Boards are expected to make critical strategic decisions about their companies' approaches to AI. It's tough to do that without a basic understanding of the AI development lifecycle and the drivers of AI systems. This guide helps board members understand the high-level steps of training a model and how to think about data, software, and hardware when developing strategic plans.
The AI model lifecycle generally progresses through three steps: data collection, training and fine-tuning, and deployment and integration. The quality of training data has a significant impact on model performance — garbage in, garbage out still applies. Board considerations for data collection include data governance, data provenance and IP rights, data protection frameworks, and competitive advantage from proprietary data.
Most organizations won't train a foundation model from scratch but will fine-tune an existing model. Board considerations for training include resource allocation and scalability, alignment with business objectives, environmental impact on ESG objectives, ethical guidelines including bias mitigation, intellectual property strategy, and talent acquisition.
Once deployed, models deliver value in real-world situations. Deployment requires ensuring security and performance both independently and jointly with other systems. Board considerations include stakeholder management, performance monitoring with defined KPIs, and incident management with board-level reporting mechanisms.
Three key assets drive the AI lifecycle: data, software, and hardware. Each is subject to changing economics and trends. The value of proprietary, domain-specific datasets has been demonstrated by organizations like Bloomberg, which trained models on its own financial data and outperformed general models on domain-specific tasks.
One of the best ways to address the changing nature of AI is to craft a strategy that is flexible and adaptable. Ensure that products and vendors allow for data portability, so your organization can switch to better solutions as they emerge. By understanding the interplay between data, software, and hardware, boards are better able to guide strategy, ensure responsible adoption, and make informed decisions about resource allocation.