Glossary

Plain terms.

The terms that appear on this site and in a typical engagement, in plain language and in alphabetical order.

A

Accessibility: Whether a product can be used by people with a wide range of sight, hearing, movement, and memory. For older adults it is the whole point, not an edge case.
Adoption: Whether a product gets used once it is bought and installed. The outcome a care organization is actually paying for.
AgeTech: Technology made with older adults, not only for them. Their feedback and co-design shape it, along with the family and caregivers around them.
AI governance: The rules and checks that decide how an AI system is built, watched, and held to account.
AI impact assessment: A structured look at what an AI system could do to the people it touches, good and bad, before and after it ships. ISO 42005 describes how to run one.
AI risk assessment: Weighing what can go wrong with an AI system, how likely it is, and how much it would matter. The technical checks and the real-world use are weighed together.
Algorithm audit: An independent check of an AI system against a stated standard: what it does, what data it uses, and whether its outputs are fair, safe, and explainable.

B2B: Business to business. On this site, a builder selling to a care organization, or a care organization buying from a builder.
Bias: When an AI system treats some people worse than others because of the data it learned from or the way it was built. Older adults are often underrepresented in that data.

Care organization: Home care, senior living, a hospital, or a health system: any organization that brings technology to the older adults in its care.
Change order: A written agreement to change the scope, timeline, or cost of work already under contract, so the change is as clear as the original deal.
Co-design: Designing with the people who will use a product, so their feedback shapes it from the start rather than at the end.
Consent: A real choice about what a product may do with a person's data or attention, explained plainly, with declining as easy as agreeing.
Conversation design: Shaping how a product talks with people, so the exchange is clear and useful.

Dark patterns: Design that tricks or pressures people into choices they would not otherwise make: hidden cancellation, pre-ticked boxes, manufactured urgency.
Design system: The shared set of components, patterns, and rules a product is built from, so every screen looks and behaves as one thing.
Diligence: The checking a buyer, partner, or investor does before committing: what the product does, what evidence supports it, and how its AI is governed.
Discovery: The research at the start of a project that establishes what the problem really is and who has it, before anything is designed.

Explainability: Whether a person can understand why an AI system did what it did, in terms that let them question it.

Fractional: Part-time and ongoing. You get senior help a few days a month, without a full-time hire.

Human-AI interaction: How a person and an AI system work together, and whether the person stays in control.
Human in the loop: A person who reviews, approves, or can override what an AI system does, especially where the decision affects someone's care.

Information architecture: How a product's content and functions are organized and named, so people can find what they need without a map.
ISO 42001 and ISO 42005: International standards for managing AI responsibly. 42001 covers how an organization runs its AI. 42005 covers how to weigh an AI system's impact on people.

Journey map: A picture of the whole experience a person has with a service, step by step, including what happens off the screen and where it goes wrong.

Large language model (LLM): The kind of AI behind most products that talk or write. Fluent and useful, and capable of stating something false with complete confidence.

NDA: A non-disclosure agreement. A promise, in writing, not to share what the other side tells you in confidence.

Onboarding: The first days of using a product: setup, first tasks, and the help offered along the way. Where adoption is usually won or lost.

Pilot: A limited, measured trial of a product in a real setting before committing to it across the organization. The measures should be agreed before it starts.
Privacy by design: Building data protection into a product from the start: collect less, explain what is collected, and keep it no longer than needed.
Product design: Deciding what a product does and how it works for the people who use it, from the first idea through every screen and interaction.
Product roadmap: The planned sequence of what a product will deliver and when, ordered by the value each release brings to the buyer and the people using it.
Product strategy: The decisions about who a product is for, what it must do for them, and why it will win, made before the roadmap and tested against evidence.
Prototype: A working model of an idea, made to test and improve it before it is built for real.

Responsible AI: Building AI that people can trust, with attention to safety, fairness, and honesty about what it does and does not do.
Retainer: An ongoing arrangement for a set amount of my time each month. The fractional shape of an engagement.

Scoped project: A defined piece of work with agreed deliverables, a timeline, and a fixed or capped budget.
Service blueprint: A journey map with the back stage added: the staff actions, systems, and handoffs behind each step a person experiences.
Service design: Looking at the whole experience around a product, including the steps, people, and handoffs that happen off the screen.
Stakeholder alignment: Getting the people who decide, build, buy, and use a product to agree on what it is for, so the work is not undone later by someone who was not in the room.
Statement of work (SOW): The document that sets out what will be done, what will be delivered, by when, and for how much. Referenced from the contract.

Technical debt: The hidden cost of shortcuts. Quick fixes that work today and make tomorrow's changes slower, riskier, and more expensive.

Usability testing: Watching real people use a product to see where it helps them and where it gets in the way.
UX (user experience): How a product feels to use, and whether it works for the person using it.
UX research: Finding out how people actually think, work, and struggle, through interviews, observation, and testing, so decisions rest on evidence rather than guesses.

WCAG: The Web Content Accessibility Guidelines. A shared standard for making digital products usable by people with a wide range of sight, hearing, movement, and memory.
Wireframe: A plain sketch of a screen's layout and content, without visual finish, used to settle what goes where before it is designed in full.