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29Sep

Employees Have Different Motivations

The workforce has undergone significant changes over the last two years. Today’s employees have different motivations, seeking more flexibility and purpose, while also expecting more from corporate leaders. Employees Have Different Motivations. Similarly, customers now demand high levels of personalization and exceptional experiences. How can C-suite executives keep up with these evolving expectations? Our research highlights emerging priorities for corporate leaders in these challenging times. In a recent webinar, we asked two Inc. 5000 CEOs about shifting priorities and the critical role of enhancing employee experiences to meet rising customer demands. The message was clear: efficient growth starts with your employees. Focusing on employee satisfaction, providing clear paths for growth, establishing strong values, and investing in the right tools are key drivers of success. However, for some leaders, old habits hinder progress. Today’s executives must not only be digitally proficient but also agile, with strong emotional intelligence to manage change and new relationships effectively. A prime example of this disconnect is seen in employee engagement. Salesforce’s recent report, The Experience Advantage, found that while 71% of C-suite executives believe their employees are engaged, only 51% of employees agree. Similarly, 70% of executives think their employees are happy, but only 44% of employees share that sentiment. How can companies enable their leaders to succeed in this era of heightened expectations? Let’s explore the top priorities for CEOs today. Top Priorities for Corporate Leaders In a world where CEOs are accountable to more stakeholders than ever, they must navigate an increasingly complex landscape. They’re expected to speak on social issues, advocate for sustainability, and ensure stability in times of rapid change. Adaptability is crucial for success. Here are some current top priorities for corporate leaders: At Salesforce, they’ve found success by operating with startup-style values—centering consumer trust, fostering constant innovation, and setting clear, simple goals. Marc Benioff’s V2MOM framework exemplifies this alignment in action. The New Skills Leaders Need After reviewing research and interviewing business leaders, several trends have emerged. The most successful executives today share the following traits: A 2021 IBM Institute for Business Value survey of 3,000 global CEOs revealed similar trends, highlighting purposeful agility and making technology a priority. The study found that 56% of CEOs emphasized the need for operational flexibility, and 61% were focused on empowering remote work. Key technologies driving results over the next few years include the Internet of Things (79%), cloud computing (74%), and AI (52%). A major shift on leader agendas is the growing focus on employee experience. As Salesforce’s chief growth evangelist, Tiffani Bova, noted, “Employees are now the most important stakeholder to long-term success.” Providing seamless, consumer-like experiences for employees is now essential for business growth. Our research also uncovered a key gap: 73% of C-suite executives don’t know how to use employee data to drive change. This disconnect between leadership perception and actual employee experience is undermining growth. Emotional Intelligence (EQ) Matters To close this gap, sharpening leaders’ emotional intelligence is essential. Last year, we conducted interviews with 10 CEOs across various sectors. Many revealed plans to replace C-suite team members with more digitally savvy and emotionally intelligent leaders better equipped to manage the modern workforce. Summit Leadership Partners’ 2020 research found that 80-90% of top-performing executives excelled because of their high EQ. In fact, EQ is twice as predictive of performance as technical skills or IQ. The Changing Role of Key Executives Who do CEOs rely on most? A decade ago, IBM’s Institute for Business Value found that 47% of CEOs considered the chief innovation officer critical. Today, only 4% of CEOs agree. The chief marketing officer and chief strategy officer roles have also seen significant declines in perceived importance. The positions that have gained prominence include the chief technology officer (CTO) and chief information officer (CIO), now ranked third in importance after the chief financial officer (CFO) and chief operating officer (COO). As Jeff McElfresh, COO of AT&T, observed, “Not all leaders are comfortable managing in a distributed model. We’ve got work to do to unlock the potential.” The rise in job titles related to the future of work—up 60% since the pandemic—reflects this shift, with hybrid work models becoming more common. Diversity Drives Innovation and Profitability Diversity in leadership has become essential for driving revenue and innovation. McKinsey’s 2020 report Diversity Wins found that companies with more gender-diverse executive teams were 25% more likely to achieve above-average profitability. Similarly, those with greater ethnic diversity outperformed their peers by 36%. Diverse management teams also deliver 19% higher revenues from innovation compared to less-diverse teams, according to research from BCG. As diversity becomes increasingly tied to executive compensation, companies must support a diverse leadership pipeline by developing inclusive talent strategies. Moving Forward To thrive in today’s business world, corporate leaders must plan for change, ensure all executives have both digital literacy and emotional intelligence, and redistribute power to drive success. The healthiest C-suites will include diverse leaders in key positions like COO, CFO, and CIO/CTO. Aligning the business around common goals—like those in Salesforce’s V2MOM framework—and eliminating barriers for employees are key to staying ahead. Innovation must remain a top priority. By investing in the right tools and connected platforms, companies can reduce costs and drive sustainable growth. Reach out to Tectonic for assistance in making the innovations that recognizes Employees Have Different Motivations. 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

September 29, 2024in Data, Salesforce, Technology

28Sep

Data Data Data

With the unprecedented surge in data—expected to grow by 22% this year according to the Salesforce State of Data and Analytics 2023 report—and the rapid evolution of AI, maintaining data quality and security has become more critical than ever. The AI and Information Management 2024 Report highlights that 52% of organizations encountered data quality issues during AI implementation, while 71% expressed serious concerns about data privacy and security in AI deployments. The cornerstone of success in any data-driven initiative is strong data governance, ensuring data quality, trust, and privacy. The report also emphasized that effective data governance maximizes the value of data assets. Beyond compliance and quality control, it democratizes data access across an organization, making it essential for long-term success. This insight explores common challenges undermining Salesforce data quality and outlines how robust data governance can lead to high-quality, reliable data. Challenges to Maintaining Quality Salesforce Data Challenge 1: Data Silos Create Fragmented Customer Information Use Case: In the Consumer Packaged Goods (CPG) sector, nearly half of companies struggle to collect and integrate the massive amount of data required for AI adoption. Data fragmentation across supply chains, product categories, and SKUs creates silos, leading to inconsistent customer information. For instance, sales and marketing teams might work with different data sets, leading to disjointed customer interactions and ineffective campaigns. This results in missed sales opportunities, inefficient resource use, and poor customer experiences, ultimately weakening strategic decision-making and competitiveness. Solution: Implementing an information lifecycle management system can address data fragmentation. It provides a unified view for managing content across repositories, helping companies maintain a holistic customer view. Challenge 2: Poor Data Quality Erodes Trust Use Case: The Office of the National Coordinator for Health Information Technology found that at least 7 out of 100 medical records are mismatched or duplicated. A healthcare organization using Salesforce without proper data governance may face incomplete or inaccurate patient information, resulting in incorrect treatments and inefficient care, which damages patient trust and increases legal risks. Solution: You leverages AI to eliminate duplicate records, ensuring clean and accurate data. It classifies content based on metadata and assigns governance policies to prevent duplication, ensuring high-quality data across systems. Additionally, Backup for Salesforce tools detect suspicious anomalies or potential ransomware activity, proactively preserving data integrity. Challenge 3: Data Growth Increases Cyber Risks Use Case: As 65% of e-commerce organizations aim to become fully data-driven by 2026, increasing data volumes introduce greater risks. Without robust protection strategies, companies face potential data loss and operational disruptions. Solution: Tectonic’s solution offers automatic daily backups and on-demand restores, safeguarding data integrity and minimizing downtime in the event of cyberattacks or data breaches. Building a Strong Data Governance Foundation To overcome these challenges, organizations must adopt a comprehensive approach to data governance. Here are seven essential steps to establish effective Salesforce data governance: Champion Salesforce Data Governance with Tectonic Effective Salesforce data governance requires collaboration across all levels of the organization. With over two decades of experience, Tectonic empowers organizations to enhance their digital operations, mitigate risks, and achieve compliance in a dynamic data landscape. By establishing a strong data governance framework, your organization can confidently navigate the challenges of data growth, security, and quality. 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

September 28, 2024in Salesforce

27Sep

Exploring Emerging LLM

Exploring Emerging LLM Agent Types and Architectures The Evolution Beyond ReAct AgentsThe shortcomings of first-generation ReAct agents have paved the way for a new era of LLM agents, bringing innovative architectures and possibilities. In 2024, agents have taken center stage in the AI landscape. Companies globally are developing chatbot agents, tools like MultiOn are bridging agents to external websites, and frameworks like LangGraph and LlamaIndex Workflows are helping developers build more structured, capable agents. However, despite their rising popularity within the AI community, agents are yet to see widespread adoption among consumers or enterprises. This leaves businesses wondering: How do we navigate these emerging frameworks and architectures? Which tools should we leverage for our next application? Having recently developed a sophisticated agent as a product copilot, we share key insights to guide you through the evolving agent ecosystem. What Are LLM-Based Agents? At their core, LLM-based agents are software systems designed to execute complex tasks by chaining together multiple processing steps, including LLM calls. These agents: The Rise and Fall of ReAct Agents ReAct (reason, act) agents marked the first wave of LLM-powered tools. Promising broad functionality through abstraction, they fell short due to their limited utility and overgeneralized design. These challenges spurred the emergence of second-generation agents, emphasizing structure and specificity. The Second Generation: Structured, Scalable Agents Modern agents are defined by smaller solution spaces, offering narrower but more reliable capabilities. Instead of open-ended design, these agents map out defined paths for actions, improving precision and performance. Key characteristics of second-gen agents include: Common Agent Architectures Agent Development Frameworks Several frameworks are now available to simplify and streamline agent development: While frameworks can impose best practices and tooling, they may introduce limitations for highly complex applications. Many developers still prefer code-driven solutions for greater control. Should You Build an Agent? Before investing in agent development, consider these criteria: If you answered “yes,” an agent may be a suitable choice. Challenges and Solutions in Agent Development Common Issues: Strategies to Address Challenges: Conclusion The generative AI landscape is brimming with new frameworks and fervent innovation. Before diving into development, evaluate your application needs and consider whether agent frameworks align with your objectives. By thoughtfully assessing the tools and architectures available, you can create agents that deliver measurable value while avoiding unnecessary complexity. 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

September 27, 2024in Data, Enterprise, Generative AI

27Sep

Growing Energy Consumption in Generative AI

Growing Energy Consumption in Generative AI, but ROI Impact Remains Unclear The rising energy costs associated with generative AI aren’t always central in enterprise financial considerations, yet experts suggest IT leaders should take note. Building a business case for generative AI involves both obvious and hidden expenses. Licensing fees for large language models (LLMs) and SaaS subscriptions are visible expenses, but less apparent costs include data preparation, cloud infrastructure upgrades, and managing organizational change. Growing Energy Consumption in Generative AI. One under-the-radar cost is the energy required by generative AI. Training LLMs demands vast computing power, and even routine AI tasks like answering user queries or generating images consume energy. These intensive processes require robust cooling systems in data centers, adding to energy use. While energy costs haven’t been a focus for GenAI adopters, growing awareness has prompted the International Energy Agency (IEA) to predict a doubling of data center electricity consumption by 2026, attributing much of the increase to AI. Goldman Sachs echoed these concerns, projecting data center power consumption to more than double by 2030. For now, generative AI’s anticipated benefits outweigh energy cost concerns for most enterprises, with hyperscalers like Google bearing the brunt of these costs. Google recently reported a 13% increase in greenhouse gas emissions, citing AI as a major contributor and suggesting that reducing emissions might become more challenging with AI’s continued growth. Growing Energy Consumption in Generative AI While not a barrier to adoption, energy costs play into generative AI’s long-term viability, noted Scott Likens, global AI engineering leader at PwC, emphasizing that “there’s energy being used — you don’t take it for granted.” Energy Costs and Enterprise Adoption Generative AI users might not see a line item for energy costs, yet these are embedded in fees. Ryan Gross of Caylent points out that the costs are mainly tied to model training and inferencing, with each model query, though individually minor, adding up over time. These expenses are often spread across the customer base, as companies pay for generative AI access through a licensing model. A PwC sustainability study showed that GenAI power costs, particularly from model training, are distributed among licensees. Token-based pricing for LLM usage also reflects inferencing costs, though these charges have decreased. Likens noted that the largest expenses still come from infrastructure and data management rather than energy. Potential Efficiency Gains Though energy isn’t a primary consideration, enterprises could reduce consumption indirectly through technological advancements. Newer, more cost-efficient models like OpenAI’s GPT-4o mini are 60% less expensive per token than prior versions, enabling organizations to deploy GenAI on a larger scale while keeping costs lower. Small, fine-tuned models can be used to address latency and lower energy consumption, part of a “multimodel” approach that can provide different accuracy and latency levels with varying energy demands. Agentic AI also offers opportunities for cost and energy savings. By breaking down tasks and routing them through specialized models, companies can minimize latency and reduce power usage. According to Likens, using agentic architecture could cut costs and consumption, particularly when tasks are routed to more efficient models. Rising Data Center Energy Needs While enterprises may feel shielded from direct energy costs, data centers bear the growing power demand. Cooling solutions are evolving, with liquid cooling systems becoming more prevalent for AI workloads. As data centers face the “AI growth cycle,” the demand for energy-efficient cooling solutions has fueled a resurgence in thermal management investment. Liquid cooling, being more efficient than air cooling, is gaining traction due to the power demands of AI and high-performance computing. IDTechEx projects that data center liquid cooling revenue could exceed $50 billion by 2035. Meanwhile, data centers are exploring nuclear power, with AWS, Google, and Microsoft among those considering nuclear energy as a sustainable solution to meet AI’s power demands. Future ROI Considerations While enterprises remain shielded from the full energy costs of generative AI, careful model selection and architectural choices could help curb consumption. PwC, for instance, factors in the “carbon impact” as part of its GenAI deployment strategy, recognizing that energy considerations are now a part of the generative AI value proposition. As organizations increasingly factor sustainability into their tech decisions, energy efficiency might soon play a larger role in generative AI ROI calculations. 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

September 27, 2024in Data, Enterprise, Generative AI, Google

26Sep

AI Agent Myths

Myths About AI Agents Agents will transform how we work, but separating fact from fiction is essential. AI agents are revolutionizing business operations, yet misconceptions persist about their capabilities and value. Understanding these myths—and the truth behind them—can help your organization unlock their potential. Myth #1: AI Agents Are Just Glorified Chatbots While chatbots and AI agents both use artificial intelligence, their functionality and complexity differ significantly. For instance, a chatbot might provide an overview of your sales metrics, but an AI agent can analyze those metrics, forecast demand, adjust inventory levels, update marketing strategies, and even notify suppliers—all proactively and autonomously. This leap in capability allows agents to optimize workflows, make strategic recommendations, and dynamically respond to changing conditions. They’re not just answering questions—they’re driving outcomes. Myth #2: They’re unpredictable and uncontrollablePopular culture often paints AI as rogue systems—think 2001: A Space Odyssey or The Terminator—but in reality, modern AI agents are designed with safety, trust, and precision at their core. The most effective agents today use advanced techniques to prevent errors and ensure their actions stay within strict boundaries. At the heart of this is a reasoning engine. This engine doesn’t just execute tasks—it creates action plans based on the user’s goals, evaluates those plans, and refines them by pulling data from customer relationship management (CRM) systems and other platforms. It then determines the correct processes to execute and iterates until the task is completed successfully, improving with each interaction. When tasks fall outside an organization’s predefined guardrails—like user permissions or compliance rules—the reasoning engine automatically flags the task and escalates it for human oversight. “Helping an agent perform accurately while understanding what it is not allowed to do is a complex task,” says Krishna Gandikota, Manager of Solution Engineering at Salesforce. “The reasoning engine plans and evaluates the AI’s approach before it takes any action. It also assesses whether it has the necessary skills and information to proceed.” This process is further enhanced by continuous learning, enabling agents to refine their decision-making and actions over time. Grounded in DataThe best agents are contextually aware, leveraging relevant, up-to-date information to perform tasks accurately. Techniques like retrieval-augmented generation (RAG) help by sourcing the most relevant data, while semantic search ensures that agents retrieve the latest and most accurate information. Salesforce’s Agentforce employs these methods using Data Cloud, which enables agents to access real-time data without physically copying or modifying it—thanks to zero-copy architecture. This ensures speed, accuracy, and compliance across all agent-driven actions. Myth #3: They’re complicated, time-consuming, and expensive to set upIt’s easy to assume that deploying AI agents would require months of integration work and millions of dollars, but that’s no longer the case. Advances in generative AI and large language models (LLMs) have drastically simplified the process. Agents can now be deployed in minutes with prebuilt topics—specific areas of focus—and actions for common tasks in customer service, sales, and commerce. For more tailored needs, low-code tools make it easy to create custom agents. Using natural language processing (NLP), you simply describe what the agent needs to do, and the system builds it for you. For instance, Agent Builder automatically suggests guardrails and resources based on the task description. By scanning an app’s metadata, it identifies semantically similar processes, creating a smarter, context-aware agent that aligns with your business operations. “All the sophistication is already built into the platform,” Gandikota explains. “The Einstein Trust Layer, reasoning engine, and vector database for RAG and semantic search work seamlessly. With this foundation, you can build a team of agents quickly and confidently.” Myth #4: They’re always fully autonomousAI agents don’t need to operate completely autonomously to deliver value. Their autonomy depends on the complexity of their tasks and the industry they serve. “Agents don’t always need to take actions autonomously,” Gandikota explains. “They’re designed to understand requests, assess whether they can proceed independently, and involve humans when necessary.” Myth #5: They won’t deliver real business valueSome businesses using generic AI tools haven’t seen the ROI they expected. That’s because generic AI isn’t tailored to specific business needs. AI agents, on the other hand, are purpose-built to perform specialized tasks with precision. Whether it’s nurturing sales leads, brainstorming marketing campaigns, or resolving service tickets, targeted AI agents excel at solving specific problems. Unlike generic AI, they don’t just provide insights—they take action, driving measurable outcomes. For example, educational publisher Wiley improved support case resolution by over 40% after adopting AI agents. By handling routine tasks, the agents freed up Wiley’s service teams to focus on more complex cases. Similarly, early adopters like OpenTable and ADP have reported significant improvements in customer satisfaction and efficiency. According to MarketsandMarkets, AI agents are driving demand for automation by enhancing decision-making, scalability, and efficiency. The global market for AI agents is expected to grow from .1 billion in 2024 to billion by 2030. The Bottom LineUnderstanding the myths—and realities—of AI agents is critical for business leaders. Misconceptions can lead to missed opportunities, while clarity around their capabilities can help organizations work smarter, faster, and more efficiently. With trusted, adaptable, and purpose-built agents, the future of business automation is already here. 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

September 26, 2024in Artificial Intelligence, Data

26Sep

AI Agents Connect Tool Calling and Reasoning

AI Agents: Bridging Tool Calling and Reasoning in Generative AI Exploring Problem Solving and Tool-Driven Decision Making in AI Introduction: The Emergence of Agentic AI Recent advancements in libraries and low-code platforms have simplified the creation of AI agents, often referred to as digital workers. Tool calling stands out as a key capability that enhances the “agentic” nature of Generative AI models, enabling them to move beyond mere conversational tasks. By executing tools (functions), these agents can act on your behalf and tackle intricate, multi-step problems requiring sound decision-making and interaction with diverse external data sources. This insight explores the role of reasoning in tool calling, examines the challenges associated with tool usage, discusses common evaluation methods for tool-calling proficiency, and provides examples of how various models and agents engage with tools. Reasoning as a Means of Problem-Solving Successful agents rely on two fundamental expressions of reasoning: reasoning through evaluation and planning, and reasoning through tool use. While both reasoning expressions are vital, they don’t always need to be combined to yield powerful solutions. For instance, OpenAI’s new o1 model excels in reasoning through evaluation and planning, having been trained to utilize chain of thought effectively. This has notably enhanced its ability to address complex challenges, achieving human PhD-level accuracy on benchmarks like GPQA across physics, biology, and chemistry, and ranking in the 86th-93rd percentile on Codeforces contests. However, the o1 model currently lacks explicit tool calling capabilities. Conversely, many models are specifically fine-tuned for reasoning through tool use, allowing them to generate function calls and interact with APIs effectively. These models focus on executing the right tool at the right moment but may not evaluate their results as thoroughly as the o1 model. The Berkeley Function Calling Leaderboard (BFCL) serves as an excellent resource for comparing the performance of various models on tool-calling tasks and provides an evaluation suite for assessing fine-tuned models against challenging scenarios. The recently released BFCL v3 now includes multi-step, multi-turn function calling, raising the standards for tool-based reasoning tasks. Both reasoning types are powerful in their own right, and their combination holds the potential to develop agents that can effectively deconstruct complex tasks and autonomously interact with their environments. For more insights into AI agent architectures for reasoning, planning, and tool calling, check out my team’s survey paper on ArXiv. Challenges in Tool Calling: Navigating Complex Agent Behaviors Creating robust and reliable agents necessitates overcoming various challenges. In tackling complex problems, an agent often must juggle multiple tasks simultaneously, including planning, timely tool interactions, accurate formatting of tool calls, retaining outputs from prior steps, avoiding repetitive loops, and adhering to guidelines to safeguard the system against jailbreaks and prompt injections. Such demands can easily overwhelm a single agent, leading to a trend where what appears to an end user as a single agent is actually a coordinated effort of multiple agents and prompts working in unison to divide and conquer the task. This division enables tasks to be segmented and addressed concurrently by distinct models and agents, each tailored to tackle specific components of the problem. This is where models with exceptional tool-calling capabilities come into play. While tool calling is a potent method for empowering productive agents, it introduces its own set of challenges. Agents must grasp the available tools, choose the appropriate one from a potentially similar set, accurately format the inputs, execute calls in the correct sequence, and potentially integrate feedback or instructions from other agents or humans. Many models are fine-tuned specifically for tool calling, allowing them to specialize in selecting functions accurately at the right time. Key considerations when fine-tuning a model for tool calling include: Common Benchmarks for Evaluating Tool Calling As tool usage in language models becomes increasingly significant, numerous datasets have emerged to facilitate the evaluation and enhancement of model tool-calling capabilities. Two prominent benchmarks include the Berkeley Function Calling Leaderboard and the Nexus Function Calling Benchmark, both utilized by Meta to assess the performance of their Llama 3.1 model series. The recent ToolACE paper illustrates how agents can generate a diverse dataset for fine-tuning and evaluating model tool use. Here’s a closer look at each benchmark: Each of these benchmarks enhances our ability to evaluate model reasoning through tool calling. They reflect a growing trend toward developing specialized models for specific tasks and extending the capabilities of LLMs to interact with the real world. Practical Applications of Tool Calling If you’re interested in observing tool calling in action, here are some examples to consider, categorized by ease of use, from simple built-in tools to utilizing fine-tuned models and agents with tool-calling capabilities. While the built-in web search feature is convenient, most applications require defining custom tools that can be integrated into your model workflows. This leads us to the next complexity level. To observe how models articulate tool calls, you can use the Databricks Playground. For example, select the Llama 3.1 405B model and grant access to sample tools like get_distance_between_locations and get_current_weather. When prompted with, “I am going on a trip from LA to New York. How far are these two cities? And what’s the weather like in New York? I want to be prepared for when I get there,” the model will decide which tools to call and what parameters to provide for an effective response. In this scenario, the model suggests two tool calls. Since the model cannot execute the tools, the user must input a sample result to simulate. Suppose you employ a model fine-tuned on the Berkeley Function Calling Leaderboard dataset. When prompted, “How many times has the word ‘freedom’ appeared in the entire works of Shakespeare?” the model will successfully retrieve and return the answer, executing the required tool calls without the user needing to define any input or manage the output format. Such models handle multi-turn interactions adeptly, processing past user messages, managing context, and generating coherent, task-specific outputs. As AI agents evolve to encompass advanced reasoning and problem-solving capabilities, they will become increasingly adept at managing

September 26, 2024in Data, Generative AI

23Sep

Oracle Advertising Sundown

Oracle Shifts Focus to B2B CX, Introduces New Fusion Cloud Features Despite winding down its online advertising products, Oracle is doubling down on its investment in customer experience (CX) technology, particularly in enabling B2B buying and supporting subscription and consumption models. During the Oracle CloudWorld conference on Wednesday, the company unveiled new capabilities for its Fusion Cloud Customer Experience and Unity Customer Data Platform. These enhancements empower Oracle CX users to analyze customer profiles to assemble B2B buying teams, leverage generative AI tools like native analytics, and utilize industry-specific accelerators to speed up the adoption of customer data tools. Key features include the ability to create self-service sites for individual accounts, enabling customers to review and summarize contracts using generative AI, receive quotes, and renew subscriptions. Other features enhance “assisted buying experiences,” blending self-service and human interaction, while tools like account onboarding and AI-powered email drafting simplify full-service sales processes. Subscription models, though still in their early stages for B2B, offer a streamlined alternative to traditional procurement processes. As Liz Miller, an analyst at Constellation Research, noted, subscription-based buying is easier and quicker, avoiding the lengthy procurement cycles many B2B buyers are familiar with. “The pain of traditional B2B buying is still fresh in everyone’s mind,” she said. Oracle Advertising Shuts Down Oracle’s advertising product support will end on September 30, as confirmed by CEO Safra Catz during the company’s June earnings call. The Oracle Advertising Data Management Platform (DMP), built from its BlueKai acquisition, is being retired, following in the footsteps of Salesforce, which discontinued its Audience Studio in 2021. Despite Oracle winding down its ad platform, this move shouldn’t be seen as a shift away from customer experience. Oracle founder Larry Ellison remains deeply involved in shaping the company’s CX strategy, with a focus on marketing tools and Apex low-code platforms, said Rob Pinkerton, Oracle’s senior vice president. Oracle’s modernized CX suite, built on the Fusion Cloud platform, has evolved significantly in recent years, though questions remain about whether it’s too late to regain market share. “Oracle as a CX platform has fallen off the radar for many buyers,” said Miller, adding that customers are no longer debating between Oracle, Microsoft, and Salesforce in the CX space. New Industry-Specific Tools for CX Oracle has also expanded its CX platform with industry-specific tools designed to accelerate the adoption of its customer data platform (CDP) across sectors such as high tech, manufacturing, professional services, telecommunications, utilities, financial services, travel, and retail. According to Rebecca Wettemann, CEO of research firm Valoir, Oracle’s Fusion platform has matured significantly and now supports the complexity of modern customer needs. Wettemann highlighted how common components like customer interaction summaries can be adapted for multiple industries, delivering faster results than traditional applications. Oracle’s Clinical Digital Assistant is one such example of this approach, illustrating the platform’s versatility and AI-driven enhancements. With these developments, Oracle continues to refine its CX offerings to better meet the unique demands of B2B customers, providing tools that streamline operations and enhance customer experiences across various industries. 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

September 23, 2024in AI Tools, Data, Enterprise, Generative AI, Salesforce, Technology

21Sep

Training and Testing Data

Data plays a pivotal role in machine learning (ML) and artificial intelligence (AI). Tasks such as recognition, decision-making, and prediction rely on knowledge acquired through training. Much like a parent teaches their child to distinguish between a cat and a bird, or an executive learns to identify business risks hidden within detailed quarterly reports, ML models require structured training using high-quality, relevant data. As AI continues to reshape the modern business landscape, the significance of training data becomes increasingly crucial. What is Training Data? The two primary strengths of ML and AI lie in their ability to identify patterns in data and make informed decisions based on that data. To execute these tasks effectively, models need a reference framework. Training data provides this framework by establishing a baseline against which models can assess new data. For instance, consider the example of image recognition for distinguishing cats from birds. ML models cannot inherently differentiate between objects; they must be taught to do so. In this scenario, training data would consist of thousands of labeled images of cats and birds, highlighting relevant features—such as a cat’s fur, pointed ears, and four legs versus a bird’s feathers, absence of ears, and two feet. Training data is generally extensive and diverse. For the image recognition case, the dataset might include numerous examples of various cats and birds in different poses, lighting conditions, and settings. The data must be consistent enough to capture common traits while being varied enough to represent natural differences, such as cats of different fur colors in various postures like crouching, sitting, standing, and jumping. In business analytics, an ML model first needs to learn the operational patterns of a business by analyzing historical financial and operational data before it can identify problems or recognize opportunities. Once trained, the model can detect unusual patterns, like abnormally low sales for a specific item, or suggest new opportunities, such as a more cost-effective shipping option. After ML models are trained, tested, and validated, they can be applied to real-world data. For the cat versus bird example, a trained model could be integrated into an AI platform that uses real-time camera feeds to identify animals as they appear. How is Training Data Selected? The adage “garbage in, garbage out” resonates particularly well in the context of ML training data; the performance of ML models is directly tied to the quality of their training data. This underscores the importance of data sources, relevance, diversity, and quality for ML and AI developers. Data SourcesTraining data is seldom available off-the-shelf, although this is evolving. Sourcing raw data can be a complex task—imagine locating and obtaining thousands of images of cats and birds for the relatively straightforward model described earlier. Moreover, raw data alone is insufficient for supervised learning; it must be meticulously labeled to emphasize key features that the ML model should focus on. Proper labeling is crucial, as messy or inaccurately labeled data can provide little to no training value. In-house teams can collect and annotate data, but this process can be costly and time-consuming. Alternatively, businesses might acquire data from government databases, open datasets, or crowdsourced efforts, though these sources also necessitate careful attention to data quality criteria. In essence, training data must deliver a complete, diverse, and accurate representation for the intended use case. Data RelevanceTraining data should be timely, meaningful, and pertinent to the subject at hand. For example, a dataset containing thousands of animal images without any cat pictures would be useless for training an ML model to recognize cats. Furthermore, training data must relate directly to the model‘s intended application. For instance, business financial and operational data might be historically accurate and complete, but if it reflects outdated workflows and policies, any ML decisions based on it today would be irrelevant. Data Diversity and BiasA sufficiently diverse training dataset is essential for constructing an effective ML model. If a model’s goal is to identify cats in various poses, its training data should encompass images of cats in multiple positions. Conversely, if the dataset solely contains images of black cats, the model’s ability to identify white, calico, or gray cats may be severely limited. This issue, known as bias, can lead to incomplete or inaccurate predictions and diminish model performance. Data QualityTraining data must be of high quality. Problems such as inaccuracies, missing data, or poor resolution can significantly undermine a model’s effectiveness. For instance, a business’s training data may contain customer names, addresses, and other information. However, if any of these details are incorrect or missing, the ML model is unlikely to produce the expected results. Similarly, low-quality images of cats and birds that are distant, blurry, or poorly lit detract from their usefulness as training data. How is Training Data Utilized in AI and Machine Learning? Training data is input into an ML model, where algorithms analyze it to detect patterns. This process enables the ML model to make more accurate predictions or classifications on future, similar data. There are three primary training techniques: Where Does Reinforcement Learning Fit In? Unlike supervised and unsupervised learning, which rely on predefined training datasets, reinforcement learning adopts a trial-and-error approach, where an agent interacts with its environment. Feedback in the form of rewards or penalties guides the agent’s strategy improvement over time. Whereas supervised learning depends on labeled data and unsupervised learning identifies patterns in raw data, reinforcement learning emphasizes dynamic decision-making, prioritizing ongoing experience over static training data. This approach is particularly effective in fields like robotics, gaming, and other real-time applications. The Role of Humans in Supervised Training The supervised training process typically begins with raw data since comprehensive and appropriately pre-labeled datasets are rare. This data can be sourced from various locations or even generated in-house. Training Data vs. Testing Data Post-training, ML models undergo validation through testing, akin to how teachers assess students after lessons. Test data ensures that the model has been adequately trained and can deliver results within acceptable accuracy and performance ranges. In supervised learning,

September 21, 2024in Artificial Intelligence, Data

21Sep

Agentforce and Thinking AI

Agentforce is how humans with AI drive customer success together, equips organizations with autonomous agents that boost scale, efficiency, and satisfaction across service, sales, marketing, commerce, and more New Agentforce Atlas Reasoning Engine autonomously analyzes data, makes decisions, and completes tasks, providing reliable and accurate results With Agentforce, any organization can build, customize, and deploy their own agents quickly and easily, with low-code tools New Agentforce Partner Network allows customers to deploy pre-built agents and use agent actions from partners like Amazon Web Services, Google, IBM, Workday, and more Customers like OpenTable, Saks, and Wiley are turning to Agentforce because it is integrated with their apps, works across customer channels, augments their employees, and scales capacity for business needs SAN FRANCISCO — September 12, 2024 – Salesforce (NYSE: CRM), the world’s #1 AI CRM, today unveiled Agentforce, a groundbreaking suite of autonomous AI agents that augment employees and handle tasks in service, sales, marketing, and commerce, driving unprecedented efficiency and customer satisfaction. Agentforce enables companies to scale their workforces on demand with a few clicks. Agentforce’s limitless digital workforce of AI agents can analyze data, make decisions, and take action on tasks like answering customer service inquiries, qualifying sales leads, and optimizing marketing campaigns. With Agentforce, any organization can easily build, customize, and deploy their own agents for any use case across any industry. The future of AI is agents, and it’s here. Our vision is bold: to empower one billion agents with Agentforce by the end of 2025. This is what AI is meant to be.” MARC BENIOFF, CHAIR, CEO & CO-FOUNDER, SALESFORCE “Agentforce represents the Third Wave of AI—advancing beyond copilots to a new era of highly accurate, low-hallucination intelligent agents that actively drive customer success. Unlike other platforms, Agentforce is a revolutionary and trusted solution that seamlessly integrates AI across every workflow, embedding itself deeply into the heart of the customer journey. This means anticipating needs, strengthening relationships, driving growth, and taking proactive action at every touchpoint,” said Marc Benioff, Chair and CEO, Salesforce. “While others require you to DIY your AI, Agentforce offers a fully tailored, enterprise-ready platform designed for immediate impact and scalability. With advanced security features, compliance with industry standards, and unmatched flexibility. Our vision is bold: to empower one billion agents with Agentforce by the end of 2025. This is what AI is meant to be.” In contrast to now-outdated copilots and chatbots that rely on human requests and struggle with complex or multi-step tasks, Agentforce offers a new level of sophistication by operating autonomously, retrieving the right data on demand, building action plans for any task, and executing these plans without requiring human intervention. Like a self-driving car, Agentforce uses real-time data to adapt to changing conditions and operates independently within an organizations’ customized guardrails, ensuring every customer interaction is informed, relevant, and valuable. And when desired, Agentforce seamlessly hands off to human employees with a summary of the interaction, an overview of the customer’s details, and recommendations for what to do next. Industry leaders like OpenTable, Saks, and Wiley are already experiencing the transformative power of Agentforce. For example, Agentforce is helping organizations like Wiley provide customers with dynamic, conversational self-service. Agentforce is configured to answer questions using Wiley’s knowledge base already built into Salesforce so it can automatically resolve account access. It also triages registration and payment issues, directing customers to the appropriate resources. With Agentforce handling routine inquiries, Wiley has seen an over 40% increase in case resolution, outperforming their old chatbot and giving their human agents more time to focus on complex cases. Why it Matters An estimated 41% of employee time is spent on repetitive, low-impact work, and 65% of desk workers believe generative AI will allow them to be more strategic, according to the Salesforce Trends in AI Report. Every company has more jobs to be done than the resources available to do them. As a result, many jobs go unaddressed or uncompleted. Agentforce provides relief to overstretched teams with its ability to scale capacity on demand so humans can focus on higher-touch, higher-value, and more strategic outcomes. The future of work is a hybrid workforce composed of humans with agents, enabling companies to compete in an ever-changing world. Supporting Customer Quotes “Piloting Agentforce has made a noticeable difference during one of our busiest periods — back-to-school season. It’s been exciting to go live with our first agent thanks to the no-code builder, and we’ve seen a more than 40% increase in case resolution, outperforming our old bot. Agentforce helps to manage routine responsibilities and free up our service teams for more complex cases.” – Kevin Quigley, Senior Manager, Continuous Improvement, Wiley “Every interaction that restaurants and diners have with our support team must be accurate, fast, and reflective of the hospitality that restaurants show their guests. Agentforce has incredible potential to help us deliver that high touch attentiveness and support while significantly freeing up our team to address more complex needs.” – George Pokorny, SVP Customer Success, OpenTable “As we advance our personalization strategy, we believe Agentforce and its AI-powered capabilities have the potential to make a real impact on our approach to customer engagement, raising the bar in luxury retail. Agentforce will improve our effectiveness across customer touchpoints, empowering our employees and augmenting their ability to deliver the elevated and more individualized shopping experiences for which Saks is known.” – Mike Hite, Chief Technology Officer, Saks Global 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

September 21, 2024in Data, Enterprise, Generative AI, Google, Salesforce, Technology

21Sep

Data Governance Frameworks

Examples of Data Governance Frameworks Data governance is not a one-size-fits-all approach. Organizations must carefully choose a framework that aligns with their unique goals, structure, and culture. Data is one of an organization’s most valuable assets, and proper governance is key to unlocking its potential. Without a well-designed framework, companies risk poor data quality, privacy breaches, regulatory noncompliance, and missed insights. A data governance framework provides a structured way to manage data throughout its lifecycle, including policies, processes, and standards to ensure data is accurate, accessible, and secure. By putting clear guidelines in place, organizations can increase trust in their data and improve decision-making. Key Pillars of a Data Governance Frameworks A robust data governance framework typically rests on four key pillars: 1. Center-Out Model The center-out model places a centralized team, such as a data governance council, at the core of the governance process. This group establishes policies and oversees data management across the organization, balancing consistency with flexibility for different departments. The Data Governance Institute’s framework is an example of this model. It focuses on creating a Data Governance Office responsible for managing key governance functions such as setting data policies, assigning data stewards, and monitoring compliance. The framework provides a clear structure while allowing business units some leeway in adapting governance practices to their needs. PwC’s model also adopts a center-out approach, with an emphasis on using data governance to monetize data assets. It highlights the importance of maintaining consistency while minimizing the risk of data silos. 2. Top-Down Model In the top-down model, data governance is driven by executive leadership, ensuring alignment with strategic goals. This model provides authority for enforcing governance standards but may face challenges if business units feel disconnected from the central governance team. McKinsey’s framework exemplifies this approach, focusing on integrating data governance with broader business transformation efforts. Executive leadership plays a key role in ensuring that governance initiatives receive the necessary attention and resources. 3. Hybrid Model The hybrid model combines centralized governance with flexibility for individual business units. It establishes an enterprise-wide framework while allowing departments to adapt governance practices to their specific needs. The Eckerson Group’s Modern Data Governance Framework represents a hybrid approach. It emphasizes the importance of people and culture, alongside technology and processes, and encourages organizations to create a roadmap for governance that evolves as needs change. This model provides a balance between centralized control and decentralized flexibility. 4. Bottom-Up Model In the bottom-up model, data governance is driven by subject matter experts and data stakeholders across the organization. This approach promotes collaboration and buy-in from the people closest to the data, ensuring that governance policies are practical and effective. The DAMA-DMBOK framework, developed by the Data Management Association, is a prime example. Although flexible, it often starts as a bottom-up initiative, driven by IT departments and data experts who later gain executive support. 5. Silo-In Model The silo-in model allows individual business units or departments to create their own governance practices. While this approach addresses localized data issues, it often leads to inconsistencies and challenges when the organization needs to integrate data across the enterprise. Though not widely recommended, the silo-in approach may emerge when specific business units take the initiative to establish governance due to regulatory requirements or data management needs within their domains. However, as organizations mature, they often transition to more holistic frameworks to support cross-functional collaboration and data integration. Choosing the Right Framework Selecting the right data governance framework involves evaluating the organization’s needs, structure, and culture. Whether an organization adopts a center-out, top-down, hybrid, bottom-up, or silo-in approach, success depends on involving key stakeholders, securing executive buy-in, and committing to continuous improvement. By treating data as a critical asset and implementing a governance framework that aligns with its business strategy, an organization can ensure that its data management practices support growth, innovation, and regulatory compliance. 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

September 21, 2024in Data, Enterprise, Salesforce, Technology

20Sep

Agentforce Advances Copilot and Prompt Builder

Agentforce was the highlight of the week in San Francisco during Salesforce’s annual Dreamforce conference—and for good reason! Agentforce Advances Copilot and Prompt Builder and that is truly exciting. Agentforce represents a groundbreaking solution that promises to transform how individuals and organizations interact with their CRM. However, as with any major product announcement, it raises many questions. This was evident during Dreamforce, where admins and developers, eager to dive into Agentforce, had numerous queries. Here’s an in-depth look at what Agentforce is, how it operates, and how organizations can leverage it to automate processes and drive value today. Agentforce Advances Copilot and Prompt Builder Many Dreamforce attendees who anticipated hearing more about Einstein Copilot were surprised by the introduction of Agents just before the event. However, understanding the distinctions between the legacy Einstein Copilot and the new Agentforce is crucial. Agentforce Advances Copilot and Prompt Builder. Agentforce Agents are essentially a rebranding of Copilot Agents but with an essential enhancement: they expand the functionality of Copilot to create autonomous agents capable of tasks such as summarizing or generating content and taking specific actions. Here are some key changes in terminology: Just like Einstein Copilot, Agents use user input—an “utterance”—entered into the Agentforce chat interface. The agent translates this utterance into a series of actions based on configurable instructions, and then executes the plan, providing a response. Understanding Agents: Topics A key difference between Einstein Copilot and Agentforce is the addition of “Topics.” Topics allow for greater flexibility and support a broader range of actions. They organize tasks by business function, helping Agents first determine the appropriate topic and then identify the necessary actions. This topic layer reduces confusion and ensures the correct action is taken. With this structure, Agentforce can support many more custom actions compared to Copilot’s 15-20, significantly expanding capabilities. Understanding Agents: Actions Actions in Agentforce function similarly to those in Einstein Copilot. These are the tasks an agent executes once it has identified the right plan. Out-of-the-box actions are available right away, providing a quick win for organizations looking to implement standard actions like opportunity summarization or sales emails. For more customized use cases, organizations can create bespoke actions using Apex, Flows, Prompts, or Service Catalog items (currently in beta). Understanding Agents: Prompts Whenever an LLM is used, prompts are necessary to provide the right input. Thoughtfully engineered prompts are essential for getting accurate, useful responses from LLMs. This is a key part of leveraging Agent Actions effectively, ensuring better results, reducing errors, and driving productive agent behavior. Prompt Builder plays a crucial role, allowing users to build, test, and refine prompts for Agent Actions, creating a seamless experience between generative AI and Salesforce workflows. How Generative AI and Agentforce Enhance CRM GenAI tools like Agentforce offer exciting enhancements to Salesforce organizations in several ways: However, these benefits are realized only when CRM users adopt and adapt to AI-assisted workflows. Organizations must prioritize change management and training, as most users will need to adjust to this new AI-powered way of working. If your company has already embraced AI, then you are halfway there. If AI hasn’t been introduced to the workforce you need to get started yesterday. Getting Started with Agentforce With all the buzz around Dreamforce, it’s no surprise that many organizations are eager to start using Agentforce. Fortunately, there are immediate opportunities to leverage these tools. The recommended approach is to begin with standard Agent actions, testing out-of-the-box features like opportunity summarization or creating close plans. From there, organizations can make incremental tweaks to customize actions for their specific needs. We have all come to expect that just as quickly as we include agentic ai into our processes and flows, Salesforce will add additional features and capabilities. As teams become more familiar with developing and deploying Agent actions, more complex use cases will become manageable, transforming the traditional point-and-click Salesforce experience into a more intelligent, agent-driven platform. Already I find myself asking, “is this an agent person or an ai-agent”? The day is coming, no doubt, when the question will be reversed. Tectonic’s AI Experts Can Help Interested in learning more about Agentforce or need guidance on getting started? Tectonic specializes in AI and analytics solutions within CRM, helping organizations unlock significant productivity gains through AI-based tools that optimize business processes. We are excited to enable you to enable Agentforce to Advance Copilot and Prompt Builder By Tectonic’s Solutions Architect, Shannan Hearne 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

September 20, 2024in AI Tools, Data, Einstein 1 Platform, Einstein Insights, Generative AI, Salesforce, Salesforce Einstein, Salesforce Implementation Services, Salesforce Marketing Automation, Salesforce Optimization

20Sep

Bye Klarna

Fintech firm Klarna is cutting ties with two major enterprise software providers, opting to automate its services using AI, and hints that more cuts could follow. Klarna co-founder and CEO Sebastian Siemiatkowski discussed the move during a recent conference call, as reported by Seeking Alpha. The company has already stopped using Salesforce, a platform that helps businesses manage sales and marketing data, and has also removed Workday, an HR and hiring platform, from its tech stack, according to a spokesperson from Klarna. This shift towards AI-driven automation is part of a larger strategy at Klarna. “We have multiple large-scale initiatives combining AI, standardization, and simplification that will allow us to eliminate several SaaS providers,” a company spokesperson said, though they did not specify which providers or services might be next. Founded in 2005, Klarna provides payment processing for e-commerce and reports over 150 million active users worldwide. Despite posting a net loss of $241 million last year—down from nearly $1 billion in 2022—the company reported a reduced loss of $32 million for the first half of 2024. With reports suggesting that Goldman Sachs has been tapped to underwrite Klarna’s potential IPO, the company’s focus on AI could strengthen its profitability prospects. This isn’t Klarna’s first AI initiative. Earlier in 2024, the company introduced an AI-powered customer service assistant in collaboration with OpenAI, which reportedly handled 2.3 million interactions in its first month and replaced the work of 700 agents. Klarna was among the early adopters of OpenAI’s enterprise ChatGPT package, and the company claims that 90% of its employees use the tool daily for process automation. Klarna’s decision to drop Salesforce and Workday is part of a broader effort to replace third-party SaaS solutions with internally developed applications, likely built on OpenAI’s infrastructure. Siemiatkowski stated in the August call, “We are shutting down a lot of our SaaS providers as we are able to consolidate.” However, not everyone is convinced. HR technology analyst Josh Bersin questioned whether Klarna could successfully replace a robust platform like Workday. “Workday systems have decades of workflows and complex data structures, including payroll and attendance,” he told Inc.. Bersin warned that developing an in-house system could lead Klarna into a “black hole of features,” with a poor user experience as a result. Many in the tech world share Bersin’s skepticism, with some suggesting the move is more of a PR tactic as Klarna gears up for its IPO. Investors and executives voiced doubts on social media, with financial insights account BuccoCapital posting on X, “Is it actually the best use of capital to rebuild in-house? Feels like a massive distraction,” while Ryan Jones, CEO of Flighty, called the move “free marketing.” Critics also point out that Klarna has downsized its workforce significantly, reducing its headcount by 1,200 over the past year, and Siemiatkowski has hinted at further reductions, suggesting the company could benefit from cutting staff from 3,800 to 2,000 employees. Siemiatkowski remains adamant that AI will allow Klarna to maintain growth despite these cuts. Bersin also noted that many tech giants have struggled to build their own HR platforms, citing examples like Google, which recently abandoned its internally developed HR software, and Amazon, which undergoes similar cycles regularly. “Microsoft,” Bersin added, “spends money on their own products but partners with SAP for HR software.” If Klarna does succeed in developing an in-house HR platform, it would be an achievement where even some of the biggest tech companies have fallen short. 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

September 20, 2024in Salesforce

19Sep

Salesforce AI Agents and New Data Cloud Features

Salesforce has announced several major updates to its Data Cloud at the annual Dreamforce conference, aimed at enhancing data analysis and boosting the capabilities of its new AI agents, Agentforce. These updates include support for unstructured data in audio and video formats, 50 new data connectors, and a low-latency pipeline to ensure faster AI responses. Salesforce describes Data Cloud as a customer data platform designed to unify data from various sources, providing actionable insights for sales, service, and marketing teams. The latest features will further improve this functionality, according to Rahul Auradkar, Salesforce’s General Manager of Unified Data Services and Einstein. Key Updates 1. Unstructured Data Support: Audio and Video Formats Salesforce has introduced the ability to process unstructured audio and video data. This allows enterprises to analyze information from sources like customer calls, training sessions, product demos, and webinars. The unstructured data can enrich customer profiles, provide deeper insights into behavior, and enhance the performance of AI agents within Agentforce by improving response accuracy. IDC’s Hayley Sutherland highlighted that vectorization of this content enables retrieval-augmented generation (RAG), enhancing the use of large language models (LLMs) to generate more effective and context-aware responses. 2. 50 New Data Connectors Salesforce has expanded its connector library with 50 new options, raising the total to 200. This helps businesses integrate diverse data sources, facilitating improved personalization and decision-making. These connectors are also critical for feeding data to AI agents, enabling them to generate more accurate insights. 3. Sub-second Data Layer for Real-time Responses A new sub-second data layer has been rolled out, allowing enterprises to ingest, unify, analyze, and act on data in real time. This layer powers Einstein Personalization, enabling instant AI-driven recommendations and analytics. It’s especially important for the low-latency needs of Agentforce’s autonomous AI agents, ensuring faster, more accurate customer interactions. 4. Governance and Security Enhancements Salesforce has introduced AI tagging and classification features for unstructured data, automating labeling and organization to ensure secure access. Policy-based governance will offer granular control over data access based on tags, metadata, and user attributes. Additionally, customer-managed encryption keys ensure data security, while the newly available private connect feature allows secure data sharing between Data Cloud and public cloud environments. 5. Data Cloud One and Hybrid Search Data Cloud One, a new feature, will allow enterprises to connect multiple Salesforce accounts across different departments or regions into a single instance, simplifying data management. The hybrid search feature, which combines vector and keyword search, is designed to improve the speed and accuracy of finding industry-specific information. Other updates include Tableau Semantics, which helps organize data based on its meaning, and a new data community platform to foster collaboration among data professionals. These updates reflect Salesforce’s commitment to enhancing data-driven decision-making and delivering powerful AI capabilities for enterprises. 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

September 19, 2024in Data, Enterprise, Salesforce

19Sep

xAI for Scientific Discovery

xAI: Advancing AI for Scientific Discovery xAI is dedicated to developing artificial intelligence that accelerates human scientific discovery, driven by a mission to enhance our understanding of the universe. Led by Elon Musk, CEO of Tesla and SpaceX, the xAI team comprises pioneers who have contributed to key advancements in AI, including the Adam optimizer, Batch Normalization, Layer Normalization, and the discovery of adversarial examples. Our team has introduced transformative technologies such as Transformer-XL, Autoformalization, the Memorizing Transformer, Batch Size Scaling, μTransfer, and SimCLR. These innovations have played crucial roles in breakthroughs like AlphaStar, AlphaCode, Inception, Minerva, GPT-3.5, and GPT-4. Dan Hendrycks, director of the Center for AI Safety, serves as an advisor to xAI. We also collaborate closely with X Corp to bring our AI technologies to over 500 million users of the X app. Timeline of Key Milestones – xAI for Scientific Discovery 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

September 19, 2024in Salesforce

18Sep

Pendo Salesforce and Productivity

Increasing productivity is an ongoing challenge for businesses. A recent McKinsey study reveals that over half of employees feel they are relatively unproductive at work. This widespread issue underscores a deeper problem. Historically, productivity was seen as a measure of individual employee performance—high performers versus low performers. But with the growing reliance on software for daily tasks, leading companies now view productivity differently. While employees remain essential, productivity increasingly hinges on process efficiency. To boost employee productivity, businesses need to focus on optimizing workflows and processes, ensuring they are well-designed, intuitive, and easy to execute. The key to achieving this? An intelligent digital adoption solution. Why optimizing processes boosts productivity Effective workflows are critical for achieving consistent service delivery, maintaining data integrity, enhancing efficiency, and supporting scalability. By streamlining processes, businesses can unlock productivity gains across the board. Pendo’s intelligent digital adoption platform integrates analytics, data, automation, and AI to drive optimized work outcomes, surpassing the capabilities of traditional digital adoption platforms (DAPs). Enhancing workflows with AI-powered analytics Pendo’s platform is designed to help employees work smarter by leveraging comprehensive analytics. For instance, revenue operations teams using Pendo can gain insights into how tasks are performed in Salesforce, identify common bottlenecks, and discover alternative approaches employees might take. The platform’s AI capabilities further enhance this process. Pendo’s machine learning model analyzes both quantitative and qualitative data from user feedback and analytics to offer AI-driven recommendations for workflow improvements. As the AI learns from user interactions, its suggestions become increasingly effective. Providing targeted support at the right time Understanding workflow challenges is only the first step. Employees may face different issues based on their roles or experience. For example, a customer success representative may require different support in Salesforce compared to a regional sales rep. Pendo’s platform can cater to these diverse needs by creating in-app guides and tooltips that offer tailored support based on user metadata and behavior. This allows high performers and superusers to work independently while providing targeted help where needed. Teams can monitor progress through Pendo Analytics, tracking metrics like workflow completion rates and time to completion. This data helps refine in-app guidance and enhance effective strategies. Empowering employees for greater achievement Industries such as media, retail, non-profit, and financial services often struggle with visibility into their sales processes and potential breakdowns. Pendo’s platform enables IT and product teams to identify operational gaps in software usage, leading to more satisfied, productive employees and improved business outcomes. As digital adoption advances, Pendo remains at the forefront of driving a smarter, more efficient future. Explore how Pendo can enhance workflow completion and efficiency in Salesforce. 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

September 18, 2024in Data, Salesforce, Technology

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