def customer_balance(ctx: RunContext[SupportDependencies], include_pending: bool) -> float: return await ctx.deps.db.customer_balance( id=ctx.deps.customer_id, include_pending=include_pending ) async def main(): deps = SupportDependencies(customer_id=123, db=DatabaseConn()) result = await support_agent.run(‘What is my balance?’, deps=deps) print(result.data) result = await support_agent.run(‘I just lost my card!’, deps=deps) print(result.data) Key Concepts Why PydanticAI Matters PydanticAI simplifies the development of production-ready AI agents by bridging the gap between unstructured LLM outputs and structured, validated data. Its ability to handle complex workflows with type safety and its seamless integration with modern AI tools make it an essential framework for developers. As we move toward a future dominated by multi-agent AI systems, PydanticAI is poised to be a cornerstone in building reliable, scalable, and secure AI-driven applications. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 10, 2024in AI Tools, Data, Generative AI
More Cool AI Tools
09Dec

Salesforce Expands Partnership with AWS

Salesforce Expands Partnership with AWS: AI and Marketplace Integration Salesforce (NYSE: CRM) is making significant strides in its partnership with Amazon (NASDAQ: AMZN), unveiling an expanded collaboration at AWS. Customers can now purchase Salesforce products directly through the AWS Marketplace, paying with AWS credits. This integration aims to simplify access to Salesforce offerings, enhance data integration capabilities, and leverage generative AI tools. Key Announcements: Marc Benioff, Chair and CEO of Salesforce, highlighted the importance of this milestone: “We’re bringing together the No. 1 AI CRM provider and the leading cloud provider to deliver a trusted, open, integrated data and AI platform. With these enhancements to our partnership, we’re enabling all of our customers to be more innovative, productive, and successful in this new AI era.” AWS CEO Adam Selipsky echoed these sentiments, emphasizing how the partnership will enable joint customers to “innovate, collaborate, and build more customer-focused applications.” Strategic Benefits: Revenue-Sharing Structure: Like app stores, Amazon will take a percentage of Salesforce’s revenue generated through AWS Marketplace. Despite this, the potential growth in sales and efficiency gains may outweigh the costs. Market Reaction: Following the announcement, both Salesforce and Amazon shares experienced a boost in premarket trading, signaling investor optimism about the partnership’s potential. This expansion reinforces Salesforce’s strategy of aligning with major cloud providers to meet growing demand for AI-driven, integrated data platforms. As this collaboration evolves, it is poised to drive significant value for businesses navigating the AI and data revolution. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 9, 2024in AI Tools, Data, Generative AI, Mulesoft, Salesforce
AI Agents, Tech's Next Big Bet
08Dec

Business Intelligence and AI

AI in Business Intelligence: Uses, Benefits, and Challenges AI tools are increasingly becoming integral to Business Intelligence (BI) systems, enhancing analytics capabilities and streamlining tasks. In this article, we explore how AI can bring new value to BI processes and what to consider as this integration continues to evolve. AI’s Role in Business Intelligence Business Intelligence tools, such as dashboards and interactive reports, have traditionally focused on analyzing historical and current data to describe business performance—known as descriptive analytics. While valuable, many business users seek more than just a snapshot of past performance. They also want predictive insights (forecasting future trends) and prescriptive guidance (recommendations for action). Historically, implementing these advanced capabilities was challenging due to their complexity, but AI simplifies this process. By leveraging AI’s analytical power and natural language processing (NLP), businesses can move from descriptive to predictive and prescriptive analytics, enabling proactive decision-making. AI-powered BI systems also offer the advantage of real-time data analysis, providing up-to-date insights that help businesses respond quickly to changing conditions. Additionally, AI can automate routine tasks, boosting efficiency across business operations. Benefits of Using AI in BI Initiatives The integration of AI into BI systems brings several key benefits, including: Examples of AI Applications in BI AI’s role in BI is not limited to internal process improvements. It can significantly enhance customer experience (CX) and support business growth. Here are a few examples: Challenges of Implementing AI in BI While the potential for AI in BI is vast, there are several challenges companies must address: Best Practices for Deploying AI in BI To maximize the benefits of AI in BI, companies should follow these best practices: Future Trends to Watch AI is not poised to replace traditional BI tools but to augment them with new capabilities. In the future, we can expect: In conclusion, AI is transforming business intelligence by turning data analysis from a retrospective activity into a forward-looking, real-time process. While challenges remain, such as data governance, ethical concerns, and skill shortages, AI’s potential to enhance BI systems and drive business success is undeniable. By following best practices and staying abreast of industry developments, businesses can harness AI to unlock new opportunities and deliver better insights. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 8, 2024in AI Tools, Data, Generative AI

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 Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 8, 2024in Artificial Intelligence, Data, Generative AI, Salesforce, Technology
06Dec

5 Attributes of an Agent

Defining the Role of AI Agents To successfully implement AI agents, organizations must clearly define their function across these five key areas: The Evolution of Agentic Automation Agentic automation represents a major shift in how enterprises leverage AI to drive productivity and efficiency. By seamlessly integrating AI agents, human employees, and automation technologies, businesses can orchestrate complex workflows from start to finish. AI agents are transforming customer service and business operations. These intelligent systems can plan and execute tasks, make informed decisions, and integrate with existing workflows to deliver superior efficiency. With the right AI strategy, businesses can elevate customer experiences by offering proactive, personalized, and highly responsive solutions. The Need for AI Agents Businesses today face mounting challenges: AI agents like Agentforce provide a scalable solution by automating interactions, streamlining processes, and ensuring continuous availability. Let’s explore what AI agents are, how they work, and how to deploy them successfully. What is an AI Agent? An AI agent is an intelligent system that autonomously interacts with customers, processes data, and executes actions without human intervention. Powered by machine learning and natural language processing (NLP), AI agents can: Unlike traditional automation, AI agents learn from interactions, refine their responses, and adapt to evolving business needs. Imagine if every employee—from the CEO to the newest sales rep—had an AI-powered assistant. With today’s AI advancements, that vision is becoming a reality. The Impact of Generative AI Agents More companies are embracing generative AI agents that leverage trusted customer data to deliver real-time insights. Tasks that once required extensive manual effort—like data analysis, trend forecasting, and customer support—can now be automated, freeing employees to focus on higher-value work. Beyond customer service, AI agents help businesses scale, meet key performance indicators, and solve problems before they escalate. The potential of this technology is just beginning to unfold. How AI Agents Work AI agents operate through a four-step process: By integrating these capabilities, AI agents can autonomously manage tasks like product recommendations, troubleshooting, and proactive follow-ups—allowing human employees to focus on strategic initiatives. Types of AI Agents Not all AI agents function the same way. Businesses can leverage different types of agents based on their operational needs: The Future of AI Agents AI-driven automation is redefining the way businesses operate. From enhancing customer experiences to optimizing internal workflows, AI agents are becoming indispensable tools for modern enterprises. As organizations invest in AI strategies, the key question remains: Are you ready to harness the full potential of AI agents to drive business success? Contact Tectonic today! By Tectonic Marketing Operations Director, Shannan Hearne Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 6, 2024in Data, Enterprise, Generative AI, Technology

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