Persona-based AI Readiness

Stop Guessing Who's
AI-Ready,
Start Verifying it

Most enterprise AI programs measure logins and course completions, not capability. The iMocha AI Readiness Index replaces self-reported confidence with performance-based evidence, benchmarked role by role across your entire workforce.

Company-wide AI Readiness
68%
of employees meet the role-ready bar,
verified across five AI personas.
Adopter
82% ready
Operator
71% ready
Builder
64% ready
Architect
57% ready
Strategist
49% ready
Ready Developing

Trusted by enterprise talent teams

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What's included · The AI Readiness Index

Five Personas,
From Frontline to C-Suite

Course completions are a proxy. A conversation is proof. Every track below is validated through iMocha's AI Fluency Conversational Assessment engine and scored against anchored, task-specific rubrics, not self-reported checklists.

Talk to an Expert
01

Adopter

Everyday, non-technical AI users

SIGNATURE CAPABILITIES

Generative AI Fluency · Responsible AI Use at Work

TARGET ROLES

Recruiters · Sellers · Marketers · Customer Ops · Finance · HR · Frontline Staff

02

Operator

Hands-on users applying AI to workflows

SIGNATURE CAPABILITIES

Applied Prompting for Business · AI-Assisted Workflow Design

TARGET ROLES

Product Managers · Project Managers · Business Analysts · Team Leads · Ops Managers

03

Builder

Technical creators building with AI

SIGNATURE CAPABILITIES

AI-Assisted Coding · Prompt Engineering for Developers

TARGET ROLES

Software Engineers · Solutions Architects · QA Engineers · Forward-Deployed Engineers

04

Architect

Advanced model & agent builders

SIGNATURE CAPABILITIES

Applied ML & Agentic Engineering · LLM Evaluation & Orchestration

TARGET ROLES

Data Scientists · ML Engineers · AI Platform Engineers · AI Modelers

05

Strategist

Executive, accountable AI leadership

SIGNATURE CAPABILITIES

AI Adoption for Leaders · AI Risk & Governance Stewardship

TARGET ROLES

Executives · Department Heads · CAIOs · CTOs · Compliance & Risk Leaders

One index. Every function. Every level.

Proof, Not Proxies

01

Proof, Not Proxies

Every persona track is scored against real AI task performance.

02

Built Role by Role

Different roles need different AI fluency. Each of the five personas is scoped to the tasks that role actually performs from frontline staff to the C-s.

03

Baseline in Weeks

Deploy as a single assessment wave for a company-wide Index almost immediately. Drill into function, team, or geography, no heavy administrative lift required.

04

Board-Ready Baseline

A defensible baseline of AI capability, from the same engine iMocha uses to validate technical and functional skills, now measuring how people actually use AI at work.

See the assessments at work

Real AI Tasks, Evaluated in Real Time

Two of the signature assessments behind the index, watch how candidates are put through actual AI work and not multiple-choice questions.

AI-Assisted Coding

Watch engineers build alongside AI in a live coding environment, scored on how well they direct, review, and refine what the model produces.

AI-Prompt Simulator

See candidates work through fixed workplace scenarios, scored across six prompt-writing dimensions on a 0-5 fluency scale.

GET STARTED TODAY

See it in Action

See how the AI Readiness Index benchmarks AI fluency across all five personas. From frontline Adopter to executive Strategist, and turns a single assessment wave into a company-wide capability map.

Frequently Asked Questions

1. What is AI-readiness?

AI readiness is whether your workforce can actually apply AI to the work their roles require, not whether they've been trained or given access. It's a capability question, measured role by role, rather than a usage or logins question.

2. How is AI-readiness different from AI adoption?

Adoption measures activity like licenses, logins, course completions. Readiness measures demonstrated capability: can this person do the AI-related tasks their role actually demands.

3. Why aren't course completions enough to measure AI readiness?

Course completions confirm exposure, not competence. Someone can finish every module and still be unable to prompt well, spot a flawed output, or apply AI safely to real work.

4. Why measure AI-readiness by role instead of one company-wide test?

A recruiter, a product manager, and an ML engineer don't need the same AI fluency. A single test is either too shallow for technical roles or irrelevant for frontline staff, so readiness is scoped to what each role performs.

5. How does iMocha actually measure AI-readiness?

Through iMocha's AI Fluency Assessment engine, scored against anchored, task-specific rubrics - real task performance evaluated the way an expert reviewer would, not a multiple-choice quiz.

6. How quickly can we baseline AI-readiness across the organization?

It deploys as a single enterprise assessment wave, so a company-wide baseline comes back in weeks, then you drill into function, team, or geography with no heavy administrative lift.

7. What do we do once we know where the gaps are?

Readiness data feeds a closed loop: benchmark capability, close verified gaps with targeted upskilling through the learning tools you already use, then prove progress with scheduled re-assessment against the original baseline.

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