KPMG Global Tech Report 2026: The Intelligence Age and What It Means for Business

The Intelligence Age: A Technology Inflection Point

We are entering what KPMG calls the “Intelligence Age” — a period of unprecedented technological acceleration where artificial intelligence is fundamentally reshaping business and society. This isn’t just another tech trend; it’s a paradigm shift from centuries of democratizing information to democratizing expertise itself.

The numbers tell the story: KPMG surveyed 2,500 technology executives across 27 countries and 8 industries, all from organizations with annual revenues above $100 million. What they discovered reveals both the massive potential and the stark challenges facing enterprises in 2026.

According to Seth Patton from Microsoft, we’re moving beyond simply making information accessible to making expertise itself broadly available. This shift is powered by AI capability per dollar that keeps growing exponentially, with subscription-based services making advanced capabilities frictionless to adopt.

The implications are profound: 88% of organizations are already investing in building agentic AI into their systems, and 92% report that managing AI agents will become an important skill within five years. We’re not just talking about automation anymore; we’re talking about artificial colleagues that can reason, adapt, and collaborate.

The Tech Maturity Reality Check

Despite the excitement around AI and digital transformation, there’s a sobering reality check in the data. Only 11% of organizations are currently at the highest maturity stage — fully scaled and continually evolving. Yet 50% expect to reach this level by the end of 2026.

This represents an ambitious leap that many organizations may not be prepared for. The gap between current capabilities and future aspirations suggests widespread over-optimism or significant underestimation of the execution challenges ahead.

Breaking down current maturity levels:

• 36% have funded strategies and are on track with scaling
• 32% have secured funding but face implementation roadblocks
• 21% are in earlier stages of their transformation journey

Interestingly, cybersecurity leads in maturity with 18% of organizations fully scaled, followed by modern delivery practices (Agile, DevOps, low-code/no-code) at 14%. Post-quantum cryptography lags significantly at only 9% fully scaled — a concerning finding given the emerging quantum computing threats.

AI at Scale: Beyond the Hype to Real ROI

While AI experimentation has moved mainstream, scaling AI effectively remains elusive for most organizations. In a counterintuitive finding, KPMG reports that only 24% of organizations are achieving ROI across multiple AI use cases — a 7-percentage-point decline from their previous survey.

This decline suggests that organizations have moved beyond “AI roulette” (scattered experimental bets) but are struggling with the more complex challenge of enterprise-scale deployment. The integration challenges, technical complexity, and organizational change required to scale AI successfully are proving more difficult than anticipated.

However, the ambition remains high: 68% expect to reach the highest level of AI adoption by end of 2026. The question is whether they can bridge the execution gap that’s currently holding them back.

Key AI scaling challenges include:

• Measurement difficulties: 58% acknowledge traditional ROI measures aren’t sufficient for AI projects
• Value communication: 55% struggle to demonstrate AI value to stakeholders
• Organizational fragmentation: 32% have too many disconnected AI projects with limited coordination
• Strategy alignment gaps: 80% of C-suite leaders report clear AI strategy, but only 68% of senior tech managers agree

A standout example comes from Uniphore, where a consulting business started with an AI use case for invoice generation. Within six weeks, AI agents increased team throughput by 5-6x, leading to 20-30 more departments building agents within three months. This demonstrates both the potential and the viral nature of successful AI implementations.

The Hidden Tax of Technical Debt

One of the most significant barriers to technological advancement is technical debt — the accumulated shortcuts and compromises made in the rush to deliver quickly. The KPMG research reveals this is a pervasive and growing problem.

69% of tech executives admit their programs make trade-offs in security, scalability, and data standardization to move fast. This creates a compounding problem where 63% say the cost of fixing tech debt actively holds back progress with new initiatives.

The impact is substantial. Organizations burdened by tech debt find themselves in a vicious cycle: they compromise on foundational elements to ship features quickly, which creates more debt, which then slows down future development and creates additional security and scalability risks.

High performers have broken this cycle. They invest more upfront in solid foundations, allowing them to move faster in the long term. While the average organization spends 35% of their tech budget on maintenance, high performers spend only 30%, freeing up resources for growth and transformation initiatives.

Agentic AI and the Future Workforce

Perhaps the most transformative trend identified in the report is the rise of agentic AI — autonomous software agents that can perform complex tasks, make decisions, and collaborate with humans and other agents. This represents a fundamental shift from tools that humans operate to digital colleagues that work alongside human teams.

The workforce implications are already becoming visible:

• Digital workforce growth: From 28% to 36% of core technology teams by 2027
• Permanent human staff: Expected to decrease from 48% to 43%
• External contractors: Declining from 24% to 21%

This isn’t simply automation replacing humans; it’s the emergence of human-AI collaborative workflows. Organizations will need to develop new management practices, including what some call “HR databases for AI agents” and new skills around agent orchestration and management.

The technology is moving toward multi-agent systems that can coordinate entire value chains, with synthetic data unlocking personalization without privacy breaches, and edge deployments bringing intelligence to stores, clinics, and factories.

What High Performers Do Differently

The most valuable insights in the KPMG report come from analyzing the top 5% of organizations that qualify as “high performers.” These organizations achieve 4.5x ROI even with lower investment relative to revenue, demonstrating that success is about execution discipline, not just spending levels.

High performers differentiate themselves in several key areas:

Tech Debt Management: Only 8% of high performers report being frequently prevented from new investments due to tech debt, compared to 45% of other organizations. They’ve made the upfront investment to build solid foundations.

Strategic Focus: High performers spend 42% of their budget on growth initiatives versus 36% for others, while spending less on maintenance (30% vs. 35%). This isn’t luck — it’s the result of better architecture and more disciplined engineering practices.

AI Value Communication: Only 17% of high performers struggle to communicate AI value to stakeholders, compared to 57% of others. They’ve developed better frameworks for measuring and articulating AI impact.

Organizational Agility: 70% of high performers report being highly resilient to change, compared to 36% of others. Only 33% are frequently impacted by market, regulatory, or tech shifts versus 65% of others.

Data and Analytics Investment: High performers prioritize data analysis and insights (53% vs. 37%), data accessibility (52% vs. 36%), and data-powered forecasting (47% vs. 33%).

Risk-Taking: Paradoxically, 87% of high performers believe they should take more risks on emerging technology, compared to 78% of others. Success breeds confidence in their ability to evaluate and adopt new technologies effectively.

Building Adaptive Organizations for Continual Change

One of the most striking findings is that 56% of organizations report their tech plans quickly become outdated due to rapid change. Traditional strategic planning cycles — often annual or multi-year — are becoming obsolete in an environment where technological capabilities and market conditions shift continuously.

This challenge requires new organizational capabilities:

Adaptive Planning Frameworks: Organizations need to move from static roadmaps to dynamic, continuously updated strategies that can pivot as conditions change. This means shorter planning cycles, more frequent reassessment, and built-in flexibility for course corrections.

Culture of Continuous Learning: 90% of organizations plan to expand and strengthen their technology ecosystem partnerships. In a rapidly changing environment, no single organization can maintain expertise across all emerging technologies. Strategic partnerships become essential for staying current.

Scenario Planning Capabilities: 67% say ineffective forecasting hampers their ability to respond to market shocks. High-performing organizations invest in scenario planning and digital twin technologies to model different futures and prepare adaptive responses.

Governance Evolution: 78% centralize technology investment planning, but high performers are more likely to balance centralized strategy with distributed execution. They maintain architectural standards while enabling rapid experimentation at the edges.

The ROI Equation: How to Measure Success in 2026

The KPMG research reveals that traditional ROI measurement approaches are insufficient for the Intelligence Age. Organizations report an average tech ROI of 200% (2x), but this figure masks significant variation and measurement challenges.

ROI follows a predictable but non-linear pattern:

• Quick-win zone: Early investments in focused areas generate high rates of return
• Integration valley: Returns slow as complexity rises and integration effort increases
• Acceleration zone: ROI picks up again as maturity improves and organizations identify high-value opportunities

The key insight is that higher investment doesn’t guarantee better returns. Success factors include:

Organizational Readiness: High performers achieve better ROI because they’ve invested in foundations that enable rapid scaling. They have better data architectures, cleaner technical debt, and more agile processes.

Portfolio Approach: Rather than betting everything on single large initiatives, successful organizations maintain a portfolio of investments across different risk levels and time horizons.

New Metrics for AI: Traditional ROI measures struggle with AI projects that deliver value through fraud reduction, risk mitigation, accelerated cash flow, and other indirect benefits. Organizations need new frameworks that capture these diverse value streams.

Cost Pressure Management: Organizations with fewer cost pressures report 2.6x ROI compared to those under intense cost pressure. This suggests that short-term financial stress can undermine long-term technology investments.

Preparing for the Next Wave of Innovation

Looking beyond 2026, KPMG identifies several emerging technologies and trends that organizations should begin preparing for now:

Post-Quantum Cryptography: With 41% of organizations worried about falling behind in quantum threat preparation, and only 9% currently at high maturity for post-quantum cryptography, this represents both a significant risk and opportunity. Companies like HPE are already “future-proofing” their server products against quantum security threats.

Advanced Simulation and Digital Twins: 78% of organizations aim to be scaling up or fully scaled in advanced simulation and digital twin technologies by 2026. These capabilities enable real-time environment modeling and sophisticated scenario planning.

AGI and ASI on the Horizon: While the timeline for Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI) remains uncertain, the trajectory is clear. Systems are becoming more autonomous, transferable, and capable of self-improvement, requiring strategic foresight and ethical frameworks.

Quantum Computing: Beyond the security implications, quantum computing will eventually enable new classes of optimization, simulation, and machine learning applications. Organizations should begin identifying use cases and developing quantum-ready architectures.

The report concludes with KPMG’s 8-point agenda for 2026:

1. Accelerate learning to build competitive moats
2. Maximize value through data-driven investment
3. Build adaptability through frameworks and culture
4. Create a future-ready, agent-empowered workforce
5. Adopt an AI-first, trust-by-design mindset
6. Strengthen data foundations and modernize tech stacks
7. Drive strategic ecosystem partnerships
8. Maintain strategic foresight for emerging technologies

The Intelligence Age represents both unprecedented opportunity and significant execution challenges. Organizations that can balance ambitious vision with disciplined execution — learning from the practices of high performers while building adaptive capabilities for continuous change — will be best positioned to thrive in this new era.

Success in 2026 and beyond won’t be determined by access to technology, but by organizational maturity in deploying it effectively. As Zack Kass notes, we’re entering an era of “abundant cognitive capacity at near-zero marginal cost.” The competitive advantage will shift from access to models to imagination, governance discipline, and the ability to orchestrate human-AI collaboration at scale.