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Businesses are moving beyond AI experimentation and focusing on the more fundamental questions of what value AI actually delivers and how can management best unlock it. As organisations increase investment and scale AI adoption, attention is shifting from implementation to measurable business outcomes, risk management, and accountability for AI-informed decisions, according to Global AI Pulse Q3 2026<\/em>, a KPMG study of 2,131 senior business leaders across 20 countries and jurisdictions.<\/em><\/p>\n Key findings<\/strong><\/p>\n Investment in AI continues to accelerate globally. Organisations plan to invest an average of US$210 million in AI over the next twelve months, up from US$186 million in Q1 2026, according to KPMG’s Global AI Pulse Q3 2026<\/em> survey. This increase is attributable to businesses expecting to see real income from their investments, with almost nine in ten organisations (89%) reporting measurable value from artificial intelligence.<\/p>\n However, despite ambitious capital expenditure plans, many organisations lack the capacity to properly monitor realised value against the overall costs of AI implementation. While organisations are becoming more sophisticated in their AI deployment, KPMG\u2019s research highlights a significant gap between tracking AI spending and a thorough understanding of its economic impact.<\/p>\n Even though most organisations can see what AI costs them, with 61% of respondents reviewing AI costs during approval and 59% monitoring costs once systems are operational, just 12% of those surveyed are able to consistently assess AI value against the cost of implementation across their entire organisation. This figure rises to 48% among those already reporting an established return on investment from AI implementation, indicating a strong relationship between disciplined planning and consistent monitoring.<\/p>\n Such findings suggest that businesses are entering a new phase of AI maturity, with success increasingly attributable to the management systems supporting AI deployment, including controls, accountability, orchestration, resilience, and cost discipline. As organisations move beyond experimentation, the focus now shifts from AI deployment to running these tools effectively at scale and properly observing, assessing, and analysing the results.<\/p>\n \u201cFor many organisations, the challenge is no longer whether to adopt AI, but how to manage it effectively at scale. The findings show that business value increasingly depends on clear accountability, robust governance, and the ability to measure outcomes against investment,” <\/em>said Yuriy Fedoriv \u2014 Partner, Advisory, Head of Turnaround and Restructuring, Head of Financial Services, KPMG in Ukraine<\/strong>.<\/p>\n Governance and accountability move to the forefront<\/strong><\/p>\n As AI becomes embedded in business operations, organisations are responding by strengthening their relevant governance and accountability structures. More than half (55%) of those surveyed now operate a formal AI management layer, with 53% placing accountability for AI-informed decisions at the C-suite level: 35% of respondents have assigned responsibility to a named C-suite executive, while 18% place accountability directly with the CEO or executive committee. Among organisations reporting an established ROI from implementing AI, 86% have also put in place formal AI management structures.<\/p>\n\n