| Term | Explanation |
| 3D CAD software | Computer-aided design software that allows for the creation and modification of three-dimensional designs and models, commonly used in engineering and design. |
| A/B testing | A research method of comparing two versions of an element, such as a webpage or app, against each other to determine which performs better. |
| acceptance criteria | The conditions that a product, such as software, must meet to be accepted by a user, customer, or other stakeholders. |
| accuracy | The degree of correctness or precision in data, information, or predictions, which is crucial for reliable decision-making and analysis. |
| adoption | Within the context of AI, refers to the process of integrating and using AI technologies or solutions within an organization or society to achieve specific goals or benefits. |
| agile | A flexible and iterative approach to project management that prioritizes collaboration and adaptability to respond effectively to changes and customer needs. |
| AI | See artificial intelligence. |
| AI adoption | The process of integrating and using AI technologies or solutions within an organization or society to achieve specific goals or benefits. |
| AI techniques | Encompass various methodologies and algorithms used in artificial intelligence to process data, extract insights, and make predictions. |
| AI-driven chatbots | Artificial intelligence programs designed to simulate conversation with human users, and often used for gathering real-time product feedback. |
| AI-generated content | Material, such as text, images, or music, created by AI systems without direct human involvement. |
| Alexa | Amazon’s cloud-based voice service and virtual assistant, accessible through smart speakers, such as the Amazon Echo. Alexa uses natural language processing to answer questions, control smart home devices, play music, and perform various tasks, providing a hands-free and convenient user experience. |
| algorithmic adjustments | Modifications made to algorithms or models to improve their performance or adapt to changing conditions. |
| algorithmic bias | The presence of unfair or discriminatory outcomes produced by AI algorithms due to biases in the data used for training or the algorithm design. |
| Amazon Web Services | Abbreviated as AWS, a comprehensive cloud computing platform provided by Amazon that offers a wide range of services such as computing power, storage options, and networking capabilities. It enables businesses and developers to use web services to build scalable, flexible, and cost-effective IT solutions. |
| API | See application programming interface. |
| application programming interface | Abbreviated as API, a set of rules and protocols that allows different software applications to communicate and interact with each other. |
| artificial intelligence | Abbreviated as AI, refers to the simulation of human intelligence in machines that can perform tasks such as problem-solving, learning, and decision-making. |
| automated data modeling | Involves the use of AI or machine learning algorithms to automatically create data models that represent relationships and patterns within datasets. |
| automation | Involves the use of technology, such as AI, to automate tasks, processes, or workflows, reducing the need for manual intervention and increasing efficiency. |
| AWS | See Amazon Web Services. |
| AWS Bedrock | A set of machine learning tools and services provided by Amazon Web Services to help businesses build and deploy AI models. |
| Azure | A cloud computing platform provided by Microsoft that offers various services, including AI and machine learning capabilities, to help organizations develop and manage applications. |
| B2B | Refers to business-to-business, the commerce between companies as opposed to between businesses and individual consumers. |
| basic AI principles | Encompass fundamental guidelines and ethical considerations for developing and using AI, ensuring it is responsible, fair, and respectful of human rights and values. |
| beneficence | The ethical principle of doing good and taking actions that promote the well-being and benefit of others, often considered in the development and use of AI technologies. |
| bias | Within the context of AI, refers to the presence of unfair or discriminatory outcomes produced by AI algorithms due to biases in the data used for training or the algorithm design. |
| blind spot | Within the context of AI, refers to areas or situations where AI models fail to recognize or understand certain patterns, leading to inaccuracies or biased decisions. |
| business-to-business | See B2B. |
| buyer requirements | Specific conditions or criteria set by customers that a product or service must meet to fulfill their needs and expectations. |
| ChatGPT | A large language model developed by OpenAI, capable of generating human-like text and used in various applications, including natural language processing and conversational agents. |
| chief AI officer | A senior executive responsible for overseeing and implementing AI strategies and initiatives within an organization. |
| churn | The rate at which customers or employees discontinue their association with a company or organization, which is crucial to track and minimize for customer or employee retention efforts. |
| cloud storage | Refers to the storage of data on remote servers accessible via the internet, providing scalable and flexible data storage solutions for individuals and organizations. |
| code of conduct | Outlines ethical guidelines and behavioral expectations for individuals within an organization or community, including considerations related to AI ethics and responsible AI usage. |
| company culture | The values, beliefs, and behaviors that shape an organization’s work environment and influence the attitudes and actions of its employees. |
| compliance mechanisms | Refer to processes and tools implemented to ensure adherence to regulations, policies, and ethical guidelines in the development and use of AI systems. |
| consumer sentiment | The overall attitude of consumers toward a product or service, which can be positive, negative, or neutral, and impacts their buying decisions. |
| Cortana | Microsoft’s virtual assistant, designed to help users interact with devices and access information through voice commands and natural language queries. |
| critical thinking | The ability to analyze, evaluate, and interpret information objectively and logically, enabling individuals to make informed and sound decisions. |
| CRM | See customer relationship management. |
| cross-functional cooperation | Collaboration and coordination between different departments or teams within an organization to achieve shared goals and tackle complex challenges that require diverse expertise. |
| culture of innovation | An organizational environment that encourages creativity, risk-taking, and the development of new ideas, fostering innovation across the company and driving continuous improvement. |
| customer assistance | Refers to providing support, information, or help to customers through AI-powered chatbots or virtual assistants. |
| customer attrition | Refers to the loss of clients or customers over time, which is a critical metric for businesses to monitor and address. |
| customer data | Encompasses information collected about customers, including preferences, behaviors, and interactions with a company’s products or services, often used for personalized marketing and improving customer experiences. |
| customer engagement | The level of involvement, interaction, and emotional connection that customers have with a brand or company, impacting their loyalty and willingness to interact with the business. |
| customer experience | Abbreviated as CX, refers to the overall impression and perception customers have of a brand based on their interactions and experiences with the company’s products, services, or support channels. |
| customer outreach | Activities and initiatives aimed at reaching out to potential or existing customers to build relationships, promote products or services, and address customer needs and concerns. |
| customer profiles | Comprehensive descriptions of customers, created using various data points and behaviors. |
| customer relationship | The connection and interaction between a business and its customers, focusing on building trust, loyalty, and satisfaction through consistent and positive experiences. |
| customer relationship management | Abbreviated as CRM, a system used to manage and analyze interactions with current and potential customers, improving customer engagement and relationships. |
| customer satisfaction | The level of contentment and fulfillment that customers experience with a product, service, or interaction with a company, influencing their likelihood to repurchase or recommend. |
| customer segmentation | The process of dividing a customer base into groups of individuals with similar characteristics for targeted marketing. |
| CX | See customer experience. |
| DALL-E | A generative AI model developed by OpenAI capable of generating inventive images from textual descriptions. |
| data analysis | The examination and interpretation of data to derive meaningful insights and conclusions, used for decision-making, problem-solving, and understanding trends or patterns. |
| data analytics | The process of examining, cleaning, transforming, and interpreting large datasets to extract insights and make data-driven decisions using various statistical and machine learning techniques. |
| data cleaning | The process of identifying and correcting errors, inconsistencies, or inaccuracies in data sets to improve data quality and reliability for analysis or AI model training. |
| data inputs | Data that is entered into a system for processing and analysis. |
| data lakes | Large storage repositories that hold vast amounts of raw data in its native format until needed. |
| data literacy | The ability to read, understand, and communicate with data, enabling individuals to interpret and make informed decisions based on data analysis. |
| data management | The practice of collecting, keeping, and using data securely, efficiently, and cost-effectively. |
| data modeling | The process of creating a representation of data structures and relationships to organize, integrate, and store information efficiently for use in databases or AI applications. |
| data patterns | Recognizable and recurring themes or trends found within data sets, which are often used to predict future trends or behaviors. |
| data privacy | Involves the protection and proper handling of personal or sensitive data, ensuring that individuals’ information is not accessed or used without their consent and is safeguarded from unauthorized access or breaches. |
| data privacy and ethics | Concerns related to the responsible handling, processing, and protection of data, especially personal data, and the ethical implications of its use in technology and business. |
| data protection | Refers to the measures and practices put in place to secure and safeguard data from unauthorized access, loss, or theft, ensuring the privacy and integrity of sensitive information. |
| data validation | The process of verifying the accuracy, completeness, and reliability of data to ensure that it is consistent and conforms to specific standards or requirements. |
| data wrangling | The process of cleaning, transforming, and preparing raw data for analysis or modeling, ensuring it is in a suitable format for use in AI or other applications. |
| debugging | The process of finding and resolving defects or problems within a software application that prevents correct operation. |
| decision-making | The cognitive process of selecting a course of action from multiple alternatives. |
| decision-making frameworks | Within the context of AI, structured approaches or methodologies that are used to make informed decisions based on data analysis and predictions provided by AI models. |
| descriptive statistics | Refer to the analysis and summary of data using various statistical measures to provide insights into its characteristics and trends. |
| diagnostic statistics | Refer to statistical techniques used to identify the causes or factors contributing to certain patterns or outcomes in data. |
| disruption | Within the context of AI, refers to the significant impact or transformation caused by the adoption of AI technologies, leading to changes in business models, industries, or societal dynamics. |
| e-commerce | Also known as electronic commerce, refers to the buying and selling of goods and services over the internet. It involves online transactions between businesses, consumers, or a combination of both, enabling global access to products and services, simplified payment processes, and personalized shopping experiences. |
| edge cases | Extreme conditions or unusual situations that occur at the boundaries of software operation, which can often reveal bugs or issues in a system. |
| electronic commerce | See e-commerce. |
| emotional intelligence | Abbreviated as EQ, the ability to understand and manage one’s emotions and recognize and empathize with the emotions of others, influencing interpersonal relationships and decision-making. |
| empathetic leadership | Also known as empathic leadership, leadership that values and demonstrates an understanding of the emotions and needs of team members and stakeholders, fostering a positive and supportive work environment. |
| empathy | The capacity to understand and share the feelings and perspectives of others, promoting better communication, collaboration, and relationship-building. |
| empathy-driven decision-making | Refers to the inclusion of empathy and emotional intelligence into the decision-making process to consider the impact of decisions on individuals or groups and make more compassionate choices. |
| employee | An individual working for a company or organization, contributing to its goals and objectives as part of the workforce. |
| employee engagement | The emotional commitment and dedication that employees have toward their work and organization, affecting their motivation, productivity, and overall satisfaction. |
| employee experience | The overall journey and interaction of an employee with their employer, encompassing all aspects of their work life, from recruitment to exit, and the impact on performance and satisfaction. |
| employee feedback system | Processes and tools used to collect and gather input, opinions, and suggestions from employees, providing insights for performance improvement and addressing workplace concerns. |
| employee review cycle | The regular period for performance assessments and feedback given to employees, often conducted annually or semi-annually to evaluate and support professional growth and development. |
| EQ | See emotional intelligence. |
| erosion | The gradual decline or reduction of customer or employee engagement and satisfaction, often leading to decreased loyalty and retention. |
| ethical AI | The development and use of artificial intelligence technologies in a manner that aligns with ethical principles, respects human rights, and avoids harm or discriminatory practices. |
| ethical considerations | Within the context of AI, refer to the moral implications of AI technologies, ensuring they align with ethical norms, protect privacy, and avoid harm to individuals and society. |
| ethics | Within the context of AI, refer to the principles and guidelines that govern the responsible development, deployment, and use of artificial intelligence technologies, addressing potential risks, biases, and ethical challenges. |
| ethics board | A committee or group responsible for reviewing, guiding, and ensuring ethical practices in the development and deployment of AI technologies. |
| facial recognition | An AI technology that identifies and verifies individuals by analyzing unique facial features, often used for security, authentication, and surveillance purposes. |
| fairness | The ethical principle of treating individuals impartially and without bias or discrimination, ensuring equal opportunities and outcomes, especially crucial in AI development and applications. |
| feature engineering | The process of selecting, transforming, or creating relevant features from raw data to improve the performance and accuracy of machine learning models. |
| feature prioritization | The process of determining the order in which features of a product should be developed or released based on their importance or urgency. |
| functional documents | Technical documents that describe the features, capabilities, and operation of a system or application. |
| GAN | See generative adversarial network. |
| GDPR | See General Data Protection Regulation. |
| General Data Protection Regulation | Abbreviated as GDPR, a regulation in EU law that addresses data protection and privacy for individuals within the European Union and the European Economic Area, ensuring control and protection of their personal data. |
| generative adversarial network | Abbreviated as GAN, a type of AI model where two neural networks, the generator and discriminator, compete against each other to produce high-quality synthetic data. |
| generative AI | AI models capable of generating creative and original content, such as text, images, or music, often using large datasets and complex algorithms. |
| generative AI mindset | A way of thinking that fosters creativity, curiosity, and exploration of new possibilities using generative AI techniques and tools, encouraging innovation and novel approaches. |
| generative design | A methodology that uses algorithms to create numerous design possibilities within defined parameters, commonly utilized to optimize designs according to specific criteria. |
| generative pre-trained transformer | Abbreviated as GPT, an advanced conversational AI model developed by OpenAI capable of having natural and interactive discussions with users. |
| GitHub Copilot | An AI-powered tool developed by GitHub to assist software developers in writing code, providing code suggestions, and completing code snippets. |
| Google Cloud | A suite of cloud computing services offered by Google, providing various tools and resources for building and deploying applications, including AI and machine learning solutions. |
| Google Gemini | An AI-powered tool developed by Google, designed to assist in writing and generating natural language text. |
| governance | Within the context of AI, refers to the establishment of rules, policies, and controls that guide the development, deployment, and use of AI technologies within an organization or society, ensuring ethical, responsible, and compliant practices. |
| GPT | See generative pre-trained transformer. |
| growth mindset | The belief that talents and abilities can be developed through effort, learning, and perseverance, encouraging a willingness to learn and embrace challenges to achieve personal and professional growth. |
| guardrails | Within the context of AI, refer to predefined boundaries or limits set on AI models to prevent them from generating harmful, biased, or undesirable outputs, ensuring they stay within ethical and safe boundaries. |
| hallucinations | Within the context of AI, refer to instances where AI models generate content that appears real but is actually synthetic and not based on real-world data. |
| Health Insurance Portability and Accountability Act | Abbreviated as HIPAA, a US federal law that protects individuals’ health information and ensures the confidentiality and security of patient data in the healthcare industry. |
| HIPAA | See Health Insurance Portability and Accountability Act. |
| human-AI collaboration | Involves the interaction and cooperation between humans and AI technologies to leverage their respective strengths and expertise, leading to improved outcomes and efficiency in problem-solving and decision-making. |
| implementation | Within the context of AI, refers to the practical application and integration of AI technologies or systems into real-world scenarios, such as business processes, products, or services. |
| inaccuracies | Within the context of AI, refer to errors or deviations in AI-generated content or predictions that do not align with the correct or expected outcomes. |
| innovation | The creation and introduction of new ideas, products, services, or processes that bring value and contribute to positive change and advancement in various fields. |
| intellectual property | Abbreviated as IP, refers to intangible creations of the mind, such as inventions, designs, or creative works, that are protected by laws to grant exclusive rights to the creator or owner. |
| interactive platforms | Digital tools that facilitate user interaction and engagement, essential for collecting immediate user responses and feedback. |
| IP | See intellectual property. |
| job satisfaction | The level of contentment and fulfillment that employees experience in their jobs, often influenced by factors such as work environment, recognition, and opportunities for growth. |
| justice | The ethical principle of fairness, equality, and adherence to laws and regulations, particularly essential when considering the implications of AI technologies on society and individual rights. |
| key performance indicator | Abbreviated as KPI, a measurable metric used to evaluate and assess the success or performance of an AI system, project, or organization against specific objectives or goals. |
| KPI | See key performance indicator. |
| large language model | Abbreviated as LLM, a type of AI model, like GPT, that can process and generate large amounts of human-like text. |
| leadership mindset | The attitudes and beliefs of leaders that influence their approach to challenges and decision-making, fostering a culture of innovation, empathy, and ethical considerations. |
| legal action | Formal action taken within the legal system, including litigation and enforcement of laws and regulations, which may be required in cases of AI misuse or legal disputes. |
| liability | Legal responsibility or accountability for actions or consequences, a significant consideration in AI development and deployment, especially in scenarios where AI decisions impact individuals or businesses. |
| LLM | See large language model. |
| machine learning | Abbreviated as ML, a subset of AI that involves training algorithms to learn patterns and make decisions from data, enabling them to improve their performance over time. |
| machine learning algorithms | A set of rules or processes that can be used by an AI system to identify new insights and patterns and predict outputs. |
| market intelligence | The process of gathering, analyzing, and interpreting information about a market, including information about customers, competitors, and other market dynamics. |
| market opportunities | Potential areas in a market where a company can introduce new products or services to meet customer demands and gain a competitive advantage. |
| market opportunity statements | Concise conclusions drawn from market data analysis that highlight potential areas for business growth or product development. |
| marketing | The process of promoting, selling, and distributing a product or service. |
| MCM | See multi-cloud management. |
| ML | See machine learning. |
| models | Within the context of AI, refer to the trained algorithms or systems that can process data, make predictions, or perform specific tasks based on the patterns and information learned during training. |
| morale | The overall confidence, satisfaction, and enthusiasm of individuals or teams within an organization, influencing productivity and work performance. |
| multi-cloud management | Abbreviated as MCM, refers to the process of managing and integrating multiple cloud computing environments, services, or platforms to streamline operations and optimize resource utilization. |
| natural language processing | Abbreviated as NLP, a branch of AI that focuses on the interaction between computers and human languages, enabling machines to understand, interpret, and generate human language. |
| NLP | See natural language processing. |
| noncompliance | Within the context of AI, the act of failing to adhere to legal, ethical, or regulatory requirements in the development and deployment of AI systems, leading to potential legal consequences or reputational damage. |
| non-maleficence | The ethical principle of avoiding harm and preventing negative consequences in AI development and deployment, emphasizing the importance of safety and responsible use of AI technologies. |
| object recognition | An AI capability that allows machines to identify and classify objects in images or videos. |
| ongoing monitoring | Within the context of AI, involves continuous assessment and analysis of AI systems, data, and outcomes to ensure their accuracy, fairness, and ethical compliance throughout their lifecycle. |
| operational efficiency | The ability of an organization to deliver products or services to its customers in the most effective and cost-efficient manner. |
| organizational culture | The shared values, beliefs, and behaviors within an organization that shape its identity, atmosphere, and employee interactions, significantly impacting organizational performance and success. |
| performance analytics | The analysis of performance data to improve decision-making and operational efficiency. |
| performance appraisal | An evaluation process used to assess an employee’s performance, strengths, and areas for improvement, providing feedback and setting performance-related goals. |
| performance data | Data that provides insights into how a software application performs under various conditions, often related to speed, reliability, and resource usage. |
| performance review | A formal assessment of an employee’s performance, skills, and achievements, typically conducted periodically to discuss progress and provide feedback on performance. |
| personalization | Tailoring a service or a product to accommodate specific individuals, often using data and AI algorithms. |
| personalization engines | Systems that use AI to analyze data and deliver tailored content and recommendations. |
| personalized interactions | Refer to tailored and customized experiences or content delivered to individuals based on their preferences, behavior, or characteristics, often enhanced with AI personalization algorithms. |
| personalized support system | An AI-powered system that provides customized assistance, guidance, or services to individuals based on their unique needs, preferences, or requirements. |
| personally identifiable information | Abbreviated as PII, refers to any data or information that can identify or distinguish an individual, such as their name, address, contact details, or biometric data, often requiring special protection and handling under data privacy laws. |
| PII | See personally identifiable information. |
| policies | Within the context of AI, sets of guidelines, rules, or principles that govern the ethical, legal, and operational aspects of AI development, deployment, and use. |
| policymakers | Individuals or entities responsible for creating, implementing, and influencing policies and regulations, including those related to AI development and deployment. |
| prediction | The act of forecasting or estimating future outcomes or events based on historical data and patterns, commonly used in machine learning and predictive analytics. |
| predictive analytics | The use of data, statistical algorithms, and machine learning models to identify patterns and predict future outcomes, helping organizations make informed decisions and develop strategies. |
| predictive analytics models | Statistical models or machine learning algorithms used to forecast future events or behaviors based on historical data. |
| procedures | Within the context of AI, specific instructions or protocols that outline how to carry out certain tasks or actions related to AI development, deployment, or operation. |
| processes | Within the context of AI, refer to the structured workflows, methodologies, or steps involved in developing, deploying, or maintaining AI systems or solutions. |
| product management | The discipline and process involved in overseeing the development, marketing, and overall management of a product or product line. |
| product recommendations | AI-powered suggestions or advice provided to customers based on their preferences, behaviors, or historical data, aiming to enhance customer satisfaction and encourage repeat purchases. |
| product specifications | Detailed descriptions of a product’s features, functionality, and requirements, guiding its development and testing. |
| productivity | The measure of output or work accomplished by individuals, teams, or organizations, considering the efficiency of resources used, often enhanced by automation and process improvements. |
| product-market fit | The degree to which a product satisfies a strong demand in a specific market, indicating its potential for success. |
| professional growth | Advancement and development of skills, knowledge, and capabilities within a profession or field, often supported by training, learning opportunities, and mentorship. |
| prospecting | The process of identifying and qualifying potential customers or business opportunities, often carried out in sales and marketing to build a pipeline of potential clients. |
| psychological safety | A part of workplace culture that encourages open communication, idea sharing, and constructive feedback, enabling employees to take risks and express their thoughts without fear of negative consequences. |
| R&D | See research and development. |
| RAI | See responsible AI initiative. |
| recognition program | An organizational initiative that rewards and acknowledges employee achievements, efforts, or contributions, promoting a positive work culture and motivating individuals to excel. |
| regulation | Within the context of AI, refers to legal or governmental rules and requirements that govern the development, deployment, and use of AI technologies to ensure responsible and ethical practices. |
| research and development | Abbreviated as R&D, within the context of AI involves the exploration, experimentation, and innovation to advance AI technologies and applications, often leading to new breakthroughs and improvements. |
| resilience | The ability of individuals or organizations to recover and adapt to challenges, setbacks, or change, enabling them to thrive even in the face of adversity. |
| responsible AI framework | A set of guidelines, principles, and best practices designed to ensure the ethical and responsible development and use of AI technologies, taking into account the impact on society, individuals, and the environment. |
| responsible AI initiative | Abbreviated as RAI, an initiative or project focused on promoting and implementing responsible AI practices, including ethical considerations, fairness, and transparency, in AI development and deployment. |
| retention | The ability to keep employees or customers within a company or organization, often achieved through creating a positive and supportive work environment and addressing their needs. |
| revenue streams | The sources of income or revenue for a business, often consisting of various products, services, or income-generating activities contributing to overall revenue. |
| risk | Within the context of AI, refers to the potential for adverse or negative outcomes, such as biases, errors, or security breaches, associated with the use of AI technologies. |
| risk assessment | Within the context of AI, the process of identifying, evaluating, and mitigating potential risks related to AI development, deployment, or usage to ensure safety, security, and compliance. |
| sales | The activities and processes involved in selling products or services to customers, including prospecting, customer outreach, and closing deals. |
| sales analytics | The analysis of sales data and performance metrics to gain insights into sales trends, customer behavior, and performance, supporting data-driven decision-making and sales strategies. |
| sales pipeline | The visual representation of sales opportunities and deals at various stages in the sales process, tracking the progress of leads and prospects toward becoming customers. |
| sales productivity | The efficiency and effectiveness of sales representatives in achieving their sales targets and generating revenue, often improved through sales training, technology tools, and performance monitoring. |
| sales prospecting | The process of identifying and qualifying potential customers or leads to engage with and pursue sales opportunities, a crucial activity for building a strong customer base. |
| sales targeting | The selection and prioritization of specific customer segments or prospects for sales and marketing efforts, aligning products and offers with the needs and preferences of target audiences. |
| search engine marketing | Abbreviated as SEM, a digital marketing strategy used to increase the visibility of a website in search engine results pages, primarily through paid advertising. |
| search engine optimization | Abbreviated as SEO, the practice of increasing the quantity and quality of traffic to one’s website through optimizing language and other parameters for organic search engine results. |
| self-awareness | Conscious knowledge and understanding of one’s emotions, strengths, weaknesses, and values, facilitating effective leadership and personal growth. |
| SEM | See search engine marketing. |
| sentiment analysis | The use of natural language processing and machine learning techniques to determine whether the sentiment or emotion expressed in text data is positive, negative, or neutral, often applied to gauge customer feedback or opinions on a particular product or topic. |
| SEO | See search engine optimization. |
| setback | A temporary or prolonged obstacle or failure encountered in achieving a goal or objective, requiring resilience and problem-solving to overcome and move forward. |
| Siri | Apple’s virtual assistant, designed to assist users in accessing information and performing tasks through voice commands and natural language interactions. |
| social and environmental impact | The effects of AI technologies and processes on society, communities, and the environment, highlighting the importance of considering and mitigating any negative consequences. |
| social listening | The process of monitoring social media channels to understand what is being said about a brand, industry, or specific topics, often informing marketing strategies and customer engagement. |
| standards | Within the context of AI, guidelines or specifications that define the technical, ethical, or operational requirements for developing, using, and evaluating AI technologies, promoting interoperability, reliability, and ethical practices across industries and applications. |
| statistics | Within the context of AI, refer to the use of mathematical techniques to analyze and interpret data, providing insights and supporting decision-making in AI applications. |
| steering committee | A group or panel responsible for setting strategic directions, providing guidance, and making decisions regarding AI initiatives or projects within an organization. |
| strategy | Within the context of AI, refers to the formulation and implementation of plans and actions to achieve specific AI-related goals or objectives within an organization or business context. |
| synthesization | The process of combining or integrating diverse elements or ideas to create something new or comprehensive, often relevant in AI to generate creative and original content or insights. |
| target user | The ideal or primary group of people for whom a product or service is designed, based on specific characteristics like behavior, needs, and preferences. |
| test cases | Sets of conditions or variables under which a tester assesses whether a software application is working correctly. |
| test coverage | The extent to which the testing process can validate different aspects of an application, including various scenarios and edge cases. |
| test data | Data sets created specifically for the purpose of testing and evaluating the performance and functionality of a software application or system, but which mimic the population that will be impacted. |
| test scenario | A high-level description of what an application will be tested for, which is independent of how it will be tested. |
| transformation | Within the context of AI, refers to significant changes or advancements in an organization or society achieved through the adoption and integration of AI technologies, impacting various aspects, including business models, operations, or customer experiences. |
| transparency | Within the context of AI, refers to making the decision-making processes and outcomes of AI models understandable, interpretable, and explainable, fostering trust and accountability in AI applications. |
| turnover | The rate at which employees leave an organization or the process of replacing employees who have left, impacting organizational stability and performance. |
| user data | Information collected about users’ interactions, behaviors, preferences, and demographics, used to understand and improve their experience with a product or service. |
| user engagement | The extent and depth of a user’s interaction and involvement with a product, service, digital content, or brand. It is a key metric to assess how effectively these elements attract and retain users’ attention, indicating their appeal, relevance, and overall user experience quality. |
| user feedback | Information provided by users about their experience with a product or service, which can be crucial for product development and improvement. |
| user needs and goals | The specific requirements and objectives that users seek to fulfill with a product or service. |
| user personas | Semi-fictional characters created to represent different user types within a targeted demographic, attitude, and behavior set. |
| user stories | Descriptions of a software feature from the end user’s perspective created in the form of a story, focusing on their needs and the value they will gain. |
| user verbatims | Direct quotes from users obtained through surveys, interviews, or feedback, providing unfiltered insights into their experiences and opinions about a product or service. |
| value proposition | The promise of value to be delivered to the customer, highlighting the unique benefits of a product or service. |
| virtual try-ons | Technology solutions that allow customers to visualize how products, like clothing or accessories, would look on them using digital images or augmented reality. |
| well-being | The state of physical, mental, and emotional health and happiness of individuals, often influenced by workplace culture, support, and work-life balance. |
| wireframe | A schematic or blueprint, often used in software development, to outline the structure of a web page or software interface. |
| work engagement | The level of enthusiasm, dedication, and involvement that employees have toward their work and organization, associated with improved productivity and job satisfaction. |
| workflow | The sequence of steps or activities that make up a process, often involving the coordination and movement of information, materials, or tasks to achieve a specific outcome. |
Software engineer, passionate about data and information, immersed in a total transformation with artificial intelligence.