AI is paying off, but governance is lagging behind

2026-07-15 · Source: CIO · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Project & Product Management · Depth: Intermediate, long

Summary

A study commissioned by SAP from Oxford Economics, "The Value of AI," surveyed 2,600 executives across 13 countries, revealing that enterprises plan to invest around \$28 million in AI, expecting a 21% ROI. While 83% of respondents believe agentic AI can fundamentally transform their organizations, only 3% are fully prepared for its deployment. The primary challenge identified is that AI risks are evolving faster than governance frameworks, with SAP's Chief AI Strategy Officer, Sean Kask, emphasizing governance as the greater problem. Significant gaps exist, including only 12% of companies having effective AI governance skills or processes, 38% lacking human-in-the-loop oversight, and 63% missing established access controls for agents. The study also highlights the need for a comprehensive "hire-to-retire" lifecycle for AI agents, encompassing detection, integration, observability, and KPI tracking, alongside leveraging ERP systems and knowledge graphs for precise, context-rich AI applications.

Key takeaway

For CTOs or Directors of AI/ML scaling enterprise AI, prioritize robust governance and transparency over immediate full-scale transformation. Your focus should be on implementing a "hire-to-retire" lifecycle for AI agents and leveraging existing ERP systems with knowledge graphs to ensure controlled, context-rich deployment. Do not delay AI adoption waiting for perfect data, but rather start with high-value, specific agent use cases to build trust and demonstrate value quickly.

Key insights

AI adoption outpaces governance, creating significant enterprise risks despite high ROI expectations.

Principles

• AI risks outpace governance framework development.
• AI agents need a full "hire-to-retire" lifecycle.
• Trust in AI requires stable ERP and human-in-the-loop.

Method

SAP's AI project selection involves assessing business value, technical feasibility, data availability, and ethical/governance aspects. This ensures transparency and prioritization of AI initiatives.

In practice

• Automate periodic financial reporting with agents.
• Optimize production planning using AI agents.
• Embed AI in ERP systems via knowledge graphs.

Topics

• AI Governance
• AI Agents
• Enterprise AI
• ERP Systems
• Knowledge Graphs
• ROI Measurement

Best for: VP of Engineering/Data, Executive, AI Product Manager, Director of AI/ML, CTO, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by CIO.