PrepAIr
Running AI
Effective AI management requires clear incident identification, proficiency in problem-solving, integrated AI in value chains, structured training programs, and a strategy for talent development and retention.
In the dynamic world of artificial intelligence, ‘Running AI’ is a process that requires continuous attention. Organizations need to establish procedures for identifying and addressing incidents. This process involves not just detecting incidents but also effectively communicating and resolving them, with operational teams playing a crucial role. Their ability to assess and solve problems is vital for the resilience of AI systems.
Having a central point for reporting AI-related incidents is a strategic advantage. It ensures a coordinated approach and enables the organization to respond swiftly, minimizing downtime and limiting the impact on operations. Guidelines for handling these incidents provide clarity and structure, ensuring that all employees know and understand the procedure.
Applying AI across the entire value chain enhances competitive advantage. AI initiatives should not be isolated within individual silos; they need to be embedded in the core processes of the organization. This means that AI should contribute to streamlining and accelerating processes from start to finish.
A clearly defined plan for managing deployed AI applications is essential for long-term success. This plan should ensure regular maintenance, updates, and the ability to scale as the organization and its AI ambitions grow.
Training and education for employees are crucial for a smooth transition to an AI-driven work environment. These programs should not only impart technical skills but also prepare employees for changing roles and processes. This calls for a proactive approach to learning and development, ensuring that employees can adapt to the rapidly changing demands of their work.
In responding to incidents, organizations must act quickly and decisively. The incident management process should be clear, swift, and efficient, with a step-by-step approach that ensures incidents are minimized and lessons are learned for future prevention.
The 6 core areas of the PrepAIr model
AI Strategy-Execution
AI strategy execution balances clear objectives, multidisciplinary teams, ongoing employee training, and KPIs (Key Performance Indicators) to ensure innovation and adaptability within organizations.
Running AI
Effective AI management requires clear incident identification, proficiency in problem-solving, integrated AI in value chains, structured training programs, and a strategy for talent development and retention.
AI Ecosystem
For successful AI implementation, insight into available knowledge, capacity, and data is essential, complemented by external expertise and partnerships, and supported by solid data management and infrastructure.
AI Data management
AI data management requires meticulous data hygiene, systematic audits, flexible IT architecture, and a roadmap that supports AI integration and future growth, ensuring quality and innovation go hand in hand.
AI Governance
AI Governance requires clear ELSA (Ethical, Legal, Social, and Algorithmic) policy definitions, risk registration, human oversight, balance in innovation, bias mitigation, and transparency for responsible and sustainable AI applications.
AI Vision
A clear vision of AI provides a strategic framework, ensures ethics, manages risks, stimulates innovation, and promotes organization-wide responsibility and future-proofing.
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