Is the Future Agentic for ERP?
the success of AI in ERP depends on user adoption. If AI agents continue to make implementation easier and enhance current workflows, it’s clear that the future of ERP could indeed be agentic.
the success of AI in ERP depends on user adoption. If AI agents continue to make implementation easier and enhance current workflows, it’s clear that the future of ERP could indeed be agentic.
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.
The UK Leading AI’s Third Wave: Insights from Salesforce’s AI Readiness Index Salesforce’s latest UK AI Readiness Index positions the UK as a frontrunner in the third wave of AI innovation, particularly in agentic AI—autonomous systems capable of decision-making and action. This comes as nations globally compete for leadership in AI development, with significant implications for economic growth, national security, and technological sovereignty. UK’s AI Readiness Exceeds G7 Averages The index reveals that the UK’s overall readiness score is 65.5, outpacing the G7 average of 61.2. Both government and business sectors outperform their peers, reflecting a robust environment for innovation. Zahra Bahrololoumi, CBE, UKI CEO of Salesforce, highlights the transformative potential of this technology, stating: “Agentic AI is revolutionising enterprise software by enabling seamless collaboration between humans and AI agents, driving customer success. The UK AI Readiness Index affirms the UK’s vision and infrastructure to lead globally in this new wave of innovation.” Driving Forces Behind UK’s Leadership The UK’s strength lies in its holistic approach to AI development, integrating: Minister for AI and Digital Government, Feryal Clark, notes: “These findings are proof that the UK is primed to leverage AI’s potential, showcasing our strength in fostering innovation, investment, and collaboration across sectors.” AI in Action: Transforming UK Businesses Salesforce’s Agentforce platform is helping UK organisations capitalise on AI’s potential. Leading companies such as Capita, Heathrow Airport, and Bionic have reported significant productivity gains: The Road Ahead: Maintaining Leadership The report outlines key priorities for sustaining the UK’s position: Salesforce’s commitment to the UK includes a $4 billion investment over five years and the opening of its AI Centre in London, aimed at training developers and administrators in cutting-edge AI technologies. What the Experts Say Antony Walker, Deputy CEO of techUK, remarks: “The Salesforce UK AI Readiness Index highlights the UK’s strong position to lead the next wave of AI innovation. By supporting SMEs, investing in skills, and ensuring flexible regulation, the UK can solidify its global AI leadership.” Paul O’Sullivan, UKI CTO and SVP Solution Engineering at Salesforce, reinforces the urgency: “We are in the third wave of AI—an autonomous age moving at unprecedented speed. The UK has a unique opportunity to lead, but this requires sustained focus on skills, innovation, and collaboration.” Conclusion As the AI revolution accelerates, the UK’s leadership in agentic AI positions it as a global AI powerhouse. By balancing innovation with responsibility and investing in infrastructure and talent, the UK is not just adapting to AI’s future but shaping it. Salesforce’s AI initiatives, including its Agentforce platform and London AI Centre, ensure the UK remains at the forefront of this transformational journey. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
Can AI-Driven Care Coordination Software Improve Workflows? University Hospitals is leveraging AI to enhance care coordination across its network of 13 hospitals and numerous outpatient settings. This effort highlights the transformative potential of AI-driven platforms in streamlining workflows, improving patient outcomes, and addressing clinician burnout. The Role of AI in Care Coordination Care coordination ensures seamless collaboration between healthcare providers, aiming for safe, appropriate, and effective treatment. Effective information-sharing can: According to the U.S. Centers for Medicare & Medicaid Services (CMS), poor care coordination can lead to: The Agency for Healthcare Research and Quality (AHRQ) advocates for a mix of technology adoption and care-specific strategies, such as proactive care plans tailored to patient needs. While electronic health records (EHRs) aid in these efforts, AI’s ability to analyze vast data sets positions it as the next evolution in care coordination. University Hospitals’ AI Initiative University Hospitals has partnered with Aidoc to deploy its AI-powered platform, aiOS, to improve radiology and care coordination workflows. Chair of Radiology Donna Plecha shared insights on how AI is already assisting in their operations: Best Practices for Implementing AI 1. Identify High-Value Use Cases: 2. Conduct Architectural Reviews: 3. Monitor ROI and Metrics: 4. Gain Clinician Buy-In: Looking Ahead AI is proving to be a valuable tool in care coordination, but its adoption requires realistic expectations and a thoughtful approach. Plecha underscores that AI won’t replace radiologists but will empower those who embrace it. As healthcare faces increasing patient volumes and clinician shortages, leveraging AI to reduce workloads and enhance care quality is becoming a necessity. With ongoing evaluations and phased implementations, University Hospitals is setting a precedent for how AI can drive innovation in care coordination while maintaining clinician oversight and patient trust. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
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 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more
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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
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 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more
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 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
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 Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more
Tectonic is a leader in Salesforce solutions for the timeshare industry. We help you unify data, connect your systems, and create unforgettable guest experiences while simplifying the management of your business lines.
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 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
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.
Salesforce CEO Marc Benioff described the launch of Agentforce as part of an “agent revolution” that rivals the transformative impacts of the cloud, social, and mobile revolutions.
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 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more