Our Approach

How We Do It

Positive. Practical. People-first.

We design marketing and learning programs that adapt to you—reducing anxiety, building confidence, and turning AI into everyday wins.

Our Philosophy

Adapt the Path

We tailor learning to each person’s readiness and needs.

Kindness + Confidence

We teach in a positive, encouraging way.

Community Multiplier

We inspire people to help others as they grow.

Understanding Adoption:

The Models We Use

There’s no single model that explains AI adoption. We blend classic diffusion theory with modern acceptance frameworks—and extend them for trust, ethics, and emotion

Diffusion of Innovations

With the adoption curve model, popularized by Everett Rogers in his 1962 book  "Diffusion of Innovations", that helps us understand the different groups of tech adopters. Based on this model, there are five distinct groups based on people’s willingness to embrace new innovations:

Innovators (2.5%): These are the first individuals to adopt a new product. They are typically risk-takers and are eager to try new ideas, often without needing proof of their effectiveness. 
Early Adopters (13.5%): This group is more socially connected and often serves as opinion leaders. They adopt new products early but are more cautious than innovators, seeking some evidence of the product's benefits before trying it. 
Early Majority (34%): These individuals adopt new products just before the average person. They are deliberate and take their time before making a decision, often waiting for the product to be proven effective. 
Late Majority (34%): This group is skeptical and adopts new products only after the majority of society has accepted them. They often require reassurance and proof of the product's reliability. 
Laggards (16%): Laggards are the last to adopt an innovation. They are typically resistant to change and may only adopt a new product when it becomes necessary.

Assumes a uniform path: The model lays out neat stages, but real access depends on costs, infrastructure, and caregiving or support needs.
Overlooks unequal resource access: Adoption is shaped by policy, funding, platform accessibility, and affordability, not just interest.
Ignores stacked barriers: People can face multiple, intersecting hurdles (usability, trust, training, stigma) that slow adoption in ways a single curve can’t capture.
Equates availability with usage: Just having a tool isn’t enough—privacy, accessibility features, language options, and safe defaults matter.
Compresses diverse experiences: A single trajectory can mask different needs, preferences, and lived realities across communities.

Acceptance Models (TAM, UTAUT)

UTAUT expands on TAM by incorporating elements from eight models, creating a more comprehensive framework with additional factors:

Performance Expectancy: Belief that using a system enhances performance.
Effort Expectancy: The ease of using the device.
Social Influence: The beliefs of peers and supervisors regarding technology use.
Facilitating Conditions: Resources and support available for easier AI adoption.

Key Limitations of UTAUT in the AI Context

Lack of Trust and Risk Dimensions
AI systems often operate as “black boxes,” giving rise to concerns over bias, accountability, and unintended consequences. UTAUT does not explicitly model these trust or risk perceptions.

Absence of Ethical and Regulatory Factors
Ethical norms (fairness, transparency) and evolving regulations (GDPR, AI Act) shape willingness to adopt AI in ways UTAUT’s original moderators don’t cover.

Limited Emotional and Cognitive Variables
AI can trigger anxiety (fear of job displacement), curiosity, or skepticism—dimensions UTAUT overlooks.

Good AI's Approach to the UTUAT Model

These models and additional variables are considered to better understand our beneficiaries. We conduct research and listening sessions to understand beneficiaries’ needs, challenges, aspirations, and emotions.

This approach defines how we promote our work, how we engage with the people we serve, and what we might develop in the future:

Marketing: To develop targeted marketing strategies based on the characteristics of different adopter groups.

Technology Development: To understand how new technologies will be received and to plan for potential challenges in adoption.

How We Apply The Extended UTUAT Model

Research and Listening

Interviews, surveys, listening sessions to map needs, barriers, and emotions.

Tailored Design

Segment by readiness; craft inclusive marketing and learning experiences.

Supportive Delivery

Buddy systems, clear guardrails, and hands-on practice with feedback.

Matching Methods to Readiness

Inspiration

Spark curiosity with stories and demos

Motivation

Find your why - time saved, impact made.

Rational

Ethics, environment, state-of-tech

Humor

Learn with levity; improve & playful prompts.

Relaxation

Reduce anxiety; make space to learn.

Connection

Buddy up - learn together, share wins.

What We Do

Raise awareness about the state of AI and its potential for good

Help people find good resources on AI

Encourage people to develop critical thinking skills

Motivate people to spread their knowledge (and do it kindly)

Help people learn in a relaxed, inspiring, motivating, fun, and present way

Enable people connections and create a learning community to combat tech isolation

Prevent the bad consequences of AI adoption and over-use: diminishing memory, intellectual numbness, complacency, lack of creativity, passive acceptance.

Invite people to commit to the ethical and humane use of AI via our pledge.

Move people through connection, community, and kindness.