Enterprise AI Still Has A Maturity Problem

A recent report published in Forbes on June 26, 2026, sheds light on a critical challenge facing businesses today: the persistent maturity problem in enterprise AI adoption. While the integration of Artificial Intelligence, particularly Generative AI, has become a baseline for many organizations, with over half having deployed some form of it, a significant gap exists between adoption and responsible, scalable implementation. The article, authored by Grayson Milbourne, a security intelligence director at OpenText Cybersecurity, points out that only one in five enterprises has achieved a level of AI maturity where systems are fully deployed with comprehensive security risk assessments. This disparity is creating substantial risks, as many companies are moving forward with AI initiatives without clearly defined strategies for privacy, security, or governance. The rapid pace of AI innovation has, in many cases, outstripped the development and implementation of necessary safeguards, a trend that could undermine long-term AI outcomes if not addressed promptly. A key finding from research conducted in partnership with the Ponemon Institute indicates that while a majority of companies report increased difficulty in managing privacy and security requirements due to AI, far fewer have established the policies and controls needed to effectively manage these risks. This disconnect contributes to limited trust in AI systems within enterprises. The article argues that this situation, while exposing weaknesses in existing security and governance models, also presents an opportunity for organizations to fundamentally rethink how these systems are designed and managed from their inception. To safely and reliably implement enterprise-scale AI systems, the report stresses the importance of treating security and governance as integral components from the very beginning, rather than as add-ons. This involves gaining a clear understanding of all AI models, services, and agents operating within the environment and controlling their access. Without proper oversight, companies risk deploying AI capabilities that operate independently of security governance, leading to blind spots that hinder risk identification and policy enforcement. As AI adoption accelerates across various business units, maintaining a clear inventory of AI systems becomes increasingly vital. Furthermore, the article advocates for an identity-first approach, extending access management to include non-human identities such as AI agents. End-to-end security must be embedded across the entire AI lifecycle, from development and training to deployment and integration within applications. This ground-up approach to security and governance is crucial for reducing vulnerabilities. Organizations are advised to incorporate security reviews, testing, governance, and continuous monitoring throughout the AI lifecycle to ensure secure and reliable operations.