Make good work repeatable

Most organizations can run successful AI pilots. The hard part is making that success repeatable across teams, projects, and time. Without systematic enablement, each new AI initiative starts from scratch, reinventing wheels and repeating mistakes.

Organizations mature through predictable stages. Understanding where you are determines what to focus on next.

*Characteristics:* Success depends on specific people with AI expertise. When they leave or get busy, progress stops. Knowledge exists in heads, not systems. *What to do:* Document everything. Create simple templates for common tasks. Start building a repository of lessons learned. *Don't do yet:* Invest in expensive training programs or enterprise AI platforms. You're not ready.

*Characteristics:* Small teams can reliably deliver AI solutions. Some shared practices exist, but they're informal. Success is still somewhat dependent on individuals. *What to do:* Develop role-specific playbooks. Create quality checklists. Establish peer review processes. Start measuring consistency. *Example playbook sections:* - Data analyst: "How to assess data readiness for AI projects" - Project manager: "Standard AI project phases and checkpoints" - Business stakeholder: "How to write effective AI requirements"

*Characteristics:* Multiple teams can execute AI projects successfully. Shared standards and practices exist. New people can become productive quickly. *What to do:* Standardize tools and platforms. Create formal training programs. Establish centers of excellence. Measure and improve quality metrics. *Quality metrics that matter:* - Time from project approval to first results - Consistency of project outcomes across teams - Speed of new team member productivity - Frequency of preventable mistakes

*Characteristics:* AI capability is a core organizational strength. Innovation happens systematically. Competitors struggle to replicate your AI maturity. *What to do:* Focus on innovation and differentiation. Share knowledge externally. Attract top talent through reputation.

Different tools work at different maturity stages, but some fundamentals apply throughout:

Collect and organize effective prompts for common tasks. Include context about when to use each one and expected results. Example structure: - Task: Meeting summary generation - Context: Weekly team meetings, 30-60 minutes - Prompt: [Specific template with variables] - Expected output: Action items, decisions, next steps - Success metrics: 90% accuracy, 5-minute processing time

Define what "good" looks like for different types of AI output. Make quality assessment repeatable and teachable. For content generation: - Accuracy: Factually correct, no hallucinations - Relevance: Addresses the specific request - Tone: Appropriate for audience and context - Structure: Logical flow, clear organization

Prevent common mistakes through systematic checking. Make implicit knowledge explicit. Pre-deployment checklist: - [ ] Success metrics defined and measurable - [ ] Failure modes identified and mitigated - [ ] Data privacy requirements met - [ ] Stakeholder expectations aligned - [ ] Rollback plan documented

Different roles need different AI competencies. Tailor training accordingly. *Business users:* How to write effective prompts, when to use AI vs. human judgment, quality evaluation *Technical teams:* Model selection, evaluation metrics, monitoring and maintenance *Management:* ROI measurement, risk assessment, strategic planning

Enablement creates compound returns. Each person who becomes competent can help others. Each documented process prevents future mistakes. Each template accelerates the next project. The goal isn't perfection—it's systematic improvement. Capture what works, share it widely, and iterate based on results.

Over-engineering early:

Under-investing in documentation:

Ignoring role differences:

Focusing on tools, not practices:

Making It Stick