Machine Learning Archives - gettectonic.com

19Nov

How Does Salesforce Use AI

With all the buzz in the news about AI, it may feel like AI is everywhere. In fact, as of 2023, over 80% of global companies report adopting AI to enhance their business operations. This means if your company isn’t yet leveraging AI to strengthen customer relationships, you risk falling behind. The good news is that Salesforce CRM already comes with a suite of AI tools ready for use. In this insight, we’ll explore how combining quality data, AI, and Salesforce can help you build more meaningful, lasting relationships with your customers. How Does Salesforce Use AI? Salesforce offers various built-in functionalities to create customizable, predictive, and generative AI experiences tailored to your business needs. One standout tool is Agentforce, which enables the creation of autonomous AI agents. If you have numerous routine tasks but limited staff, Agentforce could be the solution. For instance, if you lack an in-house customer support agent, Agentforce can build an AI service agent to handle incoming cases, responding intuitively in real-time. Not enough sales reps? No problem—create an AI sales agent to manage records, interact with leads, answer questions, and schedule meetings. Another significant AI feature is generative AI in Salesforce. According to KPMG, 77% of executives believe generative AI will have a more profound societal impact in the next three to five years than any other emerging technology. So, how can it improve your business? Salesforce’s in-house LLM, xGen, helps you generate human-like text and create original visual content from existing data or user input. This capability can enhance user experiences by automating the generation of dynamic and personalized imagery for applications. Generative AI also transforms how users interact with and consume data. Complex datasets can now be converted into easily understandable formats—visualizations, charts, or graphs—generated from natural language prompts. These insights make data accessible, enabling users to share knowledge and drive informed decisions. How Can You Use AI to Improve Customer Relationships? AI is reshaping business models, workflows, and customer engagement. By harnessing quality data, AI, and Salesforce, you can enhance how you connect with customers. Here are key ways to leverage this combination for a smarter customer strategy: Challenges You May Encounter on Your AI Journey Adopting AI in Salesforce, especially Einstein AI, offers many benefits, but it also comes with challenges. Here are some factors to consider for a successful rollout: Importance of Data Quality When Using AI Analytics Data quality is essential for AI accuracy and reliability. Poor data can skew predictions and erode user trust. Key factors that contribute to high data quality include: AI can also enhance data quality by automating data validation and cleansing. Machine learning algorithms can detect and address anomalies, duplicate records, and incomplete datasets, improving the reliability of your data over time. The Future of CRM: AI-Driven Customer Engagement and Business Growth Integrating AI into Salesforce is revolutionizing CRM by enabling businesses to engage with customers more intelligently. From automating routine tasks to enhancing decision-making and delivering personalized communication, AI-driven innovations are empowering businesses to build stronger relationships with customers. As AI continues to evolve, those who embrace it will gain a competitive edge and drive long-term growth. The future of CRM is here—and it’s smarter, faster, and more customer-focused than ever. 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

November 19, 2024in AI Tools, Data, Generative AI, Salesforce, Technology

15Nov

LLM Economies

Throughout history, disruptive technologies have been the catalyst for major social and economic revolutions. The invention of the plow and irrigation systems 12,000 years ago sparked the Agricultural Revolution, while Johannes Gutenberg’s 15th-century printing press fueled the Protestant Reformation and helped propel Europe out of the Middle Ages into the Renaissance. In the 18th century, James Watt’s steam engine ushered in the Industrial Revolution. More recently, the internet has revolutionized communication, commerce, and information access, shrinking the world into a global village. Similarly, smartphones have transformed how people interact with their surroundings. Now, we stand at the dawn of the AI revolution. Large Language Models (LLMs) represent a monumental leap forward, with significant economic implications at both macro and micro levels. These models are reshaping global markets, driving new forms of currency, and creating a novel economic landscape. The reason LLMs are transforming industries and redefining economies is simple: they automate both routine and complex tasks that traditionally require human intelligence. They enhance decision-making processes, boost productivity, and facilitate cost reductions across various sectors. This enables organizations to allocate human resources toward more creative and strategic endeavors, resulting in the development of new products and services. From healthcare to finance to customer service, LLMs are creating new markets and driving AI-driven services like content generation and conversational assistants into the mainstream. To truly grasp the engine driving this new global economy, it’s essential to understand the inner workings of this disruptive technology. These posts will provide both a macro-level overview of the economic forces at play and a deep dive into the technical mechanics of LLMs, equipping you with a comprehensive understanding of the revolution happening now. Why Now? The Connection Between Language and Human Intelligence AI did not begin with ChatGPT’s arrival in November 2022. Many people were developing machine learning classification models in 1999, and the roots of AI go back even further. Artificial Intelligence was formally born in 1950, when Alan Turing—considered the father of theoretical computer science and famed for cracking the Nazi Enigma code during World War II—created the first formal definition of intelligence. This definition, known as the Turing Test, demonstrated the potential for machines to exhibit human-like intelligence through natural language conversations. The test involves a human evaluator who engages in conversations with both a human and a machine. If the evaluator cannot reliably distinguish between the two, the machine is considered to have passed the test. Remarkably, after 72 years of gradual AI development, ChatGPT simulated this very interaction, passing the Turing Test and igniting the current AI explosion. But why is language so closely tied to human intelligence, rather than, for example, vision? While 70% of our brain’s neurons are devoted to vision, OpenAI’s pioneering image generation model, DALL-E, did not trigger the same level of excitement as ChatGPT. The answer lies in the profound role language has played in human evolution. The Evolution of Language The development of language was the turning point in humanity’s rise to dominance on Earth. As Yuval Noah Harari points out in his book Sapiens: A Brief History of Humankind, it was the ability to gossip and discuss abstract concepts that set humans apart from other species. Complex communication, such as gossip, requires a shared, sophisticated language. Human language evolved from primitive cave signs to structured alphabets, which, along with grammar rules, created languages capable of expressing thousands of words. In today’s digital age, language has further evolved with the inclusion of emojis, and now with the advent of GenAI, tokens have become the latest cornerstone in this progression. These shifts highlight the extraordinary journey of human language, from simple symbols to intricate digital representations. In the next post, we will explore the intricacies of LLMs, focusing specifically on tokens. But before that, let’s delve into the economic forces shaping the LLM-driven world. The Forces Shaping the LLM Economy AI Giants in Competition Karl Marx and Friedrich Engels argued that those who control the means of production hold power. The tech giants of today understand that AI is the future means of production, and the race to dominate the LLM market is well underway. This competition is fierce, with industry leaders like OpenAI, Google, Microsoft, and Facebook battling for supremacy. New challengers such as Mistral (France), AI21 (Israel), and Elon Musk’s xAI and Anthropic are also entering the fray. The LLM industry is expanding exponentially, with billions of dollars of investment pouring in. For example, Anthropic has raised $4.5 billion from 43 investors, including major players like Amazon, Google, and Microsoft. The Scarcity of GPUs Just as Bitcoin mining requires vast computational resources, training LLMs demands immense computing power, driving a search for new energy sources. Microsoft’s recent investment in nuclear energy underscores this urgency. At the heart of LLM technology are Graphics Processing Units (GPUs), essential for powering deep neural networks. These GPUs have become scarce and expensive, adding to the competitive tension. Tokens: The New Currency of the LLM Economy Tokens are the currency driving the emerging AI economy. Just as money facilitates transactions in traditional markets, tokens are the foundation of LLM economics. But what exactly are tokens? Tokens are the basic units of text that LLMs process. They can be single characters, parts of words, or entire words. For example, the word “Oscar” might be split into two tokens, “os” and “car.” The performance of LLMs—quality, speed, and cost—hinges on how efficiently they generate these tokens. LLM providers price their services based on token usage, with different rates for input (prompt) and output (completion) tokens. As companies rely more on LLMs, especially for complex tasks like agentic applications, token usage will significantly impact operational costs. With fierce competition and the rise of open-source models like Llama-3.1, the cost of tokens is rapidly decreasing. For instance, OpenAI reduced its GPT-4 pricing by about 80% over the past year and a half. This trend enables companies to expand their portfolio of AI-powered products, further fueling the LLM economy. Context Windows: Expanding Capabilities

November 15, 2024in Artificial Intelligence, Google, Technology

12Nov

Enterprises are Adopting AI-powered Automation Platforms

The rapid pace of AI technological advancement is placing immense pressure on teams, often leading to disagreements due to the unrealistic expectations businesses have for the speed and agility of new technology implementation. A staggering 88% of IT professionals report that they are unable to keep up with the flood of AI-related requests within their organizations. Executives from UiPath, Salesforce, ServiceNow, and ManageEngine offer insights into how enterprises can navigate these challenges. Leading enterprises are adopting AI-powered automation platforms that understand, automate, and manage end-to-end processes. These platforms integrate seamlessly with existing enterprise technologies, using AI to reduce friction, eliminate inefficiencies, and enable teams to achieve business goals faster, with greater accuracy and efficiency. This year’s innovation drivers include tools such as Intelligent Document Processing, Communications Mining, Process and Task Mining, and Automated Testing. “Automation is the best path to deliver on AI’s potential, seamlessly integrating intelligence into daily operations, automating backend processes, upskilling employees, and revolutionizing industries,” says Mark Gibbs, EMEA President, UiPath. Jessica Constantinidis, Innovation Officer EMEA at ServiceNow, explains, “Intelligent Automation blends Robotic Process Automation (RPA), Artificial Intelligence (AI), and Machine Learning (ML) with well-defined processes to automate decision-making outcomes.” “Hyperautomation provides a business-driven, disciplined approach that enterprises can use to make informed decisions quickly by analyzing process and data feedback within the organization,” adds Constantinidis. Thierry Nicault, AVP and General Manager at Salesforce Middle East, emphasizes that while companies are eager to embrace AI, the pace of change often leads to confusion and stifles innovation. He notes, “By deploying AI and Hyperintelligent Automation tools, organizations can enhance productivity, visibility, and operational transformation.” Automation is driving growth and innovation across industries. AI-powered tools are simplifying processes, improving business revenues, and contributing to economic diversification. Ramprakash Ramamoorthy, Director of AI Research at ManageEngine, highlights how Hyperintelligent Automation, powered by AI, uses tools like Natural Language Processing (NLP) and Intelligent Document Processing to detect anomalies, forecast business trends, and empower decision-making. The IT Pushback Despite enthusiasm for AI, IT professionals are raising concerns. A Salesforce survey revealed that 88% of IT professionals feel overwhelmed by the influx of AI-related requests, with many citing resource constraints, data security concerns, and data quality issues. Business stakeholders often have unrealistic expectations about how quickly new technologies can be implemented, creating friction. According to Constantinidis of ServiceNow, many organizations lack transparency across their business units, making it difficult to fully understand their processes. As a result, automating processes becomes challenging. She adds, “Before full hyperautomation is possible, issues like data validation, classification, and privacy must be prioritized.” Automation platforms need accurate data, and governance is crucial in managing what data is used for AI models. “You need AI skills to teach and feed the data, and you also need a data specialist to clean up your data lake,” Constantinidis explains. Gibbs from UiPath stresses that automation must be designed in collaboration with the business users who understand the processes and systems. Once deployed, a feedback loop ensures continuous improvement and refinement of automated workflows. Ramamoorthy from ManageEngine notes that adopting Hyperintelligent Automation alongside existing workflows poses challenges. Enterprises must evaluate their technology stack, considering the costs, skills required, and the potential benefits. Strategic Integration of AI and Automation To successfully implement Hyperintelligent Automation tools, enterprises need a blend of IT and business skills. Mark Gibbs of UiPath points out, “These skills ensure organizations can effectively implement, manage, and optimize hyperintelligent technologies, aligning them with organizational goals.” Salesforce’s Nicault adds, “Enterprises must empower both IT and business teams to embrace AI, fostering innovation while ensuring the technology delivers real value.” Business skills are equally crucial, including strategic planning, process analysis, and change management. Ramamoorthy emphasizes that these competencies help identify automation opportunities and align them with business goals. According to Bassel Khachfeh, Digital Solutions Manager at Omnix, automation must be implemented with a focus on regulatory and compliance needs specific to the industry. This approach ensures the technology supports future growth and innovation. Transforming Customer Experiences and Business Operations As automation evolves, it’s transforming not only back-end processes but also customer experiences and decision-making at every level. Constantinidis from ServiceNow explains that hyperintelligence enables enterprises to predict outcomes and avert crises by trusting AI’s data accuracy. Gibbs from UiPath adds that automation allows enterprises to unlock untapped opportunities, speeding up the transformation of manual processes and enhancing business efficiency. AI is already making an impact in areas like supply chain management, regulatory compliance, and customer-facing processes. Ramamoorthy of ManageEngine notes that AI-powered NLP is revolutionizing enterprise chatbots and document processing, enabling businesses to automate complex workflows like invoice handling and sentiment analysis. Khachfeh from Omnix highlights how Cognitive Automation platforms elevate RPA by integrating AI-driven capabilities, such as NLP and Optical Character Recognition (OCR), to further streamline operations. Looking Ahead Hyperintelligent Automation, driven by AI, is set to revolutionize industries by enhancing efficiency, driving innovation, and enabling smarter decision-making. Enterprises that strategically adopt these tools—by integrating IT and business expertise, prioritizing data governance, and continuously refining their automated workflows—will be best positioned to navigate the complexities of AI and achieve sustainable growth. 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

November 12, 2024in Agentforce Platform, Artificial Intelligence, Data, Enterprise, Salesforce, Technology

11Nov

Healthcare Can Prioritize AI Governance

As artificial intelligence gains momentum in healthcare, it’s critical for health systems and related stakeholders to develop robust AI governance programs. AI’s potential to address challenges in administration, operations, and clinical care is drawing interest across the sector. As this technology evolves, the range of applications in healthcare will only broaden.

November 11, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Laws, Life Sciences Cloud, Salesforce, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Technology

10Nov

Insurance Brokerage Financial Services Cloud

Salesforce has introduced Financial Services Cloud for Insurance Brokerages, an AI-powered platform set to launch in February 2025, designed to automate and enhance client management, policy servicing, and commission processing for insurance brokerages. Built on Salesforce’s core CRM system, Insurance Brokerage Financial Services Cloud streamlines traditionally time-consuming tasks like policy renewals, employee benefits management, and commission splits, aiming to consolidate operations and reduce operational expenses.

November 10, 2024in Data, Enterprise, Salesforce Data Cloud, Technology

04Nov

Road for AI Regulation

The concept of artificial intelligence, or synthetic minds capable of thinking and reasoning like humans, has been around for centuries. Ancient cultures often expressed ideas and pursued goals similar to AI, and in the early 20th century, science fiction brought these notions to modern audiences. Works like The Wizard of Oz and films such as Metropolis resonated globally, laying the groundwork for contemporary AI discussions.

November 4, 2024in Generative AI

02Nov

Salesforce and Mahindra

The new LOS will incorporate machine learning and automation to deliver real-time credit assessments, enabling faster loan processing and competitive interest rates, alongside improved credit risk insights. This strategic partnership underscores Mahindra Finance’s dedication to providing responsible financing solutions to India’s emerging MSME sector.

November 2, 2024in Data, Enterprise, Salesforce, Salesforce Sales Cloud, Technology

01Nov

Being AI-Driven

Imagine a company where every decision, strategy, customer interaction, and routine task is enhanced by AI. From predictive analytics uncovering market insights to intelligent automation streamlining operations, this AI-driven enterprise represents what a successful business could look like. Does this company exist? Not yet, but the building blocks for creating it are already here. To envision a day in the life of such an AI enterprise, let’s fast forward to the year 2028 and visit Tectonic 5.0, a fictional 37-year-old mid-sized company in Oklahoma that provides home maintenance services. After years of steady sales and profit growth, the 2,300-employee company has hit a rough patch. Tectonic 5.0’s revenue grew just 3% last year, and its 8% operating margin is well below the industry benchmark. To jumpstart growth, Tectonic 5.0 has expanded its product portfolio and decided to break into the more lucrative commercial real estate market. But Tectonic 5.0 needs to act fast. The firm must quickly bring its new offerings to market while boosting profitability by eliminating inefficiencies and fostering collaboration across teams. To achieve these goals, Tectonic 5.0 is relying on artificial intelligence (AI). Here’s how each department at Tectonic 5.0 is using AI to reach these objectives. Spot Inefficiencies with AI With a renewed focus on cost-cutting, Tectonic 5.0 needed to identify and eliminate inefficiencies throughout the company. To assist in this effort, the company developed a tool called Jenny, an AI agent that’s automatically invited to all meetings. Always listening and analyzing, Jenny spots problems and inefficiencies that might otherwise go unnoticed. For example, Jenny compares internal data against industry benchmarks and historical data, identifying opportunities for optimization based on patterns in spending and resource allocation. Suggestions for cost-cutting can be offered in real time during meetings or shared later in a synthesized summary. AI can also analyze how meeting time is spent, revealing if too much time is wasted on non-essential issues and suggesting ways to have more constructive meetings. It does this by comparing meeting summaries against the company’s broader objectives. Tectonic 5.0’s leaders hope that by highlighting inefficiencies and communication gaps with Jenny’s help, employees will be more inclined to take action. In fact, it has already shown considerable promise, with employees being five times more likely to consider cost-cutting measures suggested by Penny. Market More Effectively with AI With cost management underway, Tectonic 5.0’s next step in its transformation is finding new revenue sources. The company has adopted a two-pronged approach: introducing a new lineup of products and services for homeowners, including smart home technology, sustainable living solutions like solar panels, and predictive maintenance on big-ticket systems like internet-connected HVACs; and expanding into commercial real estate maintenance. Smart home technology is exactly what homeowners are looking for, but Tectonic 5.0 needs to market it to the right customers, at the right time, and in the right way. A marketing platform with built-in AI capabilities is essential for spreading the word quickly and effectively about its new products. To start, the company segments its audience using generative AI, allowing marketers to ask the system, in natural language, to identify tech-savvy homeowners between the ages of 30 and 60 who have spent a certain amount on home maintenance in the last 18 months. This enables more precise audience targeting and helps marketing teams bring products to market faster. Previously, segmentation using legacy systems could take weeks, with marketing teams relying on tech teams for an audience breakdown. Now, Tectonic 5.0 is ready to reach out to its targeted customers. Using predictive AI, it can optimize personalized marketing campaigns. For example, it can determine which customers prefer to be contacted by text, email, or phone, the best time of day to reach out, and how often. The system also identifies which messaging—focused on cost savings, environmental impact, or preventative maintenance—will resonate most with each customer. This intelligence helps Tectonic 5.0 reach the optimal customer quickly in a way that speaks to their specific needs and concerns. AI also enables marketers to monitor campaign performance for red flags like decreasing open rates or click-through rates and take appropriate action. Sell More, and Faster, with AI With interested buyers lined up, it’s now up to the sales team to close deals. Generative AI for sales, integrated into CRM, can speed up and personalize the sales process for Tectonic 5.0 in several ways. First, it can generate email copy tailored to products and services that customers are interested in. Tectonic 5.0’s sales reps can prompt AI to draft solar panel prospecting emails. To maximize effectiveness, the system pulls customer info from the CRM, uncovering which emails have performed well in the past. Second, AI speeds up data analysis. Sales reps spend a significant amount of time generating, pulling, and analyzing data. Generative AI can act like a digital assistant, uncovering patterns and relationships in CRM data almost instantaneously, guiding Tectonic 5.0’s reps toward high-value deals most likely to close. Machine learning increases the accuracy of lead scoring, predicting which customers are most likely to buy based on historical data and predictive analytics. Provide Better Customer Service with AI Tectonic 5.0’s new initiatives are progressing well. Costs are starting to decrease, and sales of its new products are growing faster than expected. However, customer service calls are rising as well. Tectonic 5.0 is committed to maintaining excellent customer service, but smart home technology presents unique challenges. It’s more complex than analog systems, and customers often need help with setup and use, raising the stakes for Tectonic 5.0’s customer service team. The company knows that customers have many choices in home maintenance providers, and one bad experience could drive them to a competitor. Tectonic 5.0’s embedded AI-powered chatbots help deliver a consistent and delightful autonomous customer service experience across channels and touchpoints. Beyond answering common questions, these chatbots can greet customers, serve up knowledge articles, and even dispatch a field technician if needed. In the field, technicians can quickly diagnose and fix problems thanks to LLMs like xGen-Small, which

November 1, 2024in Generative AI

28Oct

AI and Disability

Dr. Johnathan Flowers of American University recently sparked a conversation on Bluesky regarding a statement from the organizers of NaNoWriMo, which endorsed the use of generative AI technologies, such as LLM chatbots, in this year’s event. Dr. Flowers expressed concern about the implication that AI assistance was necessary for accessibility, arguing that it could undermine the creativity and agency of individuals with disabilities. He believes that art often serves as a unique space where barriers imposed by disability can be transcended without relying on external help or engaging in forced intimacy. For Dr. Flowers, suggesting the need for AI support may inadvertently diminish the perceived capabilities of disabled and marginalized artists. Since the announcement, NaNoWriMo organizers have revised their stance in response to criticism, though much of the social media discussion has become unproductive. In earlier discussions, the author has explored the implications of generative AI in art, focusing on the human connection that art typically fosters, which AI-generated content may not fully replicate. However, they now wish to address the role of AI as a tool for accessibility. Not being personally affected by physical disability, the author approaches this topic from a social scientific perspective. They acknowledge that the views expressed are personal and not representative of any particular community or organization. Defining AI In a recent presentation, the author offered a new definition of AI, drawing from contemporary regulatory and policy discussions: AI: The application of specific forms of machine learning to perform tasks that would otherwise require human labor. This definition is intentionally broad, encompassing not just generative AI but also other machine learning applications aimed at automating tasks. AI as an Accessibility Tool AI has potential to enhance autonomy and independence for individuals with disabilities, paralleling technological advancements seen in fields like the Paris Paralympics. However, the author is keen to explore what unique benefits AI offers and what risks might arise. Benefits Risks AI and Disability The author acknowledges that this overview touches only on some key issues related to AI and disability. It is crucial for those working in machine learning to be aware of these dynamics, striving to balance benefits with potential risks and ensuring equitable access to technological advancements. 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

October 28, 2024in AI Tools, Data, Generative AI

26Oct

Data Quality Management Process

Data quality is often paradoxical—simple in its fundamentals, yet challenging in its details. A solid data quality management program is essential for ensuring processes run smoothly. What is Data Quality? At its core, data quality means having accurate, consistent, complete, and up-to-date data. However, quality is also context-dependent. Different tasks or applications require different types of data and, consequently, different standards of quality. Data that works well for one purpose may not be suitable for another. For instance, a list of customer names and addresses might be ideal for a marketing campaign but insufficient for tracking customer sales history. There isn’t a universal quality standard. A data set of credit card transactions, filled with cancellations and verification errors, may seem messy for sales analysis—but that’s exactly the kind of data the fraud analysis team wants to see. The most accurate way to assess data quality is to ask, “Is the data fit for its current purpose?” Steps to Build a Data Quality Management Process The goal of data quality management is not perfection. Instead, it focuses on ensuring reliable, high-quality data across the organization. Here are five key steps in developing a robust data quality process: Step 1: Data Quality Assessment Begin by assessing the current state of data. All relevant parties—from business units to IT—should understand the current condition of the organization’s data. Check for errors, duplicates, or missing entries and evaluate accuracy, consistency, and completeness. Techniques like data profiling can help identify data issues. This step forms the foundation for the rest of the process. Step 2: Develop a Data Quality Strategy Next, develop a strategy to improve and maintain data quality. This blueprint should define the use cases for data, the required quality for each, and the rules for data collection, storage, and processing. Choose the right tools and outline how to handle errors or discrepancies. This strategic plan will guide the organization toward sustained data quality. Step 3: Initial Data Cleansing This is where you take action to improve your data. Clean, correct, and prepare the data based on the issues identified during the assessment. Remove duplicates, fill in missing information, and resolve inconsistencies. The goal is to establish a strong baseline for future data quality efforts. Remember, data quality isn’t about perfection—it’s about making data fit for purpose. Step 4: Implement the Data Quality Strategy Now, put the plan into action by integrating data quality standards into daily workflows. Train teams on new practices and modify existing processes to include data quality checks. If done correctly, data quality management becomes a continuous, self-correcting process. Step 5: Monitor Data Quality Finally, monitor the ongoing process. Data quality management is not a one-time event; it requires continuous tracking and review. Regular audits, reports, and dashboards help ensure that data standards are maintained over time. In summary, an effective data quality process involves understanding current data, creating a plan for improvement, and consistently monitoring progress. The aim is not perfection, but ensuring data is fit for purpose. The Impact of AI and Machine Learning on Data Quality The rise of AI and machine learning (ML) brings new challenges to data quality management. For AI and ML, the quality of training data is crucial. The performance of models depends on the accuracy, completeness, and bias of the data used. If the training data is flawed, the model will produce flawed outcomes. Volume is another challenge. AI and ML models require vast amounts of data, and ensuring the quality of such large datasets can be a significant task. Organizations may need to prepare data specifically for AI and ML projects. This might involve collecting new data, transforming existing data, or augmenting it to meet the requirements of the models. Special attention must be paid to avoid bias and ensure diversity in the data. In some cases, existing data may not be sufficient or representative enough to meet future needs. Implementing specific validation checks for AI and ML training data is essential. This includes checking for bias, ensuring diversity, and verifying that the data accurately represents the problem the model is designed to address. By applying these practices, organizations can tackle the evolving challenges of data quality in the age of AI and machine learning. Create a great Data Quality Management Process. 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

October 26, 2024in Data

26Oct

AI FOMO

Enterprise interest in artificial intelligence has surged in the past two years, with boardroom discussions centered on how to capitalize on AI advancements before competitors do. Generative AI has been a particular focus for executives since the launch of ChatGPT in November 2022, followed by other major product releases like Amazon’s Bedrock, Google’s Gemini, Meta’s Llama, and a host of SaaS tools incorporating the technology. However, the initial rush driven by fear of missing out (FOMO) is beginning to fade. Business and tech leaders are now shifting their attention from experimentation to more practical concerns: How can AI generate revenue? This question will grow in importance as pilot AI projects move into production, raising expectations for financial returns. Using AI to Increase Revenue AI’s potential to drive revenue will be a critical factor in determining how quickly organizations adopt the technology and how willing they are to invest further. Here are 10 ways businesses can harness AI to boost revenue: 1. Boost Sales AI-powered virtual assistants and chatbots can help increase sales. For example, Ikea’s generative AI tool assists customers in designing their living spaces while shopping for furniture. Similarly, jewelry insurance company BriteCo launched a GenAI chatbot that reduced chat abandonment rates, leading to more successful customer interactions and potentially higher sales. A TechTarget survey revealed that AI-powered customer-facing tools like chatbots are among the top investments for IT leaders. 2. Reduce Customer Churn AI helps businesses retain clients, reducing revenue loss and improving customer lifetime value. By analyzing historical data, AI can profile customer attributes and identify accounts at risk of leaving. AI can then assist in personalizing customer experiences, decreasing churn and fostering loyalty. 3. Enhance Recommendation Engines AI algorithms can analyze customer data to offer personalized product recommendations. This drives cross-selling and upselling opportunities, boosting revenue. For instance, Meta’s AI-powered recommendation engine has increased user engagement across its platforms, attracting more advertisers. 4. Accelerate Marketing Strategies While marketing doesn’t directly generate revenue, it fuels the sales pipeline. Generative AI can quickly produce personalized content, such as newsletters and ads, tailored to customer interests. Gartner predicts that by 2025, 30% of outbound marketing messages will be AI-generated, up from less than 2% in 2022. 5. Detect Fraud AI is instrumental in detecting fraudulent activities, helping businesses preserve revenue. Financial firms like Capital One use machine learning to detect anomalies and prevent credit card fraud, while e-commerce companies leverage AI to flag fraudulent orders. 6. Reinvent Business Processes AI can transform entire business processes, unlocking new revenue streams. For example, Accenture’s 2024 report highlighted an insurance company that expects a 10% revenue boost after retooling its underwriting workflow with AI. In healthcare, AI could streamline revenue cycle management, speeding up reimbursement processes. 7. Develop New Products and Services AI accelerates product development, particularly in industries like pharmaceuticals, where it assists in drug discovery. AI tools also speed up the delivery of digital products, as seen with companies like Ally Financial and ServiceNow, which have reduced software development times by 20% or more. 8. Provide Predictive Maintenance AI-driven predictive maintenance helps prevent costly equipment downtime in industries like manufacturing and fleet management. By identifying equipment on the brink of failure, AI allows companies to schedule repairs and avoid revenue loss from operational disruptions. 9. Improve Forecasting AI’s predictive capabilities enhance planning and forecasting. By analyzing historical and real-time data, AI can predict product demand and customer behavior, enabling businesses to optimize inventory levels and ensure product availability for ready-to-buy customers. 10. Optimize Pricing AI can dynamically adjust prices based on factors like demand shifts and competitor pricing. Reinforcement learning algorithms allow businesses to optimize pricing in real time, ensuring they maximize revenue even as market conditions change. Keeping ROI in Focus While AI offers numerous ways to generate new revenue streams, it also introduces costs in development, infrastructure, and operations—some of which may not be immediately apparent. For instance, research from McKinsey & Company shows that GenAI models account for only 15% of a project’s total cost, with additional expenses related to change management and data preparation often overlooked. To make the most of AI, organizations should prioritize use cases with a clear return on investment (ROI) and postpone those that don’t justify the expense. A focus on ROI ensures that AI deployments align with business goals and contribute to sustainable revenue growth. 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

October 26, 2024in Generative AI

24Oct

Artificial Intelligence and Sales Cloud

Artificial Intelligence and Sales Cloud AI enhances the sales process at every stage, making it more efficient and effective. Salesforce’s AI technology—Einstein—streamlines data entry and offers predictive analysis, empowering sales teams to maximize every opportunity. Artificial Intelligence and Sales Cloud explained. Artificial Intelligence and Sales Cloud Sales Cloud integrates several AI-driven features powered by Einstein and machine learning. To get the most out of these tools, review which features align with your needs and check the licensing requirements for each one. Einstein and Data Usage in Sales Cloud Einstein thrives on data. To fully leverage its capabilities within Sales Cloud, consult the data usage table to understand which types of data Einstein features rely on. Setting Up Einstein Opportunity Scoring in Sales Cloud Einstein Opportunity Scoring, part of the Sales Cloud Einstein suite, is available to eligible customers at no additional cost. Simply activate Einstein, and the system will handle the rest, offering predictive insights to improve your sales pipeline. Managing Access to Einstein Features in Sales Cloud Sales Cloud users can access Einstein Opportunity Scoring through the Sales Cloud Einstein For Everyone permission set. Ensure the right team members have access by reviewing the permissions, features included, and how to manage assignments. Einstein Copilot Setup for Sales Einstein Copilot helps sales teams stay organized by guiding them through deal management, closing strategies, customer communications, and sales forecasting. Each Copilot action corresponds to specific topics designed to optimize the sales process. 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

October 24, 2024in Artificial Intelligence, Data, Salesforce, Salesforce Sales Cloud, Technology

23Oct

Python Losing the Crown

For years, Python has been synonymous with data science, thanks to its robust libraries like NumPy, Pandas, and scikit-learn. It’s long held the crown as the dominant programming language in the field. However, even the strongest kingdoms face threats. Python Losing the Crown. The whispers are growing louder: Is Python’s reign nearing its end? Before you fire up your Jupyter notebook to prove me wrong, let me clarify — Python is incredible and undeniably one of the greatest programming languages of all time. But no ruler is without flaws, and Python’s supremacy may not last forever. Here are five reasons why Python’s crown might be slipping. 1. Performance Bottlenecks: Python’s Achilles’ Heel Let’s address the obvious: Python is slow. Its interpreted nature makes it inherently less efficient than compiled languages like C++ or Java. Sure, libraries like NumPy and tools like Cython help mitigate these issues, but at its core, Python can’t match the raw speed of newer, more performance-oriented languages. Enter Julia and Rust, which are optimized for numerical computing and high-performance tasks. When working with massive, real-time datasets, Python’s performance bottlenecks become harder to ignore, prompting some developers to offload critical tasks to faster alternatives. 2. Python’s Memory Challenges Memory consumption is another area where Python struggles. Handling large datasets often pushes Python to its limits, especially in environments with constrained resources, such as edge computing or IoT. While tools like Dask can help manage memory more efficiently, these are often stopgap solutions rather than true fixes. Languages like Rust are gaining traction for their superior memory management, making them an attractive alternative for resource-limited scenarios. Picture running a Python-based machine learning model on a Raspberry Pi, only to have it crash due to memory overload. Frustrating, isn’t it? 3. The Rise of Domain-Specific Languages (DSLs) Python’s versatility has been both its strength and its weakness. As industries mature, many are turning to domain-specific languages tailored to their specific needs: Python may be the “jack of all trades,” but as the saying goes, it risks being the “master of none” compared to these specialized tools. 4. Python’s Simplicity: A Double-Edged Sword Python’s beginner-friendly syntax is one of its greatest strengths, but it can also create complacency. Its ease of use often means developers don’t delve into the deeper mechanics of algorithms or computing. Meanwhile, languages like Julia, designed for scientific computing, offer intuitive structures for advanced modeling while encouraging developers to engage with complex mathematical concepts. Python’s simplicity is like riding a bike with training wheels: it works, but it may not push you to grow as a developer. 5. AI-Specific Frameworks Are Gaining Ground Python has been the go-to language for AI, powering frameworks like TensorFlow, PyTorch, and Keras. But new challengers are emerging: As AI and machine learning evolve, these specialized frameworks could chip away at Python’s dominance. The Verdict: Python Losing the Crown? Python remains the Swiss Army knife of programming languages, especially in data science. However, its cracks are showing as new, specialized tools and faster languages emerge. The data science landscape is evolving, and Python must adapt or risk losing its crown. For now, Python is still king. But as history has shown, no throne is secure forever. The future belongs to those who innovate, and Python’s ability to evolve will determine whether it remains at the top. The throne of code is only as stable as the next breakthrough. 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

October 23, 2024in Data, Laws

21Oct

AI in Networking

AI Tools in Networking: Tailoring Capabilities to Unique Needs AI tools are becoming increasingly common across various industries, offering a wide range of functionalities. However, network engineers may not require every capability these tools provide. Each network has distinct requirements that align with specific business objectives, necessitating that network engineers and developers select AI toolsets tailored to their networks’ needs. While network teams often desire similar AI capabilities, they also encounter common challenges in integrating these tools into their systems. The Rise of AI in Networking Though AI is not a new concept—having existed for decades in the form of automated and expert systems—it is gaining unprecedented attention. According to Jim Frey, principal analyst for networking at TechTarget’s Enterprise Strategy Group, many organizations have not fully grasped AI’s potential in production environments over the past three years. “AI has been around for a long time, but the interesting thing is, only a minority—not even half—have really said they’re using it effectively in production for the last three years,” Frey noted. Generative AI (GenAI) has significantly contributed to this renewed interest in AI. Shamus McGillicuddy, vice president of research at Enterprise Management Associates, categorizes AI tools into two main types: GenAI and AIOps (AI for IT operations). “Generative AI, like ChatGPT, has recently surged in popularity, becoming a focal point of discussion among IT professionals,” McGillicuddy explained. “AIOps, on the other hand, encompasses machine learning, anomaly detection, and analytics.” The increasing complexity of networks is another factor driving the adoption of AI in networking. Frey highlighted that the demands of modern network environments are beyond human capability to manage manually, making AI engines a vital solution. Essential AI Tool Capabilities for Networks While individual network needs vary, many network engineers seek similar functionalities when integrating AI. Commonly desired capabilities include: According to McGillicuddy’s research, network optimization and automated troubleshooting are among the most popular use cases for AI. However, many professionals prefer to retain manual oversight in the fixing process. “Automated troubleshooting can identify and analyze issues, but typically, people want to approve the proposed fixes,” McGillicuddy stated. Many of these capabilities are critical for enhancing security and mitigating threats. Frey emphasized that networking professionals increasingly view AI as a tool to improve organizational security. DeCarlo echoed this sentiment, noting that network managers share similar objectives with security professionals regarding proactive problem recognition. Frey also mentioned alternative use cases for AI, such as documentation and change recommendations, which, while less popular, can offer significant value to network teams. Ultimately, the relevance of any AI capability hinges on its fit within the network environment and team needs. “I don’t think you can prioritize one capability over another,” DeCarlo remarked. “It depends on the tools being used and their effectiveness.” Generative AI: A New Frontier Despite its recent emergence, GenAI has quickly become an asset in the networking field. McGillicuddy noted that in the past year and a half, network professionals have adopted GenAI tools, with ChatGPT being one of the most recognized examples. “One user reported that leveraging ChatGPT could reduce a task that typically takes four hours down to just 10 minutes,” McGillicuddy said. However, he cautioned that users must understand the limitations of GenAI, as mistakes can occur. “There’s a risk of errors or ‘hallucinations’ with these tools, and having blind faith in their outputs can lead to significant network issues,” he warned. In addition to ChatGPT, vendors are developing GenAI interfaces for their products, including virtual assistants. According to McGillicuddy’s findings, common use cases for vendor GenAI products include: DeCarlo added that GenAI tools offer valuable training capabilities due to their rapid processing speeds and in-depth analysis, which can expedite knowledge acquisition within the network. Frey highlighted that GenAI’s rise is attributed to its ability to outperform older systems lacking sophistication. Nevertheless, the complexity of GenAI infrastructures has led to a demand for AIOps tools to manage these systems effectively. “We won’t be able to manage GenAI infrastructures without the support of AI tools, as human capabilities cannot keep pace with rapid changes,” Frey asserted. Challenges in Implementing AI Tools While AI tools present significant benefits for networks, network engineers and managers must navigate several challenges before integration. Data Privacy, Collection, and Quality Data usage remains a critical concern for organizations considering AIOps and GenAI tools. Frey noted that the diverse nature of network data—combining operational information with personally identifiable information—heightens data privacy concerns. For GenAI, McGillicuddy pointed out the importance of validating AI outputs and ensuring high-quality data is utilized for training. “If you feed poor data to a generative AI tool, it will struggle to accurately understand your network,” he explained. Complexity of AI Tools Frey and McGillicuddy agreed that the complexity of both AI and network systems could hinder effective deployment. Frey mentioned that AI systems, especially GenAI, require careful tuning and strong recommendations to minimize inaccuracies. McGillicuddy added that intricate network infrastructures, particularly those involving multiple vendors, could limit the effectiveness of AIOps components, which are often specialized for specific systems. User Uptake and Skills Gaps User adoption of AI tools poses a significant challenge. Proper training is essential to realize the full benefits of AI in networking. Some network professionals may be resistant to using AI, while others may lack the knowledge to integrate these tools effectively. McGillicuddy noted that AIOps tools are often less intuitive than GenAI, necessitating a certain level of expertise for users to extract value. “Understanding how tools function and identifying potential gaps can be challenging,” DeCarlo added. The learning curve can be steep, particularly for teams accustomed to longstanding tools. Integration Issues Integration challenges can further complicate user adoption. McGillicuddy highlighted two dimensions of this issue: tools and processes. On the tools side, concerns arise about harmonizing GenAI with existing systems. “On the process side, it’s crucial to ensure that teams utilize these tools effectively,” he said. DeCarlo cautioned that organizations might need to create in-house supplemental tools to bridge integration gaps, complicating the synchronization of vendor AI

October 21, 2024in AI Tools, Data, Enterprise, Generative AI

21Oct

Google on Google AI

As a leading cloud provider, Google Cloud is also a major player in the generative AI market. Google on Google AI provides insights into this new tool. In the past two years, Google has been in a competitive battle with AWS, Microsoft, and OpenAI to gain dominance in the generative AI space. Recently, Google introduced several generative Artificial Intelligence products, including its flagship large language model, Gemini, and the Vertex AI Model Garden. Last week, it also unveiled Audio Overview, a tool that transforms documents into audio discussions. Despite these advancements, Google has faced criticism for lagging in some areas, such as issues with its initial image generation tool, like X’s Grok. However, the company remains committed to driving progress in generative AI. Google’s strategy focuses not only on delivering its proprietary models but also offering a broad selection of third-party models through its Model Garden. Google’s Thoughts on Google AI Warren Barkley, head of product for Google Cloud’s Vertex AI, GenAI, and machine learning, emphasized this approach in a recent episode of the Targeting AI podcast. He noted that a key part of Google’s ongoing effort is ensuring users can easily transition to more advanced models. “A lot of what we did in the early days, and we continue to do now, is make it easy for people to move to the next generation,” Barkley said. “The models we built 18 months ago are a shadow of what we have today. So, providing pathways for people to upgrade and stay on the cutting edge is critical.” Google is also focused on helping users select the right AI models for specific applications. With over 100 closed and open models available in the Model Garden, evaluating them can be challenging for customers. To address this, Google introduced evaluation tools that allow users to test prompts and compare model responses. In addition, Google is exploring advancements in Artificial Intelligence reasoning, which it views as crucial to driving the future of generative AI. 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

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