KPMG Global Tech Report 2026: Leading in the Intelligence Age

Originally published by KPMG International. Source: KPMG Global Tech Report 2026: Leading in the Intelligence Age

---

KPMG’s Global Tech Report 2026, published in December 2025, is one of the most comprehensive snapshots of enterprise technology leadership available. Based on 2,500 senior technology executives across 27 countries and eight industries, it captures a sector that is ambitious, uneven, and moving faster than most organizations can manage.

The title—Leading in the Intelligence Age—is both a description of what the best organizations are doing and a challenge to the majority that isn’t.

AI Agents Are Already Everywhere—Sort Of

88% of organizations report they are already embedding AI agents into their workflows, products, and value streams. On the surface, this is a remarkable penetration figure. But KPMG’s analysis goes deeper: the quality and scope of that embedding varies enormously.

Zack Kass, Global AI Advisor and former Head of Go-to-Market at OpenAI, contextualizes this finding: high performers expect about half of their tech teams to be permanent human staff by 2027. The rest will be AI-augmented capacity—a future where small, durable human cores orchestrate large AI ecosystems.

That’s not the present reality for most organizations. It’s the trajectory that separates leaders from laggards.

The ROI Gap Is Enormous

The performance differential between high performers and the broader market is striking:

• High performers: average AI ROI of 4.5x
• Industry average: 2x

High performers achieve more than double the return. The difference is not model quality or vendor selection—it’s readiness, governance quality, execution discipline, and organizational agility.

74% of organizations say their AI use cases are delivering business value. But only 24% achieve ROI across multiple use cases—meaning most organizations have proven AI can work in isolated contexts but haven’t scaled it across their enterprise.

The Maturity Gap Is Significant

68% of organizations aim to reach the highest level of AI maturity by end of 2026. Only 24% are there today. That means 44% of organizations are expecting to compress a multi-year maturity journey into a single year.

KPMG’s Guy Holland, Global Leader of the CIO Center of Excellence, is direct about the risk: “Adoption is rapid, but returns vary widely… Static planning is becoming obsolete. To thrive, organizations need adaptive strategies.”

The report identifies three compounding obstacles preventing organizations from reaching their maturity goals:

1. Tech debt — legacy systems that can’t be quickly integrated with AI pipelines
2. Cost pressures — AI infrastructure is expensive, and ROI timelines are longer than boards want
3. Talent shortages — 53% of organizations still lack the skills to realize their digital transformation strategies

Future Horizons: Quantum and AGI Are Coming

The 2026 report looks beyond the immediate AI agent wave to identify two emerging disruptions organizations should begin preparing for:

Quantum computing combined with AI has the potential to transform optimization, cryptography, and simulation capabilities at a scale current systems cannot approach. KPMG recommends organizations begin building quantum resilience now—partnerships, skills assessments, and infrastructure planning—rather than waiting for the technology to mature.

Artificial General Intelligence (AGI) and Artificial Superintelligence are flagged as emerging risks requiring organizational scenario planning. These are not 2026 concerns, but they are concerns that well-governed organizations are beginning to map.

The Defining Trait: Adaptive Strategy

The throughline across KPMG’s findings is adaptability. Organizations that have built cultures, governance structures, and investment frameworks that can flex with rapid technological change are outperforming those with rigid annual planning cycles.

As the report puts it: “Tech plans are often obsolete before implementation.” Leaders coordinate investment priorities dynamically, clarify strategic decisions quickly, foster cultures that embrace change, and establish strong data foundations as their bedrock.

The Intelligence Age rewards organizations that can move, learn, and reconfigure. Those built for stability in a slower-moving world face the hardest transition.

---

Research Methodology: 2,500 senior technology executives across 27 countries and 8 industries (automotive, consumer/retail, energy, financial services, government, healthcare/life sciences, industrial manufacturing, tech/telecom). All surveyed organizations report annual revenues exceeding $100M.

What This Research Misses

The 88% embedding AI agents figure is almost certainly inflated by how “embedding” is defined. Deloitte’s parallel research finds only 21% of companies planning agentic AI deployment have mature governance models — which is inconsistent with 88% already embedding agents. The definition of “embedding AI agents” likely captures organizations using any AI system with some degree of autonomous behavior, including simple chatbots, recommendation systems, and workflow automation tools that predate the “agentic AI” terminology. Deployment of genuinely agentic AI — systems that autonomously plan, decide, and act across multi-step workflows — is far narrower.

The 44-percentage-point maturity ambition gap (68% targeting peak maturity; 24% there now) assumes organizations can compress multi-year maturity journeys into twelve months. Enterprise technology maturity frameworks consistently find that organizations underestimate implementation timelines by 2–3x. IDC research on digital transformation maturity shows the median organization takes 3–5 years to move from ad-hoc to systematic adoption of transformative technologies. Expecting 44% of organizations to leap to peak AI maturity in 2026 is not consistent with historical technology adoption patterns.

The talent gap data (53% lacking needed skills) interacts with the ROI gap in a way the report doesn’t address. Organizations that lack talent to execute transformation strategies are also the ones reporting 2x industry-average ROI, not 4.5x. KPMG surfaces both findings but doesn’t model the relationship: the ROI gap between high performers and the industry is almost certainly driven partly by talent, not just strategy or technology choices. Cognizant’s 2026 research identifies AI mastery as a “baseline expectation” that all employees will need — which is a much more fundamental workforce transformation than hiring AI specialists.

The AGI and superintelligence “future horizon” section may create misleading urgency. Flagging AGI and ASI as organizational risks requiring “scenario planning” is technically accurate but potentially distracting. Current AI systems, including the most advanced multimodal models, do not exhibit the generalization, goal-directedness, or autonomy that AGI implies. The AI safety research community has significant disagreements about AGI timelines, with serious researchers estimating anywhere from 2030 to beyond 2100. Organizations would benefit more from concrete action on current agentic governance gaps than from AGI scenario planning.

Source: KPMG Global Tech Report 2026