The Multi-Year AI Advantage: Building the Enterprise of Tomorrow by Capgemini

The report published by the Capgemini Research Institute in 2026, details a global shift in how organizations approach artificial intelligence. Moving past isolated pilot programs and short-term experimentation, forward-thinking enterprises increasingly view AI as a core, long-term strategic operating capability. Rather than treating AI investment as discretionary, organizations are deploying blended structural strategies, building robust technical data foundations, and navigating sweeping macroeconomic and geopolitical changes.

Central to this transition is the concept of human-AI chemistry, where organizations actively redefine workplace skillsets to ensure high-trust, effective collaboration between human operators and autonomous systems. 

Key Insights 

Survey Demographics & Scope

• Survey Size: The report is based on an online global questionnaire of 1,505 executives. 

• Executive Level: All respondents were senior leaders at the director level and above. 

• Revenue Criteria: Surveyed organizations span 15 countries and 15 industries, each generating more than $1 billion in annual revenue.

Strategic Sentiment & Budget Allocation

• Investment Horizon: 53% of organizations view AI as a long-term capability tool with a sustained 5+ year investment horizon. 

• Budget Increases: On average, organizations expect to allocate 5% of their annual business budget to AI initiatives in 2026, up from 3% in 2025. 

• Strategic Shifting: 64% of organizations have started pausing lower-value AI projects to redirect their resources to high-impact areas, and 63% expect to rationalize or consolidate their AI initiatives over the coming year. 

• Strategic Risk: 53% of leaders believe that failing to scale AI as rapidly as competitors will result in missed strategic opportunities and a lost competitive edge. 

• Preparing for an “AI Winter”: In the event of a global decline in AI optimism, 80% of organizations plan to shift focus to foundational capabilities (data, infrastructure), and 72% will prioritize high-impact, low-cost AI initiatives. 

Technology Adoption & Maturity Levels

• Generative AI Scaling: 38% of surveyed organizations have already operationalized and scaled Generative AI use cases across their businesses. 

• Agentic AI Traction: At a global level, 36% of organizations are piloting or deploying agentic AI systems (with China leading at 46%, compared to 41% in the US). 

• Edge AI Expansion: Nearly half (47%) of organizations report limited or full deployment of Edge AI across functions and locations. Traditional Machine Learning (ML) leads overall organizational adoption with 44% scaled use cases. 

• Traditional AI Success: 58% of organizations state that outcomes from traditional ML and Natural Language Processing (NLP) implementations have exceeded expectations. 

• Make vs. Buy Strategies: 44% of organizations primarily buy commercial off-the-shelf AI solutions, 31% primarily build capabilities in-house, and 14% pivot between decisions depending on the use case. 

• Language Models: 64% of organizations are actively investing in or evaluating Small Language Models (SLMs) or fine-tuned open-source models as alternatives to large proprietary ones. 

Drivers, Enablers, and Performance Metrics

• Adoption Catalysts: Leaders cited stronger executive sponsorship and vision (67%) as the most effective way to accelerate AI adoption, followed by access to external partnerships (59%). 

• Top Value Expectations: The top areas where companies expect to derive the most value from AI include operational efficiency and cost reduction (85%), revenue growth and market expansion (84%), and risk management/compliance (81%). 

• Top KPIs for AI Strategy Success: The most favored metrics used to evaluate AI include ROI or business value realized (73%), number of use cases scaled (68%), and revenue growth attributable to AI (68%). 

• Technical Investment Growth: Over the next 12 months, the fastest-growing technical AI investment allocations will target data foundations and pipelines (72%), followed by computer/infrastructure (64%). 

• Sovereignty Priorities: 54% of organizations now prioritize data sovereignty to ensure sensitive or regulated data remains under their direct control when utilizing external AI models. 

Human-AI Chemistry & Reskilling

• Collaboration Benefits: 66% of organizations report that human-AI collaboration has led to measurable improvements in productivity and decision quality. 

• Workforce Empowerment: 59% report that employees feel empowered to use AI in their day-to-day work. 

• Defined Roles: Only 48% mention having clearly defined roles and responsibilities for humans and AI systems to work together effectively. 

• Redefining Skills: 23% of organizations are actively redefining traditional skillsets to align with AI adoption across most roles, while 41% are doing so in select roles. 

• Newly Emphasized Capabilities: Of the companies redefining roles, 64% are focusing on enhancing human-AI collaboration, 48% on AI model evaluation and 48% on ethical decision-making. 

Conclusion

The Capgemini report concludes that achieving a definitive multi-year AI advantage requires shifting focus away from isolated tool deployment toward system-wide integration and organizational flexibility. True enterprise-wide transformation relies on balancing near-term operational efficiencies (Transform Now) with long-term strategic scaling (Build Tomorrow). Ultimately, technology is moving faster than static enterprise frameworks can absorb; therefore, future market leadership belongs to organizations that continuously re-evaluate their technical infrastructure, secure data sovereignty, and nurture an empowered human-in-the-loop workforce capable of directing fleets of autonomous AI agents safely and responsively.

You can check out the full report here.