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Level 1: Individual Use

Overview​

At Level 1, AI is used by individuals and small teams for personal productivity. There's no formal organizational deployment, limited IT involvement, and minimal governance. Users discover AI tools on their own and adopt them for specific tasks.

Think: Using ChatGPT for research, Grammarly for writing, or AI coding assistants for development.

Characteristics​

Scale​

  • Users: Individuals, small teams (2-10 people)
  • Usage: Ad-hoc, task-specific
  • Organizational Support: Minimal to none
  • IT Involvement: None or advisory only
  • Governance: Informal or nonexistent

Use Cases​

  • Research and information gathering
  • Writing assistance and editing
  • Code generation and debugging
  • Data analysis and visualization
  • Meeting summarization
  • Email drafting

AI Interaction Model​

  • Primarily chat interfaces
  • Direct user-to-AI conversations
  • Copy-paste workflows
  • Standalone tools, not integrated systems

Risk Profile​

  • Data Risk: Low (individual data only)
  • Operational Risk: Minimal (doesn't affect business operations)
  • Compliance Risk: Low to moderate (depends on what users input)
  • Lock-In Risk: Very low (easy to switch or stop using)
  • Reputation Risk: Low (limited external visibility)

Vendor Evaluation at Level 1​

Critical Criteria​

USE - Output Quality and Usability

  • Does it produce helpful, accurate results?
  • Is the interface intuitive enough for individuals to use without training?
  • Do users actually find it valuable?

Priority: 🔴 CRITICAL

Why: At Level 1, the only thing that matters is whether individuals find the tool useful. If output quality is poor or the tool is hard to use, adoption won't happen.

Important Criteria​

CHANGE - Basic Customization

  • Can users customize for their specific needs?
  • Can they adjust tone, verbosity, or focus?

Priority: 🟡 IMPORTANT

Why: Individuals have diverse needs. Some customization enables broader adoption.

Lower Priority Criteria​

SEE - Transparency Priority: 🟢 NICE TO HAVE

  • Transparency is interesting but not critical at individual scale
  • Users care about results, not how they're generated

LEAVE - Exit Strategy Priority: 🟢 LOW PRIORITY

  • Switching costs are minimal at Level 1
  • Individual users can change tools easily

LEARN - Capability Building Priority: 🟢 LOW PRIORITY

  • Learning happens organically through use
  • Formal training not necessary for individual tools

ADAPT - Future-Proofing Priority: 🟢 LOW PRIORITY

  • Tool evolution matters but isn't urgent
  • Easy to switch if better tools emerge

Examples of Level 1 Tools​

Appropriate Level 1 Vendors​

  • ChatGPT Plus / Claude Pro: General-purpose AI chat for individuals
  • GitHub Copilot: AI coding assistant for developers
  • Grammarly: AI writing assistant
  • Notion AI: AI features within note-taking tool
  • Otter.ai: Meeting transcription and summarization

Why appropriate: Excellent usability, good output quality, designed for individuals, easy to start/stop.

Inappropriate Level 1 Vendors​

  • Complex enterprise platforms requiring procurement and IT setup
  • Tools requiring extensive training before productive use
  • Solutions with multi-year contracts and steep penalties

Why inappropriate: Overkill for individual use, friction prevents adoption.

Level 1 Decision-Making​

Who Decides?​

  • Individual Users: Choose tools based on personal needs
  • Team Leads: May recommend or approve team usage
  • IT: Usually not involved unless security concerns arise

What to Evaluate?​

  1. Time to Value: Can user be productive in < 30 minutes?
  2. Output Quality: Does it produce useful results consistently?
  3. Cost: Affordable at individual or small team level?
  4. Security: Any obvious data risks with typical use?

What NOT to Worry About Yet?​

  • Integration with enterprise systems
  • Organizational governance
  • Long-term vendor strategy
  • Migration planning
  • Compliance audits (beyond basic data handling)

Risks at Level 1​

Data Leakage​

Risk: Users paste confidential information into public AI tools

Mitigation:

  • Basic data handling guidelines
  • User education on what not to input
  • Consider paid plans with data privacy guarantees

Shadow IT​

Risk: Proliferation of unapproved tools without IT visibility

Mitigation:

  • Keep barriers to adoption low
  • Work with IT on approved tool list
  • Communicate simple security guidelines

Quality Inconsistency​

Risk: Users make decisions based on inaccurate AI outputs

Mitigation:

  • Emphasize AI as assistant, not authority
  • Encourage verification of critical information
  • Share best practices for prompt engineering

Wasted Time​

Risk: Users spend more time fighting bad tools than they save

Mitigation:

  • Allow organic adoption—don't force tools
  • Share success stories to guide tool selection
  • Make it easy to try and abandon tools

When to Move to Level 2​

Signs You're Ready for Level 2​

✅ Multiple individuals using AI successfully for similar tasks ✅ Desire to standardize and integrate AI into department workflows ✅ Need to share AI results across teams ✅ IT and leadership see value and want to support broader adoption ✅ Compliance or security concerns require more control

Signs You Should Stay at Level 1​

⚠️ Adoption is still experimental with mixed results ⚠️ No clear organizational support or budget ⚠️ Users haven't established best practices yet ⚠️ Value is still being proven

Common Mistake​

Moving to Level 2 too fast: Deploying enterprise AI solutions before proving value at individual level. Result: Expensive failures that damage AI credibility.

Transitioning from Level 1 to Level 2​

Prepare for Level 2​

When Level 1 proves value and you're ready to scale:

  1. Document Successful Use Cases

    • What are individuals using AI for?
    • What results are they getting?
    • How much time/money is being saved?

  2. Identify Common Patterns

    • Which use cases are most common?
    • What workflows could benefit from AI integration?
    • Which departments are ready for broader adoption?

  3. Engage IT and Compliance

    • What security/compliance requirements exist?
    • What integration capabilities are needed?
    • What governance will be required?

  4. Evaluate for Level 2

    • Current Level 1 tools may not scale
    • Use Level 2 evaluation criteria
    • Focus on CHANGE (integration) and SEE (governance)

Vendor Transition​

Option A: Upgrade current vendor to enterprise plan

  • Pros: Continuity, existing user familiarity
  • Cons: Many Level 1 tools lack Level 2 capabilities

Option B: Switch to Level 2-capable vendor

  • Pros: Better integration, governance, and scalability
  • Cons: User retraining, workflow disruption

Recommendation: Evaluate your Level 1 vendor's enterprise capabilities early. If weak, plan migration before Level 2 deployment.

Best Practices for Level 1​

Do's ✅​

  • Start simple: Use general-purpose AI tools before specialized ones
  • Encourage experimentation: Let users discover what works
  • Share successes: Help others learn from early adopters
  • Keep barriers low: Don't require approvals for individual use
  • Collect feedback: Learn what users find valuable
  • Set basic guidelines: Simple rules about data handling

Don'ts ❌​

  • Don't over-govern: Heavy processes kill adoption
  • Don't force tools: Let value drive adoption
  • Don't ignore security: Basic data handling rules are essential
  • Don't promise Level 2: Don't commit to enterprise deployment before proving value
  • Don't choose complex tools: Enterprise platforms are overkill at Level 1
  • Don't neglect measurement: Track time saved, user satisfaction

Level 1 Success Metrics​

Adoption Metrics​

  • Number of users trying AI tools
  • Percentage of team using AI regularly
  • Tools being used and for what tasks

Value Metrics​

  • Time saved per user per week
  • Self-reported productivity improvements
  • Quality improvements in outputs

Readiness Metrics​

  • User satisfaction and enthusiasm
  • Clear use cases with quantifiable benefit
  • Interest from leadership and other departments

Example Level 1 Journey​

Month 1-2: Discovery​

  • Individual engineers start using ChatGPT for code explanation
  • Marketing team member tries AI for content drafts
  • Analyst uses Claude for research summarization

Status: Organic, uncoordinated, experimental

Month 3-4: Adoption​

  • 40% of engineering team now using AI coding tools
  • Marketing manager sees quality improvements, asks team to try AI
  • Finance team hears about time savings, wants to try for report writing

Status: Growing adoption, value becoming clear

Month 5-6: Standardization Interest​

  • Multiple teams using AI successfully
  • CTO asks: "Should we get enterprise licenses?"
  • IT concerned about data being pasted into public tools
  • Leadership wants to understand ROI and scale potential

Status: Ready to consider Level 2

Decision Point​

Evaluate: Can current tools scale to Level 2 (integration, governance)?

  • If yes: Upgrade to enterprise plans
  • If no: Evaluate Level 2-capable vendors

Key Takeaways​

  1. Level 1 is about proving value at individual scale

    • Focus on output quality and usability
    • Keep barriers to adoption low
    • Let organic adoption drive scale

  2. Vendor evaluation is simpler at Level 1

    • USE criterion is critical
    • Other criteria are lower priority
    • Easy to switch if tool doesn't work

  3. Level 1 is preparation for Level 2

    • Document what works
    • Identify patterns and opportunities
    • Build organizational AI literacy
    • Evaluate vendors for next stage

  4. Don't stay at Level 1 too long

    • Once value is proven, move to Level 2
    • Staying too long creates shadow IT risks
    • Competitors move faster with Level 2+ deployments

  5. Choose vendors that can scale

    • Even at Level 1, evaluate for Level 2 potential
    • Switching is easy now, expensive later
    • Plan ahead to avoid costly migration

Next Steps​