Optimising Your Efforts: Your AI Value Chain Guide for Getting AI’s Full Potential
AI value doesn’t just come at the decision stage - it comes step by step, from data collection to continuous monitoring. Each stage of the AI value chain plays a crucial role. Learn how to strengthen every link to ensure AI success in your organisation.
The Misconception: AI Value is only Generated at the End of the Process
Too many people I talk to believe that the real value of AI only comes from the decision-making stage - but that’s really only part of the story. Every step along the AI value chain contributes to the outcome. Skipping or underestimating the earlier stages like data quality or governance can (and almost certainly will) derail your AI efforts before they even begin.
Understanding the AI Value Chain: From Collection to Decisions
AI’s potential is ultimately gained through a series of critical steps, each step building upon the last to ensure accurate insights and impactful decisions. Here’s a breakdown:
- Data Collection: Gathering raw data from internal systems, IoT devices, social media, etc., is the foundation. The quality of input matters.
- Data Processing and Storage: Clean and process data into formats suitable for analysis.
- Data Quality Assurance: Validate, deduplicate, and ensure accuracy—this step is crucial to reliable outcomes.
- Data Governance: Implement standards for privacy, compliance, and security, ensuring trustworthy data usage.
- Data Analysis and Insights: Leverage models and algorithms to extract actionable insights.
- Model Development & Training: Train AI models with datasets to improve learning.
(You can explore the full guide attached for all the stages and how to optimise each for success.)
