KPMG’s Global Tech Report 2026 argues that enterprises are entering an “Intelligence Age” where advantage comes less from owning the latest tools and more from how systematically they are scaled, governed, and embedded into the business. Drawing on a survey of 2,500 tech executives across 27 countries and eight industries, the report finds that most organisations have moved beyond experimental “AI roulette,” but only a small minority have reached truly mature, continuously evolving technology estates. The central question it poses to CIOs and CXOs is whether their ambition for 2026 can match the operational reality needed to get there.
Ambition vs. Maturity: A Narrow Top Tier
Across 10 technology domains—covering AI and automation, cybersecurity, XaaS, data and analytics, digital twins, Web3 and more—79 percent of organisations place themselves in one of the top three maturity stages, signalling that basic strategies and investments are in place. Yet only 11 percent say they are “fully scaled and continually evolving,” while 36 percent are still in the process of scaling and 32 percent admit they are hitting blocks despite having funding and sponsorship.
Cybersecurity and “modern delivery” (Agile, DevOps, low-code/no-code) are the most advanced, with 18 percent and 14 percent of organisations respectively reporting full scale, but other areas lag. Despite this, half of all respondents expect to reach the top maturity tier by the end of 2026 and 83 percent expect to be in the top two tiers, creating a clear execution gap between where organisations stand and where they say they will be in just 12 months.
High Performers: Same Budget, Better Outcomes
Within the sample, KPMG identifies a five‑percent “high performer” group that consistently outpaces peers on both maturity and value. To qualify, these organisations sit in the top two stages for at least five technologies, are at the highest process maturity in at least five tech functions, and report 200 percent or higher ROI on digital investments over the prior 12 months.
While the overall average ROI is around 2x, this group reports 4.5x, even though they do not necessarily spend more relative to revenue; instead, they avoid chronic tech debt, focus more of their budget on growth and transformation, and maintain stronger governance. Only 8 percent of high performers say tech debt frequently prevents them investing in new programmes, versus 45 percent among other organisations, and they are less likely to compromise on basics like security, scalability and data standardisation in the rush to move fast. In practice, this means they spend less time paying for past shortcuts and more time compounding the benefits of earlier modernisation.
Making AI Pay: Strategy, Metrics and Unified Programs
The report confirms rapid AI adoption but more mixed success on value. While 74 percent of organisations say current AI use cases are providing business value, only 24 percent report achieving ROI across multiple use cases at scale, down seven percentage points from the previous survey. At the same time, 68 percent expect to reach the highest level of AI adoption by the end of 2026—“innovating and deploying AI use cases into production at scale, delivering ROI across multiple use cases”—highlighting a optimism–execution gap familiar from the broader maturity story.
One structural issue is measurement: 58 percent of organisations acknowledge that traditional ROI metrics are insufficient for AI, and 55 percent of tech executives struggle to demonstrate and communicate AI value to stakeholders and shareholders. KPMG’s interviews suggest that leading adopters expand their view of value beyond efficiency and headcount reduction to include fraud and risk mitigation, cash‑flow acceleration, decision quality and customer outcomes, and then bake these dimensions into AI KPIs from the outset.
High performers also run AI as a unified programme rather than a series of disconnected pilots: only 2 percent of them report having too many uncoordinated AI projects and teams, compared with 34 percent of other organisations, and 91 percent centralise tech investment prioritisation within IT‑led models.
Operating Model Shifts: Adaptive Strategy, Data Foundations and Human–AI Teams
KPMG underscores how quickly plans now decay: 56 percent of tech leaders say their technology strategies become outdated rapidly because of the pace of change, but only 16 percent of high performers feel similarly exposed. To cope, organisations are trying to balance their tech portfolios more dynamically across maintenance, incremental growth and transformation; on average, budgets for 2025 split roughly 35 percent, 36 percent and 29 percent across these three buckets.
High performers, however, tilt 42 percent of spend toward growth and only 30 percent toward maintenance, reflecting earlier work to tackle legacy and tech debt and a greater focus on compounding value. Data capabilities are a key enabler: across the sample, executives prioritise improvements in data security, analysis and insights, accessibility, and data‑powered forecasting, and 78 percent aim to be scaling or fully scaled in advanced simulation and digital twins by 2026 to support richer scenario planning.
On the workforce side, organisations expect digital assistants and AI agents to represent about 36 percent of core tech FTE capacity within two years, up from 28 percent today, with permanent human staff dropping from 48 percent to 43 percent and contractors reducing slightly. High performers still anticipate keeping around half of their permanent human staff by 2027, signalling a deliberate choice to pair automation with investment in human expertise, particularly in governance, judgment and orchestration roles.
Preparing for Agentic AI, Quantum and the Next Wave
Looking beyond 2026, the report frames agentic AI, quantum computing and more general forms of AI (AGI and ASI) as the “next wave” that will stress‑test today’s architectures, controls and skills. Already, 88 percent of organisations say they are investing in building agentic AI into systems, and 92 percent believe that managing AI agents will become an important skill within five years, prompting new roles, registries and governance structures for digital workers.
At the same time, 41 percent of tech leaders worry they are falling behind in preparing for quantum threats and in implementing post‑quantum cryptography, even as they continue to scale current AI deployments. KPMG’s closing recommendations urge leaders to strengthen data foundations, modernise and rationalise tech stacks, adopt AI‑first but “trust‑by‑design” principles, and deepen ecosystem partnerships with more rigorous attention to data sovereignty, model evaluation and partner geography.
The through‑line is that leading in the Intelligence Age will require treating organisational learning, governance and human adaptability as seriously as capital expenditure on models and infrastructure.
Read more about this report here.
