AIfirst

AIfirst is a concept in technology strategy and product development that describes prioritizing artificial intelligence at the outset of design, implementation, and decision-making processes. It emphasizes building systems around AI capabilities, data pipelines, and machine learning models, with the aim of enabling intelligence-driven functionality from the ground up rather than adding AI capabilities as a later enhancement.

Origins and usage: The term gained traction in the 2010s as companies sought to leverage ML and AI across products and services. It is often contrasted with mobile-first and cloud-first strategies, and with data-driven or human-centered approaches that may delay AI integration.

Principles: Core elements typically associated with an AI-first approach include ensuring data readiness and governance, designing modular architectures that can accommodate models, integrating ML workflows into product roadmaps, prioritizing explainability and transparency, addressing privacy and security from the start, and establishing continuous learning and monitoring.

Applications: AIfirst guides product strategy in software, platforms, and services that rely on AI for features such as personalization, automation, or prediction. It is also used in enterprise IT planning and policy research to frame investigations around data infrastructure and model deployment.

Criticism and considerations: Critics note that AI-first can be resource-intensive and may risk over-identifying AI solutions for problems that do not require them. Governance, bias, accountability, and ethical implications require explicit attention, and human-centered design should remain a consideration to avoid automation pitfalls.

Related topics: AI-first design, data-centric AI, AI ethics.