As artificial intelligence accelerates innovation across industries, a new working paper argues that governance must evolve from a reactive function to an engineered discipline embedded in growth strategies. Published today, Working Paper No. 1 of the GTMGO Canon, titled 'Engineering Trust: Why the AI Economy May Require a New Executive Management Discipline,' proposes Go-To-Market Governance (GTMGO) as a new executive management discipline designed to help organizations engineer governance into growth rather than applying it after the fact.
The paper introduces the Version 1.0 Freeze of foundational GTMGO concepts, including Governance Engineering as the scientific methodology; the Go-To-Market Governance Officer as the accountable executive; the Governance Velocity Gap™, which describes the challenge when innovation outpaces governance; and GTMGO Thermodynamic-Friction™, the cumulative resistance from slow-evolving governance relative to enterprise change. These concepts aim to provide a unified framework for managing trust in AI-enabled enterprises.
Rather than restating existing legal or compliance frameworks, the paper synthesizes observations from aviation, legal practice, professional sports labor relations, entertainment, broadcasting, healthcare-adjacent governance, privacy, cybersecurity, and enterprise leadership. It applies management science, systems thinking, and engineering methodology to propose a discipline that can be applied across regulated industries. The author, Peter Q. John, a communication and compliance executive, argues that recurring engineering principles exist across trusted professions and that the AI economy requires a new approach to governance.
The GTMGO Canon is being released as a sequence of Working Papers to encourage disciplined inquiry and practical refinement. Executives, directors, governance professionals, lawyers, technologists, engineers, cybersecurity practitioners, privacy leaders, healthcare administrators, financial institutions, regulators, researchers, and academics are invited to review the Working Paper and contribute observations. Meaningful feedback will be documented through the GTMGO Research Notes process and considered for future publications.
Working Paper No. 1 will be released in the coming weeks. For more information, visit the author's LinkedIn profile. The paper is copyrighted by Peter Q. John and represents Version 1.0 of the GTMGO Canon.
