# Qvest Engage AI Maturity Framework

## Purpose

This framework provides a structured orientation for all Qvest Engage employees to:
- Understand their current AI capability level
- Identify next development steps
- Apply AI meaningfully in their role
- Contribute to business value and client impact

It is **not a technical framework only** — it connects:
- Individual skills
- Business impact
- Responsibility and risk

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## Core Principles

1. **AI is a capability, not a role**
   Every function (PM, Design, Dev, Corporate) uses AI differently.

2. **Progression = Value Creation**
   Higher maturity means more business impact — not just better prompting.

3. **Responsibility grows with maturity**
   The higher the level, the more accountability for output and outcomes.

4. **Not everyone needs Level 5**
   Excellence in your role at Level 3–4 is often more valuable than shallow Level 5 skills.

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## Overview of Levels

> This framework operates within the guardrails defined in the **Qvest Engage AI Usage Guideline**.
> 
> Important: The term "Level" in this document refers to **capability and skill development**, not usage risk classification.
> Usage risk and allowed behavior are defined in the AI Guideline.

| Level | Name | Focus | Output Scope | Responsibility |
|------|------|------|-------------|----------------|
| 1 | Assisted Work | Personal productivity | Internal only | Low |
| 2 | Professional Application | Client-ready output | Reviewed deliverables | Medium |
| 3 | Workflow Integration | Team efficiency | Standardized usage | Medium–High |
| 4 | Solution Building | Client value creation | Product / feature level | High |
| 5 | Strategic AI | Business transformation | Portfolio / strategy | Very High |

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# Level 1 – Assisted Work ("Efficiency")

## Description
AI is used as a personal assistant to save time on repetitive or low-risk tasks.

## Typical Activities
- Summarizing documents or meetings
- Drafting emails or texts
- Generating simple ideas or outlines
- Basic code snippets or formatting

## Skills
- Basic prompting (context, task, format)
- Understanding AI limitations (hallucinations)
- Ability to validate obvious errors

## Output Usage
- Internal only
- Heavy editing required
- Copy & paste workflows common

## Risks
- Blind trust in outputs
- Data privacy issues (uploading sensitive data)

## Role Examples
- **PM:** Meeting summaries, user story drafts
- **Designer:** Moodboards, inspiration prompts
- **Developer:** Small snippets, debugging hints
- **Corporate:** Email drafts, research summaries

## How to Reach Level 2
- Learn structured prompting (context + constraints)
- Validate outputs critically
- Start using AI for real deliverables (with review)

---

# Level 2 – Professional Application ("Reliability")

## Description
AI outputs are used in client-facing work — with full human validation.

## Key Shift
"I can defend this output in front of a client."

## Typical Activities
- Creating slides, concepts, documentation
- Writing production-ready code (reviewed)
- Generating UX concepts or flows

## Skills
- Advanced prompting (few-shot, iteration)
- Output validation and refinement
- Data awareness (what can/cannot be shared)

## Output Usage
- Integrated into deliverables
- Reviewed and adapted

## Risks
- Over-reliance without understanding
- Subtle errors in logic or facts

## Role Examples
- **PM:** AI-assisted concept decks, user stories
- **Designer:** Wireframes, UX copy
- **Developer:** Refactoring, test generation
- **Corporate:** Reports, presentations

## How to Reach Level 3
- Combine multiple tools
- Reuse prompts/templates
- Integrate AI into daily workflows

---

# Level 3 – Workflow Integration ("Scalability")

> All workflows must comply with the data classification, tool usage, and security rules defined in the AI Usage Guideline.

## Description
AI becomes part of structured workflows and team processes.

## Focus
Efficiency at scale across projects and teams.

## Typical Activities
- Tool chaining (e.g. ChatGPT + Midjourney + Notion)
- Using AI inside IDEs (Copilot, Cursor)
- Automating recurring tasks

## Skills
- Tool selection and orchestration
- Prompt standardization
- Understanding strengths of different models

## Output Usage
- Reusable assets
- Team-level standards

## Risks
- Tool fragmentation
- Inconsistent quality across team

## Role Examples
- **PM:** AI-assisted backlog generation pipelines
- **Designer:** Asset generation workflows
- **Developer:** AI-assisted development environments
- **Corporate:** Automated reporting

## How to Reach Level 4
- Identify repeatable use cases for clients
- Move from usage → solution thinking
- Understand APIs and integrations (basic level)

---

# Level 4 – Solution Building ("Value Creation")

## Description
AI is embedded into products, features, or client solutions.

## Focus
Creating measurable business value using AI.

## Typical Activities
- Building AI-powered features
- Integrating LLM APIs into products
- Designing AI-driven user experiences
- Developing workflows (e.g. content automation)

## Skills
- API usage (LLMs, embeddings)
- Basic system architecture
- Understanding latency, cost, and scalability

## Output Usage
- Production systems
- Client-facing solutions

## Risks
- Poor architecture decisions
- Cost and performance issues
- Compliance risks

## Role Examples
- **PM:** AI feature definition & prioritization
- **Designer:** AI interaction design (UX for AI)
- **Developer:** LLM integration, RAG systems
- **Corporate:** AI-enabled internal tools

## How to Reach Level 5
- Think beyond features → business models
- Identify new revenue streams
- Develop strategic AI perspectives

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# Level 5 – Strategic AI ("Transformation")

## Description
AI is used to shape products, services, and business models.

## Focus
Company-wide and client-level transformation.

## Typical Activities
- Defining AI strategies
- Creating new AI-driven offerings
- Advising clients on AI transformation
- Coaching teams

## Skills
- AI governance and ethics
- Market understanding
- Strategic thinking

## Output Usage
- Portfolio decisions
- Business models

## Risks
- Misalignment with business value
- Overhyping AI capabilities

## Role Examples
- **PM:** AI product strategy
- **Designer:** AI-driven experience vision
- **Developer:** Platform architecture decisions
- **Leadership:** AI-first company strategy

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# Mapping to AI Usage Guideline

| Capability Level | Typical Usage | Corresponding Guideline Level |
|-----------------|--------------|-------------------------------|
| Level 1–2 | Internal productivity | Level 1 (Assistive AI) |
| Level 2–3 | Client deliverables | Level 2 (AI-augmented Delivery) |
| Level 4–5 | AI in products / systems | Level 3 (AI-driven Solutions) |

This mapping ensures that capability growth always happens within defined governance boundaries.

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# Self-Assessment Guide

Ask yourself:

1. Where do I primarily use AI today?
2. Is my output internal or client-facing?
3. Do I understand and validate what I produce?
4. Do I use AI ad-hoc or systematically?
5. Do I create value beyond my own productivity?

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# Development Path (Practical Next Steps)

## From Level 1 → 2
- Use AI in real deliverables
- Learn structured prompting

## From Level 2 → 3
- Build repeatable workflows
- Combine tools

## From Level 3 → 4
- Identify client use cases
- Prototype AI features

## From Level 4 → 5
- Think in business models
- Drive strategic discussions

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# Final Note

This framework is a **guideline, not a rulebook**.

Success is not reaching Level 5 —
Success is **maximizing impact in your role using AI**.

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*Qvest Engage – AI Capability Development Framework*