Framework
Pillar 5: Scaling AI
Why Scaling AI matters
One-time consolidation and readiness of data combined with a few AI-driven workloads does not a future-ready organization make.
In time, most organizations will turn their attention from future readiness to scaling (and sustaining) their investment in AI and the data platform upon which it depends.
AI Ops
AI Operations (shorthanded as “AIOps”) involve the patterns, best practices, and tools used to develop, deploy, monitor, and maintain AI workloads. It is a sub-discipline within DevOps, focusing on automating and scaling AI capabilities.
Data Governance
Data is the lifeblood of AI and must be well governed in order for AI to begin approaching trustworthiness. Data Governance involves the measures taken to secure, govern, and manage data. It is essential for ensuring data quality, mitigating risks, and supporting AI and other data-driven workloads.
Technical Debt
Technical Debt refers to the accumulated cost of maintaining legacy systems and poor architectural practices. It hinders innovation and creates risks when combined with AI, necessitating the retirement of outdated technologies.
Monitoring and Metrics
Monitoring and Metrics involve tracking the performance and impact of AI initiatives, capabilities, and individual workloads. This includes assessing maturity, adoption, content moderation, technical performance, and return on investment.
Digital Literacy
Digital Literacy is the knowledge and skills required to effectively use AI technologies. It involves educating colleagues on AI capabilities, ethical use, and prompt engineering, ensuring that they can fully leverage AI in their work.