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EY Guides Enterprises Through Structured Agentic AI Adoption Using Maturity Model
EY, the global professional services network formerly known as Ernst & Young, is positioning itself as a key advisor for companies looking to embed agentic artificial intelligence (AI) into their operations. The firm’s corporate finance consultants, who help chief financial officers (CFOs) navigate capital markets and create sustainable value, are now offering guidance that aligns with a newly developed agentic AI maturity model.
EY’s corporate finance practice is part of a network of member firms that operate as separate legal entities under Ernst & Young Global Limited, a UK company limited by guarantee. The practice provides financial and capital‑market expertise to CFOs, helping them assess risk‑return trade‑offs and improve decision‑making. According to EY’s own materials, the firm’s consultants leverage deep experience in finance to support clients in deploying new technologies, including AI, that can drive long‑term value.
The agentic AI maturity model, which has been discussed in industry articles and technical blogs, divides the adoption journey into three stages: connected insight, coordinated intelligence, and adaptive intelligence. In the first stage, organizations collect and link data to generate insights. The second stage introduces coordinated intelligence, where multiple AI agents collaborate to recommend actions. The final stage, adaptive intelligence, allows agents to act autonomously with human approval and audit.
To move through these stages, the model recommends a set of cross‑industry actions that apply to all stakeholders. The actions include: (1) setting a clear long‑term ambition for AI; (2) designing new systems to be agent‑ready from the outset; (3) treating data as a strategic asset; (4) deploying AI over existing workflows; (5) investing in semantic interoperability so that data can be shared with unambiguous meaning; (6) building governance structures in parallel with technology; and (7) investing in professional capability to manage and operate AI systems.
EY’s advisory services are framed around these actions. The firm’s corporate finance consultants help CFOs articulate a long‑term AI ambition, evaluate the financial impact of agent‑ready systems, and align investment with governance and data strategy. By integrating semantic interoperability principles, EY advises that companies can reduce the risk of data silos and improve the reliability of AI outputs.
The importance of a structured approach is underscored by recent regulatory developments. The European Union’s Artificial Intelligence Act, which entered into force in August 2024, imposes compliance obligations on high‑risk AI systems, including those that operate autonomously. In the United States, executive orders issued in 2023 and 2025 have directed federal agencies to develop a unified national AI policy and to evaluate state AI laws for potential conflicts. These regulatory frameworks increase the need for clear governance and auditability—elements that the agentic AI maturity model explicitly addresses.
In practice, EY’s guidance helps organizations avoid the pitfalls of fragmented AI pilots. By focusing on a clear ambition, agent‑ready design, and robust governance, companies can transition from isolated experiments to integrated, enterprise‑wide AI capabilities. The model’s emphasis on semantic interoperability also supports compliance with data‑protection regulations, such as the EU’s General Data Protection Regulation, by ensuring that data is shared with shared meaning.
Looking ahead, EY’s corporate finance consultants will likely continue to refine their AI advisory offerings as new regulatory requirements emerge and as the technology matures. Companies that adopt the maturity model’s staged approach may be better positioned to realize the business value of agentic AI while maintaining compliance and governance standards.
EY’s corporate finance practice is part of a network of member firms that operate as separate legal entities under Ernst & Young Global Limited, a UK company limited by guarantee. The practice provides financial and capital‑market expertise to CFOs, helping them assess risk‑return trade‑offs and improve decision‑making. According to EY’s own materials, the firm’s consultants leverage deep experience in finance to support clients in deploying new technologies, including AI, that can drive long‑term value.
The agentic AI maturity model, which has been discussed in industry articles and technical blogs, divides the adoption journey into three stages: connected insight, coordinated intelligence, and adaptive intelligence. In the first stage, organizations collect and link data to generate insights. The second stage introduces coordinated intelligence, where multiple AI agents collaborate to recommend actions. The final stage, adaptive intelligence, allows agents to act autonomously with human approval and audit.
To move through these stages, the model recommends a set of cross‑industry actions that apply to all stakeholders. The actions include: (1) setting a clear long‑term ambition for AI; (2) designing new systems to be agent‑ready from the outset; (3) treating data as a strategic asset; (4) deploying AI over existing workflows; (5) investing in semantic interoperability so that data can be shared with unambiguous meaning; (6) building governance structures in parallel with technology; and (7) investing in professional capability to manage and operate AI systems.
EY’s advisory services are framed around these actions. The firm’s corporate finance consultants help CFOs articulate a long‑term AI ambition, evaluate the financial impact of agent‑ready systems, and align investment with governance and data strategy. By integrating semantic interoperability principles, EY advises that companies can reduce the risk of data silos and improve the reliability of AI outputs.
The importance of a structured approach is underscored by recent regulatory developments. The European Union’s Artificial Intelligence Act, which entered into force in August 2024, imposes compliance obligations on high‑risk AI systems, including those that operate autonomously. In the United States, executive orders issued in 2023 and 2025 have directed federal agencies to develop a unified national AI policy and to evaluate state AI laws for potential conflicts. These regulatory frameworks increase the need for clear governance and auditability—elements that the agentic AI maturity model explicitly addresses.
In practice, EY’s guidance helps organizations avoid the pitfalls of fragmented AI pilots. By focusing on a clear ambition, agent‑ready design, and robust governance, companies can transition from isolated experiments to integrated, enterprise‑wide AI capabilities. The model’s emphasis on semantic interoperability also supports compliance with data‑protection regulations, such as the EU’s General Data Protection Regulation, by ensuring that data is shared with shared meaning.
Looking ahead, EY’s corporate finance consultants will likely continue to refine their AI advisory offerings as new regulatory requirements emerge and as the technology matures. Companies that adopt the maturity model’s staged approach may be better positioned to realize the business value of agentic AI while maintaining compliance and governance standards.
