A guide for software engineering leaders on adopting and scaling AI engineering within their organizations. It distinguishes between AI-enabled applications (defined workflows, predictable outputs) and AI agents (autonomous, dynamic decision-making), explaining the implications for scoping, risk, and governance. The guide also covers team staffing and upskilling needs — including token economics, evaluation-driven development, and data literacy — and offers strategies for scaling AI adoption enterprise-wide by focusing on real business problems, building user trust, and measuring business impact rather than model count.
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