AI Playbook for the Agile Enterprise

Purpose

This playbook provides a formal framework designed to accelerate the realization of AI value through proofs of concept before solutions transition into foundational services managed by traditional IT. The objective is to empower teams to innovate responsibly while maintaining alignment with organizational governance and security standards, offering a roadmap for agile AI development and deployment.

Prerequisites

Prior to initiating any AI initiative, the following conditions must be met:

• Adherence to Policies: All company policies and procedures governing the use of AI and data must be strictly followed.
  
• IT Leadership Briefing: IT leadership must be informed of planned activities to ensure transparency and alignment.
  
• Use of Private Models: Only private AI models that do not expose or use company data for external training are permitted.
  

Play 1: Identify Opportunities

Develop a comprehensive list of potential AI use cases through a combination of:

• Stakeholder interviews
  
• Internal research
  
• Large language model (LLM) exploration
  

When conducting interviews beyond your immediate team, consider the following questions to uncover valuable insights:

1. What opportunities do you believe we are not currently leveraging?
   
2. What aspects of your work do you find least enjoyable?
   
3. What tasks do you find most fulfilling?
   
4. Where do you feel you provide the most value to the organization?
   
5. How much of your daily time is devoted to high-value tasks?
   
6. What obstacles prevent you from spending more time on these high-value activities?
   

The output should be a simple, unstructured list comprising notes, ideas, problem statements, friction points, and manual processes.

Play 2: Translate Into Solution Statements

Transform the raw data from Play 1 into clear, consistent one-line solution statements.

Example

• Raw data: “Joe spends four hours daily responding to customer order status inquiries.”
  
• Solution statements:
  
  • “Send daily order status notifications to customers.”
    
  • “Develop a chatbot to handle order status questions.”
    

This process ensures the ideas are articulated in a uniform and actionable manner.

Play 3: Score and Prioritize

Evaluate each proposed solution to ensure that efforts are directed toward initiatives that deliver the highest value with the least effort.

• Assign an Impact score (1 = greatest impact; 5 = lowest impact).
  
• Assign an Effort score (1 = least effort; 5 = greatest effort).
  
• Calculate Priority as the product of Impact × Effort.
  

Lower priority scores indicate higher priority initiatives.

Example Table

Periodically revisit and refine scores based on learnings from initial implementations.

Play 4: Build Prototypes

Leverage self-service integration platforms (iPaaS) such as Make or Zapier to develop prototypes. This phase embraces a “mavericky,” ad-hoc, agile style that prioritizes speed and experimentation over perfection.

Steps:

1. Register and experiment with basic integrations (e.g., email to spreadsheet to task management tools like Asana or Monday). Play around with simple configurations to get comfortable with the platform.
   
2. Define automation triggers and end-points:
   
   • Trigger: Could be time-based (e.g., daily at 9 AM) or event-based (e.g., new sales order confirmation). Direct ERP integration may not be feasible at first; consider workarounds like having the ERP send a confirmation email to a dedicated inbox, which serves as the trigger.
     
   • End-point: Deliver the intended result (e.g., notify customers or create a task), and channel outputs into a consolidated task management system for tracking, notes, and closure.
     
3. Manage outputs: Use tools like Asana or Monday to create actionable human tasks linked to the automation (e.g., “Review sales order data analysis and take action”). This ensures a centralized, reportable process.
   
4. Refine agents: Optionally, integrate steps that extract feedback (e.g., Asana notes) and feed it through an LLM to update your agent definitions or guardrails.
   

This phase is intentionally flexible—focus on creating momentum and demonstrating value quickly.

Play 5: Evaluate and Transition

Once prototypes are in place, conduct a formal review of results. The goal is to determine what stays, what evolves, and what gets retired as you shift from ad-hoc development to enterprise-grade solutions.

• Success measurement: Assess how well each initiative met its objectives.
  
• Identify limitations: Consider if data gaps, lack of connectivity (e.g., to ERP or other core systems), or design issues limited effectiveness.
  
• Path forward: Decide what requires refactoring, what should be discontinued, and what is ready to be integrated into the enterprise AI foundation. Recognize that while the maverick approach fosters rapid development, long-term sustainability demands rigor, structure, and alignment with core IT governance.
  

This agile AI playbook is designed to guide agile teams through structured AI experimentation while maintaining alignment with enterprise standards and objectives.