Software, AI & Technology Glossary

The policies, decision rights, controls, and accountability practices used to develop and operate AI systems responsibly across their lifecycle.

A structured evaluation of an organization’s strategy, data, technology, governance, skills, and operating model before it invests in AI initiatives.

The use of approved AI tools to support test design, data preparation, defect analysis, automation, and quality reporting under human review.

A software delivery approach where engineers use approved AI tools to accelerate research, coding, testing, refactoring, and documentation while retaining human accountability.

The connection of applications or services through defined interfaces so they can exchange data, trigger workflows, and operate as a coordinated system.

The governance, publication, security, monitoring, and lifecycle control of APIs used by internal teams, partners, and customer applications.

The disciplined design, development, integration, testing, and operation of software applications around defined user and business requirements.

An architecture pattern that provides a dedicated backend layer for a specific frontend experience, such as web, mobile, or partner applications.

The processes and tools used to transform operational data into reports, dashboards, and analysis that support business decisions.

The redesign and automation of repeatable business workflows to reduce manual effort, improve consistency, and provide clearer operational data.

A data-integration technique that detects inserted, updated, or deleted records and delivers those changes to downstream systems with minimal delay.

Continuous Integration and Continuous Delivery practices that automate building, testing, and releasing software to make changes safer and more frequent.

The improvement or migration of applications, infrastructure, and operating practices to use scalable cloud services, automation, and modern architecture.

A field of AI that enables systems to analyze images or video for tasks such as detection, classification, inspection, and visual search.

The packaging of an application and its dependencies into a portable, isolated runtime unit that behaves consistently across environments.

The connection of a customer relationship management platform with websites, sales, service, marketing, analytics, and operational systems.

A mobile or desktop application built from a shared codebase that can run across multiple operating systems while retaining platform-specific capabilities.

A platform that unifies customer data from multiple sources into governed profiles that can support analytics, segmentation, and activation.

The roles, policies, standards, and controls that keep organizational data secure, understandable, accountable, and fit for approved use.

A scalable repository that stores large volumes of raw or lightly processed structured, semi-structured, and unstructured data for future use.

A data architecture that combines the flexible storage of a data lake with management and analytical capabilities commonly associated with a warehouse.

A decentralized data operating model where domain teams own data products while shared standards provide governance, discovery, and interoperability.

A repeatable process that collects, validates, transforms, and moves data from source systems into destinations used for analytics, operations, or AI.

The degree to which data is accurate, complete, consistent, timely, valid, and suitable for operational, analytical, or AI use.

A centralized analytical data store designed to combine information from multiple systems for reporting, business intelligence, and historical analysis.

A stable, long-term team assigned to a customer’s product roadmap, with roles and capacity shaped around ongoing delivery needs.

The integration of security practices, automated checks, and shared responsibility throughout software development and infrastructure operations.

A practical plan that connects business priorities with customer experience, operating processes, data, technology investment, and measurable outcomes.

The coordinated redesign of customer experiences, operations, business models, and organizational capabilities using digital technology and data.

A software design approach that organizes models and architecture around business domains, shared language, and clearly bounded areas of responsibility.

A data workflow that collects information from source systems, converts it into a usable structure, and loads it into a target platform.

A system design in which services publish and respond to business or technical events, enabling asynchronous workflows and looser coupling.

An experienced technology executive engaged on a flexible basis to guide strategy, architecture, teams, investment, governance, and critical programs.

Artificial intelligence that creates new text, code, images, audio, or other content based on patterns learned from data and supplied instructions.

An operational approach where version-controlled configuration is the source of truth and automated processes reconcile environments with that desired state.

A content management system that delivers structured content through APIs, allowing teams to use the same content across websites, apps, and channels.

A mobile application built with web technologies and delivered inside a native container to share implementation across iOS and Android.

A coordinated process for detecting, prioritizing, responding to, communicating, and learning from technology service disruptions.

The management of cloud and infrastructure resources through version-controlled definitions that can be reviewed, tested, and deployed consistently.

A cloud platform for building, deploying, monitoring, and governing integrations between applications, data sources, APIs, and business partners.

A machine-learning model trained on large volumes of language data to understand and generate text, code, summaries, and other language-based outputs.

A branch of AI in which models learn patterns from data to make predictions, classifications, recommendations, or automated decisions.

Ongoing responsibility for agreed technology operations, monitoring, maintenance, support, reliability, and continuous improvement under defined service levels.

An architecture that divides a system into independently deployable services aligned to focused capabilities and connected through defined interfaces.

The smallest useful version of a product that can validate critical assumptions with real users before larger investment and expansion.

The engineering practices used to version, deploy, monitor, evaluate, govern, and continuously improve machine-learning models in production.

A software architecture where one platform securely serves multiple customer organizations while separating their data, configuration, and access.

An application developed specifically for one operating system using its native languages, frameworks, interface conventions, and device capabilities.

AI techniques that enable software to analyze, classify, search, translate, summarize, or generate human language.

The ability to understand a system’s internal condition through connected metrics, logs, traces, events, and operational context.

Testing that measures responsiveness, throughput, stability, and resource behavior under expected, peak, stress, or sustained workloads.

The creation and operation of shared internal platforms that give delivery teams standardized, self-service paths for building and running software.

The use of historical data, statistical methods, and machine learning to estimate likely future outcomes, behaviors, or operational conditions.

A structured process for understanding users, problems, value, feasibility, risk, and priorities before committing to full product development.

A web application enhanced with browser and device capabilities to provide installability, offline behavior, notifications, and app-like interactions.

An engagement model where a provider takes responsibility for delivering an agreed project scope, team, plan, quality controls, and outcomes.

A whole-lifecycle approach that builds quality into requirements, design, code, automation, environments, releases, and production feedback.

The processing and analysis of continuously arriving data quickly enough to support immediate operational decisions, alerts, or customer experiences.

The repeated verification that existing functionality still works after software, configuration, dependency, or environment changes.

An AI pattern that retrieves relevant information from approved knowledge sources and supplies it to a generative model to improve contextual answers.

Software automation that performs defined, rules-based interactions across user interfaces and systems to reduce repetitive manual work.

Software delivered and maintained as an online service, commonly using subscription access, shared platform operations, and continuous updates.

A cloud customer relationship management platform used to coordinate sales, service, marketing, automation, analytics, and customer data.

A delivery lifecycle that integrates security requirements, design review, secure coding, testing, release controls, and maintenance practices.

The assessment of software and configurations for vulnerabilities, unsafe behavior, access-control weaknesses, and security requirement failures.

A cloud execution model where the provider manages underlying infrastructure and applications consume automatically scaled functions or managed services.

A documented agreement that defines measurable service commitments, responsibilities, support coverage, response targets, and reporting expectations.

Moving quality activities earlier in delivery so requirements, design, code, and integrations are tested before defects become expensive to resolve.

Quality practices performed after release, including monitoring, experimentation, resilience testing, and analysis of real production behavior.

An engineering discipline that applies software practices to operations through service objectives, automation, monitoring, incident response, and reliability work.

An engagement model that adds selected specialists to an existing customer team to address capability, capacity, or delivery gaps.

The future cost and delivery constraint created when software decisions favor short-term speed over maintainability, quality, security, or architecture.

An evidence-based review of product, architecture, data, security, teams, delivery capability, cost, and risk for investment or acquisition decisions.

A sequenced plan connecting business objectives with architecture, platforms, delivery initiatives, dependencies, investment, and measurable milestones.

The use of scripts, frameworks, and pipelines to execute repeatable software checks and provide faster feedback on product quality.

A shared quality capability that establishes testing standards, governance, expertise, automation, metrics, tools, and reusable practices across teams.

Business-led validation that a solution supports agreed user needs, processes, rules, and acceptance criteria before release or operational adoption.

The design and development of websites and applications so people with different abilities can perceive, navigate, understand, and use them.

The measurement of meaningful website actions such as enquiries, registrations, purchases, downloads, and campaign-assisted customer journeys.