Glossary
Plain-language definitions of the AI, product, and design terms we use in our work.
Human-Centered Design & UX
Contextual inquiry is a user research method in which a researcher watches people do their real work in their own environment and asks questions as the work happens.
Customer journey mapping is the process of creating a visual representation of the steps a customer takes to reach a goal with an organization, including their actions, thoughts, emotions, and touchpoints.
A design sprint is a time-boxed process, often five days long, in which a small cross-functional team moves from a defined problem to a prototype tested with real users.
Design thinking is a problem-solving approach that applies designers' methods, such as understanding users, reframing problems, generating ideas, prototyping, and testing, to create solutions centered on people's needs.
Digital accessibility is the practice of designing and building websites, apps, documents, and other digital products so that people with disabilities can perceive, understand, navigate, and use them.
Heuristic evaluation is a usability inspection method in which experts review an interface against a set of established design principles, called heuristics, to find likely usability problems.
Human-centered design is an approach to building products, services, and systems around the needs, behaviors, and context of the people who use them, tested with those people throughout the process.
Product discovery is the process of learning which problems are worth solving and which solutions are worth building, using research and quick experiments before a team commits to full development.
Usability testing is a research method in which representative users try to complete realistic tasks with a product or prototype while a researcher observes where they succeed, struggle, or fail.
User experience (UX) is the overall experience a person has when using a product, service, or system, including how useful, easy to use, accessible, and satisfying it is.
User research is the systematic study of the people who use a product or service, including their needs, behaviors, goals, and pain points, so teams can make better design and business decisions.
Modernization
AI-native development is an approach to building software in which AI is designed into both the development process and the product from the start, rather than added to existing tools or workflows later.
Application modernization is the process of updating existing software applications to newer architectures, platforms, languages, or interfaces so they are easier to maintain, scale, secure, and use.
Legacy modernization is the process of updating, rebuilding, or replacing outdated software and systems so they meet current business needs, security expectations, and the way people work today.
Rapid prototyping is the practice of quickly building early, simplified versions of a product, feature, or interface so teams can test ideas with users and stakeholders before committing to full development.
Technical debt is the future cost of rework that builds up when software teams choose quick or limited solutions now instead of approaches that would be easier to maintain and change later.
Agentic AI
Agent orchestration is the coordination of multiple AI agents, tools, and human steps so they work together on a larger task, including how work is divided, sequenced, shared, and monitored.
Agentic AI refers to AI systems that can pursue a goal with limited human direction by planning steps, using tools or data, taking actions, and adjusting based on the results.
To agentify an organization means restructuring how its work gets done so that AI agents handle defined tasks and workflows alongside people, with clear ownership, limits, and human oversight.
An AI agent registry is a shared, up-to-date record of every AI agent operating in an organization, including who owns it, what it does, what it can access, and how much risk it carries.
AI agents are software systems that use AI models to interpret information, decide on actions, and carry out tasks toward a goal, often by calling tools, data sources, or other applications.
AI coding agents are AI tools that can plan and carry out software development tasks, such as writing, editing, testing, and explaining code, by working directly with a codebase and development tools.
AI workflow automation is the use of AI models or agents to carry out or support steps in a business process, including steps that involve reading documents, interpreting requests, or making judgment calls.
Human-in-the-loop (HITL) is a design approach in which people take part in an AI or automated system's process by reviewing, correcting, or approving its outputs at defined points.
AI Adoption
AI adoption is the process of moving AI from something an organization has access to into something its people use every day to do real work, with results the business can measure.
AI change management is a structured approach to helping people and teams move to new ways of working when AI tools or agents are introduced, addressing their roles, skills, workflows, and concerns.
AI enablement is the work of preparing an organization's data, systems, access controls, and people so AI tools can connect to real workflows and deliver reliable results.
AI governance is the set of policies, processes, roles, and controls an organization uses to make sure its AI systems are developed and used responsibly, legally, safely, and in line with its goals.
AI literacy is the set of knowledge and skills people need to understand what AI is, how it works, how to use it effectively, and how to judge its outputs and risks.
An AI readiness assessment is a structured evaluation of how prepared an organization is to adopt AI, covering its strategy, data, technology, skills, governance, and culture.
AI use case prioritization is the process of comparing potential uses of AI against criteria such as business value, feasibility, risk, and readiness to decide which ones to pursue first.
A forward deployed engineer (FDE) is a software engineer who embeds with a customer to turn an AI platform or product into a working production system that fits that customer's workflows, data, and constraints.
Shadow AI is the use of AI tools or accounts outside an organization's approved processes or visibility.