From AI awareness to everyday confidence
Awareness of AI is no longer the bottleneck. Confidence is. AIAN is built for the people who have heard plenty about AI and still have no idea what it's for in their own week.
This page describes a proposal. It contains no adoption statistics, no customer claims and no lead capture — nothing here is a form.
The problem we see
General-purpose AI tools present a blank box and ask people to imagine a use for it. For someone who is hesitant, that blank box is the barrier: not the technology, not the price, but the absence of an obvious, personally relevant reason to begin. Generic tutorials don't fix this, because they teach the tool rather than the person.
The opportunity
If you know what someone cares about, what they're trying to get done and what has put them off, you can propose one specific small win instead of a capability. AIAN treats that understanding as structured, owned data — which makes personalisation repeatable and measurable rather than anecdotal.
How AIAN works underneath
Understand each person
A structured adoption profile: experience, confidence, interests, goals, barriers and preferred formats — captured as stable data, not free text.
Deliver relevant small wins
Deterministic matching turns that profile into a handful of concrete, ten-minute actions with plain-language steps and a ready-to-use starter prompt.
Learn from feedback
Every 'useful' and 'not for me' is a signal. The profile is a living record that gets sharper each time someone tries something.
Illustrative participant profile
"Sam", 48, retail supervisor
- Experience
- Tried AI once or twice
- Confidence
- 2 of 5 — a little unsure
- Interests
- Cooking, family, gardening
- Goals
- Save time, get organised, save money
- Barriers
- Where to start, trust and accuracy
- Prefers
- Step-by-step, short text
A fictional composite created for this proposal.
Sample participant journey (illustrative)
- 1Completes the seven-step profile in about five minutes, skipping age and name.
- 2Receives three suggestions, each with a plain 'why this fits you' rationale.
- 3Tries the ten-minute meal-planning action using the copyable starter prompt.
- 4Marks it useful; progress saves and the first-week plan moves on.
- 5Returns on day three to ask a follow-up, and marks one suggestion 'not for me'.
A four-week structure
Short enough to commit to, long enough to see whether anything actually changed.
Set up and recruit
Agree the participant group and success measures. Configure the profile questions for the audience. Brief facilitators.
Onboard participants
Participants complete the guided profile and receive their first personalised starting point.
First useful actions
Participants attempt at least one recommended action. Feedback and completions are captured as they go.
Review and report
Read the aggregate picture: where people started, what they tried, what they returned to, and how confidence moved.
What we'd agree to judge it on
Onboarding completion
Share of invited participants who finish the profile.
First useful action
Share who mark at least one recommendation as completed.
Return engagement
Share who come back after their first session.
Self-reported confidence change
Movement on the same 1–5 confidence scale, before and after.
Targets are set with the partner at the start of the pilot. AIAN publishes no benchmark figures, because we have no evidence base to quote yet.
Working today
- Guided seven-step adoption profile with save and resume
- Versioned, AIAN-owned profile schema with stable enum values
- Deterministic local recommendation rules with visible rationale
- Step-by-step actions, copyable starter prompts and completion tracking
- Useful / not-for-me feedback capture
- Profile export and full local data deletion
On the roadmap
- AIAN-hosted accounts with server-side authorisation and per-person row-level access
- Multi-language content (the interface currently ships in English)
- Consent-aware channel orchestration across email, WhatsApp and SMS
- Mapped contact sync to a partner CRM for segmentation and lifecycle
- Cohort analytics and facilitator dashboards
- Adaptive recommendations informed by aggregate feedback
Where the data lives, and what it's allowed to do
AIAN owns the adoption record
The versioned AdoptionProfile schema is the source of truth: stable enum values, a schema version, created and updated timestamps, and defensive parsing on every read. Storage sits behind a repository interface, so today's device-local store can be replaced by an AIAN-hosted database without touching product code.
CRM receives a mapped projection
A documented mapping module projects the profile onto HubSpot contact properties for segmentation and lifecycle — multi-selects serialised with semicolons, machine values rather than display labels. It is a pure transform: no credentials exist in this build and no network sync runs.
Consent-aware orchestration
Channel preferences, consent records and subscription status are three separate structures. A stated preference is never promoted to consent, and only an explicit consent record can ever unlock a send. No integrations are active in this prototype.