AI Integration Strategy

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How can businesses prepare a digital strategy to leverage AI and Blockchain Tech?

AI and Blockchain Tech Strategy Operational Checklist.

Problem Definition & Objectives

• Clearly define the business problem you're trying to solve
• Determine if AI agents are the right solution for this problem
• Align AI initiatives with specific business goals and measurable outcomes
• Identify key performance indicators (KPIs) to measure success

Data Readiness Assessment

• Evaluate data availability, accessibility, and quality
• Ensure data is properly instrumented and digitized where necessary
• Assess data cleaning requirements and processes
• Determine if additional data sources are needed

Human Expertise Identification

• Identify domain experts who can articulate current processes
• Document how processes should be reimagined with AI agents
• Assess skills gaps in your team for building and maintaining agents
• Plan for upskilling or hiring to address expertise gaps

Agent Type Selection (TACO Framework)

• Taskers: Consider for singular goals that break down into multiple tasks (most realistic for 2025)
• Automaters: Evaluate for end-to-end processes across multiple systems
• Collaborators: Assess for human-AI partnership opportunities
• Orchestrators: Consider for complex multi-agent systems (longer-term)

Policy & Governance Framework

• Define appropriate autonomy levels for AI agents
• Establish human oversight requirements and checkpoints
• Create kill switches and fallback mechanisms
• Determine where human-in-the-loop is necessary (especially for financial decisions)
• Develop ethical guidelines aligned with organizational values

Technology Infrastructure

• Evaluate build options:
  • Open-source frameworks
  • Commercial platforms
  • Pre-built solutions
• Consider a polyglot approach to maintain flexibility across platforms
• Assess integration requirements with existing systems
• Evaluate scalability needs

Implementation Planning

• Create a phased implementation roadmap
• Prioritize quick-win projects for early success
• Plan for more complex, long-term AI transformations
• Ensure cross-departmental collaboration

Day 2+ Operations

• Develop monitoring systems to prevent agent drift
• Create feedback mechanisms to improve agent performance
• Plan for regular updates as data and requirements change
• Establish maintenance protocols and responsibilities

Risk Management

• Conduct AI risk assessments for individual algorithms
• Perform broader assessment reviews of entire AI programs
• Implement controls to ensure compliance with regulations
• Test for fairness, transparency, and safety

Scale Preparation

• Start with smaller use cases as pilots
• Document learnings from initial implementations
• Create a framework for scaling successful pilots
• Develop metrics to determine when to scale