[{"_id":"project-settings","settings":{"translateMetaTags":true,"translateAriaLabels":false,"translateTitle":false,"showWidget":true,"isFeedbackEnabled":false,"fv":1,"customWidget":{"theme":"custom","font":"rgb(255,255,255)","header":"rgb(30,106,160)","background":"rgba(0,47,86,1)","position":"left","positionVertical":"bottom","border":"rgb(0,0,0)","borderRequired":false,"widgetCompact":true,"isWidgetPositionRelative":false},"widgetLanguages":[],"activeLanguages":{"es-LA":"Español (América Latina)","fr":"Français","zh-Hans":"中文","pt-BR":"Português (Brasil)","de":"Deutsch","ar":"العربية","ja":"日本語","ru":"Русский","it":"Italiano","tr":"Türkçe","th":"ไทย","vi":"Tiếng Việt","ko":"한국어","pl":"Polski","en":"English"},"enabledLanguages":["ar","de","en","es-LA","fr","it","ja","ko","pl","pt-BR","ru","th","tr","vi","zh-Hans"],"debugInfo":false,"displayBranding":true,"displayBrandingName":false,"localizeImages":false,"localizeUrls":false,"localizeImagesLimit":false,"localizeUrlsLimit":false,"localizeAudio":false,"localizeAudioLimit":false,"localizeDates":false,"disabledPages":[],"regexPhrases":[{"phrase":"#Showing 1-48 of <var total_pages=\"\"></var> items","candidate":"#Showing 1-48 of ","variables":["<var total_pages=\"\"></var>"],"regex":"^#Showing 1\\-48 of ([\\d ]{4,}) items$"},{"phrase":"#<var page1=\"\"></var> - <var page2=\"\"></var> of <var page3=\"\"></var>","candidate":"#","variables":["<var page1=\"\"></var>","<var page2=\"\"></var>","<var page3=\"\"></var>"],"regex":"^#([\\d ]{1,3}) \\- ([\\d ]{1,3}) of ([\\d ]{1,5})$"},{"phrase":"#<var results=\"\"></var> Results found for \"<var quotes=\"\"></var>\"","candidate":"#","variables":["<var results=\"\"></var>","<var quotes=\"\"></var>"],"regex":"^#([\\d ]{1,}) Results found for \"(.{1,})\"$"},{"phrase":"#<var num-hours=\"\"></var> hours ago","candidate":"#","variables":["<var num-hours=\"\"></var>"],"regex":"^#([\\d ]+) hours ago$"},{"phrase":"#(<var hour1=\"\"></var>:<var min1=\"\"></var> minutes)","candidate":"#(","variables":["<var hour1=\"\"></var>","<var min1=\"\"></var>"],"regex":"^#\\(([\\d ]{1,2}):([\\d ]{1,2}) minutes\\)$"},{"phrase":"#You are signed in as <var signed_in_as=\"\"></var>","candidate":"#You are signed in as ","variables":["<var signed_in_as=\"\"></var>"],"regex":"^#You are signed in as (.+?)$"},{"phrase":"#<var hour=\"\"></var>:<var minutes=\"\"></var> minutes","candidate":"#","variables":["<var hour=\"\"></var>","<var minutes=\"\"></var>"],"regex":"^#([\\d ]{1,2}):([\\d ]{1,2}) minutes$"}],"allowComplexCssSelectors":false,"blockedClasses":false,"blockedIds":false,"phraseDetection":true,"customDomainSettings":[],"seoSetting":[],"translateSource":false,"overage":false,"detectPhraseFromAllLanguage":false,"googleAnalytics":false,"mixpanel":false,"heap":false,"disableDateLocalization":false,"ignoreCurrencyInTranslation":false,"blockedComplexSelectors":[]},"version":196694},{"_id":"en","source":"en","pluralFn":"return n != 1 ? 1 : 0;","pluralForm":2,"dictionary":{},"version":196694},{"_id":"outdated","outdated":{"#he441650@uaeh.edu.mx":1,"#he441650@uahe.edu.mx":1,"#ISBN: 9781422144114":1,"#by Karen Berman, Joe Knight":1,"#financial-intelligence":1,"#What...":1,"#Karen Berman<span ljsid-1=\"\">, </span>":1,"#Real-Time Retrieval Enterprise-Wide":1,"#These modern job aids are no longer static references. They are dynamic, AI-enabled search tools embedded into daily workflows, helping employees complete tasks, access information, and respond more effectively in real time. This represents a significant shift—from memorizing information to retrieving it instantly. Employees no longer need to retain every detail; instead, they need immediate, context-aware support on demand.":1,"#These modern job aids are no longer static references. They are dynamic, AI-enabled search tools embedded into daily workflows, helping employees complete tasks, access information, and respond more effectively in real time. This represents a significant shift—from memorizing information to retrieving it instantly....":1,"#Over the past year, we have seen a meaningful shift in how workplace learning and support tools are evolving. Historically, employees relied heavily on memory and periodic reference materials to do their jobs. Today, organizations increasingly are adopting artificial intelligence-powered knowledge tools that place information at employees' fingertips—or even serve it proactively—making memorization far less critical than before.":1,"#by Yulia Barnakova":1,"#Employees No Longer Need to Retain Every Detail; Instead, They Need Immediate, Context-aware Support on Demand.":1,"#Copyright© of Training is the property of Lakewood Media Group LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.":1,"#The shift from memorization to retrieval is redefining how L&D drives impact. It is no longer just about designing content but about enabling real-time performance through intelligent, embedded support. Learning professionals are helping structure knowledge for AI search tools, refining content to reflect real-world tasks, and ensuring employees can access critical information without delay. Those who lean into this transition will help their organizations truly perform better and faster, as well as stay agile for whatever comes next.":1,"#L&D’s Changing Role":1,"#For Learning professionals, this evolution calls for both upskilling and reframing their role. Beyond designing learning programs, they are curating knowledge for both employees and AI, working with subject matter experts to codify expertise and help build the knowledge bases that support tools such as bots and copilots. Leading teams are embracing this work as knowledge stewardship, ensuring institutional knowledge is captured, searchable, and sustained. In doing so, they contribute to what many call a \"digital brain\"—a centralized knowledge ecosystem that underpins AI tools and supports performance at scale.":1,"#Still, not all learning can be retrieved on demand. Skills such as communication, empathy, and decision-making require development through practice. Many organizations are addressing this through AI-powered role-plays and simulations—tools that scale one-on-one coaching and give learners space to practice and build confidence using natural, conversational language.":1,"#Learning teams are starting to incorporate these new job aids into their learner support tools and experiences. Rather than frontloading every detail during training or sending employees to lengthy manuals, these types of job aids reduce the pressure to remember and make knowledge recall seamless. This shift also is creating a new training need—teaching employees how to work effectively with these systems. Skills such as asking clear questions, refining searches, and interpreting AI-generated suggestions are becoming core competencies.":1,"#Teaching Employees to Learn the Tools":1,"#This kind of real-time retrieval also is expanding across the enterprise. Sales teams now use it to surface product knowledge quickly. HR teams use it to help answer policy questions quickly. Field technicians pull up troubleshooting guidance on-site. At Accenture, we also use various knowledge bots connected across repositories and datasets to help access information fast—whether it was covered in training but forgotten, needed unexpectedly, or is simply new learning for a role. This saves tremendous time spent memorizing and hunting for knowledge sources when needed.":1,"#In call centers, for example, AI copilots listen to conversations live, suggesting responses, surfacing relevant product details, and automating notes. Most platforms now include these tools, and new AI-native systems are emerging. The result is faster onboarding for agents and less time spent remembering or asking others to clarify policies during live customer interactions.":1,"#Clicking this link will redirect to relevant products for the Author Yulia Barnakova.":1,"#ISBN: 978-1-39423-362-5":1,"#<strong ljsid-0=\"\"><a ljsid-0-0=\"\">Wilhelm Bielert</a></strong> is a distinguished figure in the world of digital transformation, with more than two decades of significant contributions. Since 2022, he’s served as the senior vice president and chief information officer of the Canadian multinational company Premier Tech and has been instrumental in guiding its digital direction. His prior roles include vice president operations and chief digital officer, and he played a key role in founding Premier Tech Digital. Bielert has been a lecturer in digital transformation at the University of Hamburg since 2014. Named a top 25 global Industrial Internet of Things thought leader by CBT in 2021, he seamlessly merges academic wisdom with innovative business strategies.":1,"#©2024 by Michael Proksch, Nisha Paliwal, and Wilhem Bielert":1,"#<strong ljsid-0=\"\"><a ljsid-0-0=\"\">Nisha Paliwal</a></strong> is a visionary technologist and a passionate advocate for human-centered thinking. With more than 20 years of experience in leveraging technology and data insights to create true business value, she’s established herself as a trailblazer in the tech industry. Paliwal has held various leadership roles in top financial institutions. She’s extremely passionate about democratizing mentoring and dedicates her time to several nonprofit companies in a mission to drive female representation in the tech industry.":1,"#<strong ljsid-0=\"\"><a ljsid-0-0=\"\">Michael Proksch</a></strong> is a highly accomplished expert and industry leader, celebrated for his remarkable ability to generate substantial business value with AI across a broad spectrum of organizations. His expertise spans from agile start-ups to multinational Fortune 2000 corporations. Proksch is widely respected for his deliberate, holistic, systematic, and forward-looking approach to driving successful AI implementations, drawing from his profound knowledge of AI strategy and operations.":1,"#Acquiring forward-thinking, innovative talent is important for building the right teams to run AI projects. Beyond the necessary technical competencies for the role, talent should be evaluated for soft skills like critical thinking, communication, and trust building. The current work environment for data management roles is often stressful and poorly defined, which leads to high turnover and project inefficiency. Companies must clearly define roles, offer competitive salaries, and cultivate an AI-friendly workplace to acquire and retain top talent.":1,"#Data governance frameworks define strategies for organizing data, adhering to regulations, and maintaining security protocols. Investing in data governance is essential to ensuring data confidentiality, integrity, and availability across your organization. Additionally, be sure to establish sufficient data infrastructure, including data storage, cleaning, and deletion processes.":1,"#AI achievers spend a significant 35 percent of their AI project budget on data management, including all data sourcing, governance, and preparation functions. Data can be sourced in two forms: <em ljsid-1=\"\">Primary data</em> is collected for a specific AI use, while <em ljsid-3=\"\">secondary data</em> is obtained from another organization, like an academic lab. Primary data is ideal for AI training, but it’s often difficult to obtain and unstructured.":1,"#Data Management":1,"#AI systems often have significant hardware requirements, so ensure that your hardware is sufficient to efficiently run your algorithms. Specifically, large training datasets and generative AI tools require numerous central and graphic processing units to run effectively.":1,"#<em ljsid-0=\"\">AI algorithm development</em>. Prioritize data preparation, model training, and feature selection in development.":1,"#<em ljsid-0=\"\">AI algorithm deployment</em>. Identify the right deployment method by analyzing processing capacity, user experience, and speed.":1,"#by Nisha Paliwal, Wilhem Bielert, Michael Proksch":1,"#<em ljsid-0=\"\">AI algorithm management</em>. Monitor your AI systems for biases, security vulnerabilities, and efficiency.":1,"#<em ljsid-0=\"\">AI output consumption</em>. Determine how you want to visualize AI outputs and integrate them with other systems.":1,"#Having the proper software and hardware in place before you begin an AI project minimizes setbacks and supports efficient development. The following elements are necessary considerations for any AI development environment:":1,"#Part IV: Required Capabilities":1,"#Overcoming negative assumptions about AI and training employees to work in synergy with it requires a diligent effort to communicate a vision, remove barriers, and institutionalize new changes. By investing in an AI-friendly culture, you create momentum toward your goal and lay the foundation for future AI projects.":1,"#With over 90 percent of Americans believing that AI has a negative impact on society, companies must make a concerted effort to create an AI-friendly culture. The most successful AI achievers have three core cultural values: data-driven thinking, curiosity-fueled innovation, and collaboration. Employees must have a foundational understanding of data, feel comfortable making mistakes, and trust their management team for large AI projects to succeed.":1,"#Cultivating an AI-friendly Culture":1,"#<em ljsid-0=\"\">Reinforcement</em>. Sustain the change by celebrating success and addressing setbacks.":1,"#<em ljsid-0=\"\">Ability</em>. Train and coach others to develop the skills necessary to achieve the change.":1,"#<em ljsid-0=\"\">Knowledge</em>. Provide training and educational resources to stakeholders.":1,"#A Practical Guide to Business Value Creation with Artificial Intelligence from Strategy to Execution":1,"#<em ljsid-0=\"\">Desire</em>. Foster motivation across the organization to engage with the initiative.":1,"#<em ljsid-0=\"\">Awareness</em>. Spread information about why the change is necessary and what value it provides to all stakeholders.":1,"#In addition to project management skills, change management skills are essential for leaders to adapt to shifting priorities and technologies. The following model provides a framework for handling change across projects:":1,"#Leading an AI integration project begins with understanding project management fundamentals and developing a long-term strategic framework. It can be difficult to add an AI project into an existing workflow, as AI projects have a higher chance of falling behind schedule and going over budget. Prioritizing data management, stakeholder communication, and continuous support after the project is complete is key to sustaining successful AI projects.":1,"#Leading Successful Projects":1,"#AI strategies must be driven from the top of the organizational hierarchy to ensure consistent funding, support, and management. As technologies evolve and industries transform, ensure that your strategic framework is flexible enough to adapt properly.":1,"#<em ljsid-0=\"\">Management systems</em>. Put a management team in place to oversee the integration.":1,"#<em ljsid-0=\"\">Capabilities</em>. Analyze the resources you’ll need to fulfill your aspiration.":1,"#<em ljsid-0=\"\">Value creation</em>. Establish how AI will create value within your business opportunities.":1,"#<em ljsid-0=\"\">Focus</em>. Determine the specific business opportunities you want to explore.":1,"#Clicking this link will redirect to relevant products for the Authors Michael Proksch.":1,"#<em ljsid-0=\"\">Aspiration</em>. Define the finish line for all stakeholders.":1,"#Implementing AI requires a close alignment between a defined AI strategy and an overall enterprise strategy. A five-step strategic framework can help establish clear AI direction in your business:":1,"#Crafting an AI Strategy":1,"#Part III: Enterprise Integration":1,"#Highlighting AI limitations is an important step to alleviating fears of stakeholders wary about unsustainable AI acceleration. AI decision-making is limited in <em ljsid-1=\"\">generalizability</em>, or its ability to contextualize new data that isn’t a part of its training. Humans have high generalizability, which emphasizes the importance of human-AI collaboration. Additionally, simplifying AI algorithms can decrease data costs, increase accuracy, and improve transparency.":1,"#The rapid acceleration of AI raises concerns about its decision-making capabilities, especially when uncontrolled by a human. Some AI algorithms are called <em ljsid-1=\"\">black boxes</em> because they can’t be easily understood by a human. AI decision-making must be explained clearly within its context to all necessary stakeholders to ensure transparency.":1,"#Managing AI’s Decision-making":1,"#An important motivator to stakeholders is <em ljsid-1=\"\">benevolence</em>, or an AI project’s ability to positively impact the world outside of profit generation. Collaborating with academic research labs, environmental initiatives, and other public good sectors is key to establishing trust in AI.":1,"#A 2019 Deloitte study reported that 67 percent of high-level executives don’t use data from their own organization’s tools. As AI continues to transform industries, it’s critical for companies to earn stakeholder trust across their organizations. Many leaders lean on AI transparency, explainability, and bias mitigation to earn stakeholder trust, but these features must be paired with nontechnical motivations to be successful.":1,"#Michael Proksch":1,"#These strategies require large amounts of resources and data to function, but you can still increase actionability with low resources by disaggregating your value chains. For example, instead of analyzing total return on investment (ROI) for an advertising campaign, analyze the ROI of each advertising medium separately to determine where the limited resources should be spent.":1,"#An <em ljsid-1=\"\">actionable insight</em> is a suggestion based on an AI system that can improve a value chain. Selecting a low-risk credit card applicant is an actionable insight that uses <em ljsid-3=\"\">output-based selection</em> to make choices based on comparing potential outputs. If output comparison isn’t possible, the <em ljsid-5=\"\">output adaptation</em> strategy works by comparing the effect of different business actions on a single output. A third strategy is <em ljsid-7=\"\">AI automation</em>, or outputting actions instead of insights, like how an autonomous car inputs sensory data and outputs a turning action.":1,"#Creating Actionable Insights":1,"#A <em ljsid-1=\"\">return on data assets (RODA)</em> is a formula that divides total income from data assets by the costs to maintain those assets. A high RODA value indicates that further resources should be allocated to manage that data, while a low RODA value indicates that the data is less valuable. Comparing RODA values allows companies to both evaluate data value and make strategic data decisions.":1,"#Data has always been a major value contributor to companies because of its prediction and pattern recognition uses, but the revolution of AI creates an increased demand for data collection, storage, and analysis systems. Understanding the value of data begins with an assessment of how completely and accurately it represents its context. Larger datasets that account for more variables allow for more complex AI decisions.":1,"#Collecting Valuable Data":1,"#About $3.1 trillion of value is lost annually because of poor AI data quality. AI-enabled processes must have data protocols in place that ensure clean, context-specific data to ensure that models are sustainable and reliable. Additionally, AI strategies must contain an understanding of stakeholder motivation to determine how to mitigate adoption risk. Customers may be wary about discrimination in models, employees could fear job insecurity, and leaders might be concerned about ceding power to autonomous processes.":1,"#Developing business domain knowledge is essential to determining where, how, and when to implement AI into processes. AI achievers differentiate themselves from competitors by better understanding their industries, customers, and <em ljsid-1=\"\">value chains</em>, which are their value-generating processes. Business domain knowledge is necessary to determine which AI algorithms will produce the best analytics and which can illuminate hidden patterns and provide insights to increase value chain efficiency.":1,"#AI-centric Elements":1,"#Part II: Overcoming Value Challenges":1,"#Clicking this link will redirect to relevant products for the Authors Wilhem Bielert.":1,"#<em ljsid-0=\"\">AI products and services</em> refers to a product or service that increases in value via AI integration. For example, customers are willing to spend 24 percent more on a vehicle with autonomous driving capability, so integrating AI into a product can significantly increase its perceived value.":1,"#<em ljsid-0=\"\">Decision-making automation</em> is the process of active decision-making by AI. Automatic systems can act on decisions with increased speed and efficiency, which can improve processes like stock trading and credit card application approval.":1,"#<em ljsid-0=\"\">Decision-making augmentation</em> has a more direct impact on a process through the AI generation of actionable insights. With human oversight, AI can help break past human biases and limited cognition to suggest new paths forward.":1,"#<em ljsid-0=\"\">Process optimization</em> is the practice of increasing efficiency in a multistep process like a supply chain. AI can analyze data at different stages within a process to determine an optimal flow.":1,"#There are four primary methods of AI value generation, and determining which aligns best with your business is key to successfully integrating AI:":1,"#Four Types of AI Value Creation":1,"#AI business opportunities can be explored by examining critical performance indicators with quantifiable financial impacts like customer retention and satisfaction. By focusing on financial impacts, it’s easier to obtain funding and develop a strategic framework for an AI project. However, new opportunities must be weighed against risks. <em ljsid-1=\"\">Feasibility risk</em> is the management capability to acquire, use, and maintain AI systems, which must be considered when determining a project’s feasibility. <em ljsid-3=\"\">Adoption risk</em> is represented by the willingness of stakeholders to agree with an AI solution. Employees, customers, and leadership must be aligned on an AI framework to ensure that stakeholder value remains high.":1,"#Management consulting company McKinsey estimates that AI will add $13 trillion of value to the global economy. It’s essential for companies to understand how AI business opportunities and risks weigh against each other to determine how to tap into this expected new value.":1,"#Three Factors of AI Business Value Creation":1,"#John Deere demonstrated a concept for autonomous farming machines in 2013 that could analyze soil health, pests, and crop growth. The company invested heavily in data analytics and robotics while staying flexible as new technologies emerged. In 2022, John Deere unveiled a self-driving tractor powered by AI algorithms to process complex terrain in real time. Today, companies may feel the urge to quickly adopt AI systems, but those like John Deere display the necessary focus on long-term solutions to truly overcome human limitations.":1,"#Wilhem Bielert":1,"#John Deere demonstrated a concept for autonomous farming machines in 2013 that could analyze soil health, pests, and crop growth. The company invested heavily in data analytics and robotics while staying flexible as new technologies emerged. In 2022, John Deere unveiled a self-driving tractor powered by AI algorithms...":1,"#The most successful achievers in artificial intelligence (AI) have been those who created sustainable value for their businesses by purposeful AI integration. Companies like Amazon, Coca-Cola, and John Deere have altered their technologies, mindsets, and cultures to adopt AI and stay focused on difficult goals.":1,"#The Journey of AI Achievers":1,"#Part I: Value Creation Potential":1,"#Foster a collaborative, AI-friendly culture to ensure sustained AI project success.":1,"#Build stakeholder trust by setting a defined goal, explaining complex processes, and demonstrating value generation.":1,"#Maintain transparent data collection, governance, and security protocols to mitigate risk.":1,"#Focus on applying AI to business opportunities that directly increase value.":1,"#Expanding business domain knowledge is key to identifying artificial intelligence (AI) integration opportunities.":1,"#In <strong ljsid-1=\"\">The Secrets of AI Value Creation</strong>, Michael Proksch, Nisha Paliwal, and Wilhem Bielert describe how to leverage artificial intelligence (AI) to generate sustainable value in current and future business functions. As companies rush to tap into AI’s value potential, it becomes increasingly critical for leaders to strategically align business interests with AI systems. By investing in data management systems, identifying AI-related business opportunities, and aligning stakeholder motivations, companies can position themselves as leaders in the AI era.":1,"#Clicking this link will redirect to relevant products for the Authors Nisha Paliwal.":1,"#Nisha Paliwal":1,"#The combination of AI’s scalable data processing, predictive analysis, and insight generation allows a future of rapid scientific advancement and society-changing insights.":1,"#Both internal and external regulations are needed to help ensure that AI is developed ethically.":1,"#The practice of using AI to address global problems, improve the environment, and improve lives is known as <em ljsid-1=\"\">AI for Good</em>.":1,"#In <strong ljsid-1=\"\">AI for Social Good</strong>, Rahul Dodhia describes how artificial intelligence (AI) can be used to better society. AI is the newest technology to revolutionize society with the potential to cultivate tremendous positive change. However, this change is only possible if AI developers safeguard their systems against bias, data leaks, and unethical uses. By establishing regulations, mitigating risk, and removing bias from data, AI systems can be used to create a more equitable and safe future for all people.":1,"#ISBN: 978-1-39420-578-3":1,"#<strong ljsid-0=\"\"><a ljsid-0-0=\"\">Rahul Dodhia</a></strong> heads the AI for Good Research Lab at Microsoft, based in Redmond, Washington. He leads a team of AI researchers dedicated to addressing global challenges using artificial intelligence. His work focuses on sustainability, humanitarian action, and health issues, paying special attention to climate adaptation in the Global South. Prior to his current role, he led machine learning teams at several corporations, including eBay, Amazon, and Expedia. He also served at the NASA Ames Research Center, where he applied foundational research on human memory to address safety concerns in general aviation and space flight.":1,"#Whether or not the AI revolution results in a technological dystopia, utopia, or somewhere in between depends on how ethically AI models are developed, deployed, and integrated into our society. History shows that technological revolutions are often wrought with crises and hardships, so we must treat the opportunity to reach beyond human intelligence with great care and responsibility.":1,"#Just like electricity, communication tech, and the Internet, AI is the next transformational technology to reshape society. Enabled by AI, new technologies like quantum computing and DNA storage offer to further revolutionize scientific research and innovation. As society responds to the revolution, AI teams must remain flexible and collaborative while cultivating a symbiotic relationship with AI tools. For those outside of AI development, new opportunities will become available as businesses require unique support roles for their AI integrations. Model trainers, prompt engineers, and project managers become increasingly valuable as AI becomes commonplace in businesses.":1,"#The quality of an AI project often depends on the quality of available data and the chosen modeling method. AI teams must preprocess their data for biases, inconsistencies, and errors while balancing the accuracy of their model by tuning its precision and sensitivity.":1,"#At the beginning of a project, it’s necessary to set a roadmap with defined objectives to make sure all stakeholders, resources, and potential blockers are accounted for. AI projects often go beyond initial scope and require additional resources to complete, so setting expectations at the beginning of the project reduces future friction. Be sure to thoroughly assess the existing hardware infrastructure for the project, as models are often limited by computing power.":1,"#<em ljsid-0=\"\"> The AI expert</em>. As the lead programmer, designer, and model trainer, the AI expert works with the team to build and evaluate the AI model.":1,"#<em ljsid-0=\"\"> The domain expert</em>. An authority in the field that the AI project targets ensures that the project is useful for end users and actually solves the problem.":1,"#<em ljsid-0=\"\"> The project manager</em>. By planning feature development, managing the team, and bridging gaps between other teams, the project manager ensures the project’s quality from start to finish.":1,"#by Rahul Dodhia":1,"#AI projects are data, resource, and time intensive endeavors that require close communication between all key team members. Along with the backing of executive leadership and other support teams, the following three roles are essential to any successful AI project:":1,"#Getting the Best Out of Your AI Team":1,"#Current AI systems collect enormous amounts of personal data without user consent, raising ethical and data privacy concerns. Cambridge Analytica faced widespread scrutiny in 2018 when they were found to be creating psychological profiles of users to sell to political advertising businesses. By ensuring user consent, algorithmic transparency, and data security, AI developers ensure that their models are legal, ethical, and safe to be used at large scales.":1,"#Safety regulations for AI models are necessary in the same way that buildings should be inspected for safe construction. Especially in physically dangerous applications like autonomous vehicles, healthcare, and weapon systems, AI must be regulated for strict data governance protocols, accuracy, and bias to minimize their risks. Additionally, the question of who or what is accountable for AI harm must be answered so regulators can hold developers responsible for unethically developed AI.":1,"#Whenever significant technological advancements like nuclear power, the Internet, and current AI tools are made, their risks must be managed carefully by regulatory bodies. Just as consumers want to ensure their prescription medicine is determined safe by the FDA, consumers also want to ensure that their personal information is securely, transparently, and ethically used by AI systems.":1,"#Putting Safeguards Around AI":1,"#AI also contributes to the spread of disinformation and increases political tensions on social media. As seen in the 2016 election, foreign operatives can use bots to create AI-generated content that polarizes and divides voters. The effects of fake content are yet to be fully realized, and the need for regulation only increases as AI tools become better at mimicking human behavior.":1,"#In a similar scenario, the Los Angeles Police Department worked with researchers to create an AI model that could predict where crimes were likely to occur based on historical data. However, the model was found to unfairly target Black neighborhoods, as a Black person is five times more likely to be stopped by the police without cause than a white person. By training a model on biased data, AI can perpetuate historical biases like racism, sexism, and homophobia.":1,"#As is the case with any major technological advancement, AI can be incredibly dangerous even if developed with good intentions. The University of Washington developed drones that use AI image recognition to help detect disaster victims buried under debris by their heat signatures. While developed for a good purpose, the technology to detect humans with AI-enabled drones also allows governments to autonomously surveil a population.":1,"#When Good AI Goes Bad":1,"#Using Artificial Intelligence to Save the World":1,"#Beyond our planet, AI offers scientists the opportunity to identify life on other planets through the rapid processing of massive data sets. Astronomers also found that AI systems were able to discover new insights given their lack of theoretical assumptions. The combination of AI’s scalable data processing, predictive analysis, and insight generation allows a future of rapid scientific advancement and increased human understanding.":1,"#Proteins are the building blocks of organisms and are difficult to visualize because of their complex, changing structures. New proteins could be discovered, but their function couldn’t be understood without identifying their structure. Recently, DeepMind created an AI algorithm that could provide a 3D model of a protein, which completely transformed the study of proteins and opened the door to discoveries in protein-related diseases like Parkinson’s and Alzheimer’s.":1,"#AI’s ability to rapidly process data completely revolutionizes how scientists research and discover new findings. In an ecosystem, a <em ljsid-1=\"\"> keystone species </em> is a species that maintains the food chain, habitat structure, and vegetation growth. When a keystone species becomes displaced or decreases in population, the entire ecosystem falls apart. Environmental scientists often perform repetitive pattern-recognition tasks through collected audio and visual data, which can now be offloaded to AI models, allowing future disruptions in keystone species to be quickly monitored and addressed.":1,"#AI for Good: Pursuit of Scientific Knowledge":1,"#AI image analysis is also useful in medical contexts, where AI systems can find patterns and irregularities faster and more accurately than humans. As the Food and Drug Administration (FDA) continues to approve AI tools for cancer screening and detection, diseases can be identified and treated at speeds once thought impossible. Ultimately, the extent to which AI can be used for good depends on the federal, private, and public sector support the projects receive and the strength of regulations ensuring ethical practices.":1,"#For areas struggling with food and water security due to climate change, predictive AI can be used to alleviate famine and drought conditions. At the beginning of the Russian invasion of Ukraine, NASA used AI tools to analyze the effect of the invasion on the world’s global wheat supplies, which enabled actions to maintain resiliency. Currently, water usage is being optimized in Kenyan farms through satellite image and soil moisture analysis to create optimal irrigation schedules.":1,"#<em ljsid-0=\"\"> AI for Good </em> is the practice of using AI to address global problems, improve the environment, and improve lives. As climate change worsens weather conditions across the world, tools to combat an increased quantity of natural disasters become more important. AI excels at quickly processing data and delivering real-time insights, so AI is used to help disaster relief specialists analyze satellite imagery and determine where immediate support is needed. Prior to disasters, models can input data from remote sensors to check for common disaster warning signs.":1,"#Understanding how human neurons work is essential to understanding neural networks. Just like a light switch, neurons take an input and send an electrical signal to other neurons. Neural pathways are created by a series of neurons associating a pattern to a specific input. Similar to how a detective gathers primary evidence, identifies suspects based on the evidence, then builds a case based on the likeliest suspect, a <em ljsid-1=\"\"> layered neural network </em> analyzes data through ordered layers of processing. By understanding how networks function within AI models, we can increase the explainability and transparency of complex AI systems.":1,"#<em ljsid-0=\"\"> Reinforcement learning</em>. Models are rewarded for ideal outputs, which trains them to maximize rewards and therefore the quality of outputs.":1,"#<em ljsid-0=\"\"> Unsupervised learning</em>. Models are given unlabeled data and sets to identify their own patterns, which can reveal patterns hidden to humans.":1,"#Clicking this link will redirect to relevant products for the Author Rahul Dodhia.":1,"#<em ljsid-0=\"\"> Supervised learning</em>. Models are trained on sets of correctly labeled data in the same way humans learn using flashcards and practice exam questions.":1,"#The first significant wave of AI development was centered around <em ljsid-1=\"\"> machine learning </em> , which is a set of AI techniques that develops correlations between inputs and outputs. In practice, a machine learning algorithm can take an input of cat images, identify features like ear shape, and output an insight that the image contains a cat. The following three types of machine learning demonstrate how AI systems emulate human cognition:":1,"#AI Explained: A Non-Technical Guide":1,"#While $9.8 trillion of value is estimated to be added to the global economy by 2030 because of AI innovations, there are very few safeguards in place to ensure that these innovations will benefit society. The spread of misinformation, job insecurity, and wealth inequality may worsen unless we develop and adhere to regulations that mitigate AI risks.":1,"#As hardware improved, <em ljsid-1=\"\"> deep learning </em> became possible, which is a layered network of neuron-like nodes designed to replicate the human brain. From the foundation of neural networks, a machine learning algorithm was developed that could identify content in images, and the race to develop increasingly powerful and comprehensive artificial intelligence (AI) systems began. Today, companies like Google, Microsoft, and Meta are investing heavily into AI talent acquisition and accelerating AI model development.":1,"#In the early 1800s, Charles Babbage developed the blueprints for a machine (the Difference Engine) capable of solving difficult mathematical problems. This design was the first functional computer and served as the foundation for future innovation by scientists like John von Neumann and Alan Turing. During his research, Turing created a test called the <em ljsid-1=\"\"> Imitation Game </em> , in which a machine and group of humans are asked the same questions to determine how accurately a machine could reflect human behavior. The test is still discussed today as large language models like ChatGPT demonstrate a remarkable ability to mimic human speech.":1,"#In the early 1800s, Charles Babbage developed the blueprints for a machine (the Difference Engine) capable of solving difficult mathematical problems. This design was the first functional computer and served as the foundation for future innovation by scientists like John von Neumann and Alan Turing. During his research, Turing created a test called the <em ljsid-1=\"\"> Imitation Game </em> , in which a machine and group of humans are asked the same questions to determine how accurately a machine could reflect human behavior. The test is still discussed today as large language models like ChatGPT demonstrate...":1,"#A Brief History of Artificial Intelligence":1,"#Successful AI teams require close collaboration between AI experts, domain experts, and project managers.":1,"#Unfortunately, AI models originally designed with good intentions can be used unethically by governments and bad actors. AI developers need to ensure that their models are legal, ethical, and safe to be used at large scales.":1,"#Yves Pigneur, Alexander Osterwalder, Alan Smith, Tim Clark, Bruce Hazen":1,"#by Yves Pigneur, Alexander Osterwalder, Alan Smith, Tim Clark, Bruce Hazen":1,"#business-model-you-second-edition":1,"#Clicking this link will redirect to relevant products for the Authors Bruce Hazen.":1,"#Clicking this link will redirect to relevant products for the Authors Tim Clark.":1,"#Clicking this link will redirect to relevant products for the Authors Alan Smith.":1,"#Clicking this link will redirect to relevant products for the Authors Alexander Osterwalder.":1,"#In today’s complex, dynamic labor market, focusing on a profession, rather than solely a job or series of jobs, is key to...":1,"#Clicking this link will redirect to relevant products for the Authors Yves Pigneur.":1,"#ISBN: 978-1-119-87964-0":1,"#In <strong ljsid-1=\"\">Brag Better</strong>, Meredith Fineman offers a series of strategies designed to help you overcome your discomfort, appropriately call attention to your accomplishments, and share the precise ways that your work is making a difference for your team, company, and the world around you. Fineman, the host of the podcast <em ljsid-3=\"\">It Never Gets Old</em> and founder...":1,"#ISBN: 978-0-5930-8681-0":1,"#by Meredith Fineman":1,"#brag-better":1,"#Clicking this link will redirect to relevant products for the Author Meredith Fineman.":1,"#Formal authority and titles are a double-edged sword. On the one hand, fewer people, especially younger ones, are inclined to blindly follow orders anymore. They especially need to feel they're part of an organization with a meaningful vision, that senior managers have transparent motives and make clear the reasons for anything that they ask people below to...":1,"#by Allan Cohen":1,"#<strong ljsid-0=\"\">Allan Cohen</strong> is a consultant on organizational change and leadership for companies as large as GE, IBM, and Lafarge, and as entrepreneurial as Access Technology and Menon. His best-selling books on management include <em ljsid-2=\"\">Managing for Excellence, Influence Without Authority</em>, and <em ljsid-4=\"\">Influencing Up</em>. Throughout his career he has directed and taught in executive development programs for thousands of managers.":1,"#Clicking this link will redirect to relevant products for the Speaker Allan Cohen.":1,"#<em ljsid-0=\"\"> Give control</em>. Offer people choices to reduce their fear and increase their engagement.":1,"#<em ljsid-0=\"\"> Clarify and reinforce priorities</em>. Let people know what’s changing and what isn’t.":1,"#<em ljsid-0=\"\"> Increase understanding</em>. Provide a simple summary of what the change is and why it’s occurring.":1,"#When a change initiative is proposed, most employees want to know what the change means to them, why it’s happening, and what the world will look like after the change has been made. To help people accept change more rapidly, leaders can use four approaches:":1,"#Section One: Understanding Change":1,"#As humans, we are averse to change. Unfortunately, this often makes it difficult for business leaders to implement change initiatives successfully. <strong ljsid-1=\"\">HBR Guide to Leading Through Change</strong> from Harvard Business Review Press offers 21 articles with insights and actionable tips for understanding change and communicating new directions, as well as building teams that are resilient and embrace the dynamic nature of today’s business environment.":1,"#ISBN: 978-1-64782-687-1":1,"#Teams often make...":1,"#<em ljsid-0=\"\"> Provide support</em>. Allow people time to worry and ask questions.":1,"#Communicate the Urgency, Maintain the Momentum, Achieve the Vision":1,"#managing-knock-your-socks-off-service":1,"#by Chip R. Bell, Ron Zemke":1,"#Clicking this link will redirect to relevant products for the Authors Ron Zemke.":1,"#This philosophy emphasizes the importance of hiring the right people and shifts hiring practices. To be good fits for customer service roles, people need...":1,"#Clicking this link will redirect to relevant products for the Authors Chip R. Bell.":1,"#Jacqueline Carter, Rasmus Hougaard":1,"#Source: Quality":1,"#Brian Kaminski, Debra A. Ball, Kevin Webb":1,"#John Britt, Henry Paul, Ed Jent":1,"#Recently Viewed <span ljsid-2=\"\">(1538)</span>":1,"#<var hour=\"\"></var>:<var minutes=\"\"></var> minutes":1,"#You are signed in as <var signed_in_as=\"\"></var>":1,"#The article focuses on the importance of reshaping personal career narratives to facilitate professional growth and opportunities. It emphasizes that outdated and negative self-stories can hinder career advancement, suggesting that individuals should reflect on their experiences with a future-oriented perspective. The author introduces the CAR model—Context, Action, Result—as a framework for crafting empowering narratives that highlight one's strengths and unique contributions. Additionally, the article encourages individuals to identify their \"golden thread,\" which connects their experiences and aspirations, ultimately guiding them toward their next career steps.":1,"#Level Up Your Brand Narrative":1,"#Source: Business Officer":1,"#<span ljsid-0=\"\">Book Summary</span> <span ljsid-2=\"\">|</span> Meredith Fineman":1,"#<span ljsid-0=\"\">Book Summary</span> <span ljsid-2=\"\">|</span> William Arruda":1,"#<span ljsid-0=\"\">Book Summary</span> <span ljsid-2=\"\">|</span> Cindy McGovern":1,"#<span ljsid-0=\"\">Video</span> <span ljsid-2=\"\">|</span> Gerry Valentine":1,"#think-fast":1,"#ISBN: 9781118004630":1,"#by Guy A. Hale":1,"#Clicking this link will redirect to relevant products for the Author Guy A. Hale.":1,"#Most people’s instinct when confronted with a large obstacle, or series of obstacles, is...":1,"#Post-session assessments show that clients with simulated AI coaches achieved significant progress toward their goals, comparable to those with human coaches. Both groups substantially outperformed the noncoached control group in developing self-awareness and taking concrete goal-directed actions.":1,"#3. Facilitates measurable goal achievement":1,"#Clients rated both simulated AI and human coaching sessions as highly valuable. They consistently mentioned four key elements they valued most about their experience: effective communication style, demonstrations of empathy, help in generating personal insights, and assistance in developing realistic strategies specific to their challenges.":1,"#2. Delivers meaningful coaching experiences":1,"#Clients established strong connections with both simulated AI and human coaches in just a single, hour-long session. The data shows comparable relationship quality metrics across both conditions, with clients appreciating the engaging, purposeful conversation regardless of coach type.":1,"#Clients established strong connections with both simulated AI and human coaches in just a single, hour-long session. The...":1,"#1. Builds collaborative working relationships":1,"#For her dissertation, Amber S. Barger, a talent development executive, investigated the transformative potential of artificial intelligence in coaching to help people change their mindsets and behavior. Her randomized controlled experiment measured client reactions to a simulated AI coach that performed in ways akin to an expert professional human coach. She found that AI coaches of the future can be just as effective as human coaches in providing significant coaching experiences. Barger's research earned her ATD's 2025 Dissertation Award.":1,"#What passes for good service is often simply ordinary service, with each customer treated exactly the same as the previous one. Customers receive little acknowledgement or appreciation as workers perform their tasks and move on to the next person in line. According to Steve Curtin in Delight Your Customers, this happens because organizations and their employees emphasize the mundane job functions of service work when they should instead be focusing on the true essence of the job: giving customers the best experience possible. By focusing on job essence, enterprises can empower employees to elevate overall customer service skills.":1,"#Jerome P. Finnigan, Warren H. Schmidt":1,"#High-quality customer service is a key differentiator for many businesses, yet providing it seems to be falling down the priority list amidst cost and competitive pressures. Meanwhile, those companies that provide great service and innovate ways to keep both customers and employees happy are enjoying higher profits and lower long-term costs. These are the companies that go beyond customer satisfaction to customer loyalty. In Managing Knock Your Socks Off Service, authors Chip R. Bell and Ron Zemke show how companies can make that transition by implementing eight principles backed by both research and experience.":1,"#Artificial intelligence (AI) is rapidly gaining traction in businesses across industry sectors. The latest advance in this technology field is generative AI, which represents a giant step forward. As leaders grapple with how to deploy AI effectively, they face decisions with significant consequences. In Human + Machine, Updated and Expanded, Paul R. Daugherty and H. James Wilson offer insights and frameworks to help organizations profit from today’s age of AI.":1,"#Human + Machine, Updated and Expanded":1,"#The article focuses on the role of talent development practitioners as change agents in helping organizations navigate workplace transformations. It highlights the importance of individuals like internal training leads, instructional designers, and external consultants who blend expertise with interpersonal skills to facilitate change. Key characteristics of effective change agents include empathy, resilience, and strong communication skills, enabling them to support employees through transitions and foster a culture of adaptability. The article emphasizes that building a network of change agents within organizations is crucial for sustaining change efforts and enhancing overall organizational agility and resilience.":1,"#Become a Change Agent":1,"#Diana Verde Nieto":1,"#Fred A. Miller, Judith H. Katz":1,"#The article presents results from across 16 studies with 7,860 U.S. participants, in which researchers found that employees who unplug from work during nonwork hours are seen by managers as more productive but less promotable. This promotability penalty occurred even when unplugging did not affect actual performance or when done for noble reasons, highlighting a disconnect between perceived commitment and true effectiveness. The effect was mitigated when managers were reminded of company policies supporting work-life boundaries, emphasizing the need for alignment between organizational values and managerial evaluations.":1,"#Employees Who Unplug Are Seen as More Productive—but Less Promotable":1,"#Managing Change<span ljsid-1=\"\">, </span>":1,"#<span ljsid-0=\"\">Book Summary</span> <span ljsid-2=\"\">|</span> Yves Pigneur, Alexander Osterwalder, Alan Smith, Tim Clark, Bruce Hazen":1,"#Copyright© of TD: Talent Development is the property of Association for Talent Development and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.":1,"#Bookmarked (5)":1,"#In Customer Experience 3.0, John A. Goodman provides a blueprint for how companies can increase their top and bottom lines by delivering better and more efficient customer experiences in today’s age of “techno service.” Beginning with setting and managing customer expectations, Goodman walks readers through the key considerations of designing successful end-to-end customer experiences and tackling certain implementation hurdles that will arise. Additionally, technology discussions are cleverly interwoven throughout the book to illustrate how, when utilized properly and intelligently, technological tools play a vital role in providing great customer experiences.":1,"#The study of the theory and practice of TQM has led to three conclusions: (1) The current world situation makes the examination of TQM a must. (2) TQM is deeply rooted in American organizational theory and management practice. (3) The accomplishments of the Baldrige Award winners effectively underscore the power of the TQM process. As individuals and companies examine TQM to decide whether or not they wish to pursue the process, it is useful to understand the beliefs, concepts, theories, and practices that contribute to TQM as well as those that are dysfunctional. Insights from several important areas of management are key to TQM, including scientific management, group dynamics, training and development, achievement motivation, employee involvement, sociotechnical systems, organizational development, corporate culture, the new leadership theories, cross-functional teams, and strategic planning. Total Quality Management goes by many names, but has customer satisfaction as its single goal.":1,"#Bill Huyett, Richard Dobbs, Tim Koller":1,"#<strong ljsid-0=\"\">Chip R. Bell</strong> is a senior partner with The Chip Bell Group (CBG), headquartered near Atlanta. Prior to starting CBG in 1980, he was Director of Management Development for NCNB (now Bank of America). Dr. Bell is the author or coauthor of several best-selling books, including <em ljsid-2=\"\">Wired and Dangerous, Take Their Breath Away, Magnetic Service, Service Magic, </em>and <em ljsid-4=\"\">Customers as Partners.</em> He has appeared on CNBC, CNN, ABC, and FOX Business, and his work has been featured in <em ljsid-6=\"\">The Wall Street Journal,</em> <em ljsid-8=\"\">Fortune, USA Today, Fast Company,</em> and <em ljsid-10=\"\">Business Week</em>.":1,"#<em ljsid-0=\"\">Never overlooking the fundamentals.</em> If an employee makes customers feel welcome but falls behind in paperwork or other administrative tasks, recognizing the customer-facing behavior can send the wrong message and irritate other employees.":1,"#Supporting and coaching employees and involving them in decisions about service delivery.":1,"#www.chipbell.com":1,"#Remaining both optimistic and tenacious.":1,"#The most effective service leaders also know how to inspire and sustain service-focused cultures by:":1,"#<em ljsid-0=\"\">Celebrating appropriately.</em> Celebrations when goals are met or projects conclude can reinforce priorities. However, they can also feel like just another party if they are not all about recognizing and rewarding the people who achieved the impressive levels of service.":1,"#<em ljsid-0=\"\">Going beyond the individual.</em> Teamwork drives great customer service, so recognition ought to reward everyone involved in providing a great service experience.":1,"#Successful service organizations treat their employees the way they want their customers treated, which means managers need to be honest and keep their promises to employees. Customers expect the same respect.":1,"#The Best Service Leaders Know How to Serve":1,"#Recognition Encourages Ideal Behaviors, But Only if Done Right":1,"#<strong ljsid-0=\"\">Managing Knock Your Socks Off Service</strong> by Chip R. Bell and Ron Zemke provides a clear and comprehensive blueprint for service organizations that want to give themselves a competitive edge. Its astute suggestions go beyond typical service-related advice, and will likely provide even seasoned service managers with new ideas. The book contains many specific examples of exemplary service in action at well-known, successful companies to illustrate the imperatives. The book is primarily written for service managers and executives, but it will also interest frontline service employees who would like to advance in their careers. Later sections assume familiarity with earlier sections, so the book is best read cover to cover. The book includes lighthearted illustrations, endnotes, and an index.":1,"#Listening to employees with undivided attention and asking questions to ensure understanding.":1,"#Connecting with employees and making themselves visible and available. They communicate in-person with employees rather than electronically (when possible) or work directly with customers during high-demand times.":1,"#When dealing with problems, coaches deliver feedback in private and stay focused on the behavior rather than the person. They remind the individual of the impact of the behavior on the entire team to keep the focus on colleagues and customers, not the coach.":1,"#Ongoing training, perhaps 20 to 40 hours per year, for all employees can further bond them to their employers and improve customer service. This training is best focused on four areas:":1,"#Training is extremely important to providing consistently outstanding service, but training as usual, which typically begins with an information-laden orientation, is often boring and can deflate even the most enthusiastic new hire. A superior strategy is to spread the typical day-one tasks out over several weeks. Employees can fill out paperwork and set up their e-mail accounts and passwords before their first day, freeing that valuable time for creating relationships and understanding the company culture. Some organizations match new hires with more experienced employees during the early weeks of work to facilitate both these outcomes. Some companies take the unexpected step of asking new employees for feedback during their first few days on the job. These fresh, outside perspectives may surprise long-serving managers who no longer notice potential problems. Seeking these perspectives also demonstrates to new hires that they and their opinions are valued.":1,"#The statement’s purpose is to translate words into employee actions by being clear enough that employees always know what to do when they have to make a decision about how to serve customers. For example, the Ritz-Carlton hotel has a service vision statement that includes the “three steps of service.” It also includes 20 “basics” that detail more specific rules, such as “any employee who receives a guest complaint ‘owns’ the complaint.” The service vision is even summarized in a sound-byte, “We are ladies and gentlemen serving ladies and gentlemen,” that is easy for employees to remember and reminds them of the basis for the other rules.":1,"#Employees also need inspiration, which managers can instill through modeling and gratitude. Modeling ideal behaviors shows employees that managers mean what they say. Gratitude that gets results is expressed specifically. Otherwise, workers may not know what they did to earn it and therefore may not know what behaviors to repeat. Effective gratitude is also completely separated from coaching. When mangers thank employees and then give advice at the same time, employees almost always interpret the thank you as sugarcoating rather than as sincere.":1,"#Customers are Loyal to Businesses that Are Easy to Deal With":1,"#Offer atonement appropriate to the size of the failure.":1,"#Managers need to clearly give employees permission to make decisions in unexpected situations. They also need to remember to ask frontline service employees for ideas to improve service and solve problems.":1,"#<em ljsid-0=\"\">Product and service knowledge.</em> Today, customers often know a great deal about products and services before they buy, so they expect service employees to know even more. Customers even expect service employees to know about competitors’ products.":1},"version":196694}]