Artificial intelligence has moved beyond experimentation. Organizations across industries are investing heavily in AI, deploying generative AI and embedding it into core business operations at an unprecedented pace.

This article explores one of three overarching trends shared in our June 2026 press release, C-Suite AI adoption is rising, yet ambition is outpacing enterprise readiness. Based on conversations with more than 1,800 business and technology leaders worldwide, the insights reveal how quickly this transformation is happening. GenAI adoption has increased by 30 percent in the past two years, and 62% of organizations are already applying AI to core business and operational processes.

Yet while AI ambition continues to accelerate, enterprise readiness is not keeping pace. Understanding this gap can help organizations prioritize the investments, governance and operating model changes needed to realize measurable business value.

The challenge has shifted from AI adoption to AI value

Just a few years ago, organizations were exploring AI's potential through pilots and proofs of concept. Today, the conversation has shifted.

AI is being embedded into customer interactions, operational workflows, and enterprise platforms to improve productivity, enhance customer experience and support better decision-making.

This shift represents a significant milestone in enterprise AI maturity. However, adopting AI and realizing value from AI are not the same thing.

The organizations realizing the greatest value are not necessarily deploying the most AI solutions. They are building the enterprise capabilities required to scale AI responsibly, sustainably and effectively.

Enterprise readiness is falling behind

Organizations are pursuing AI faster than they are preparing for enterprise AI adoption.

While AI adoption is becoming widespread, only 40% of organizations report having an enterprise AI strategy. Even fewer—just 20%—extend that strategy across their broader ecosystem of stakeholders.

A similar pattern exists with data. Although data is the foundation of effective AI, only 54% of organizations have established an enterprise data strategy, and only 36% extend that strategy across their stakeholder ecosystem.

These findings highlight a significant enterprise readiness gap. One in two organizations either does not quantify the outcomes of its AI initiatives or lacks an enterprise-wide AI or data strategy.

Organizations are advancing AI programs faster than they are reengineering the governance, data and operating model capabilities needed to scale and measure them effectively. As a result, many struggle to move from successful pilots to enterprise-scale adoption that delivers measurable value.

Why measurable AI value remains elusive

Despite growing investment and enthusiasm, value realization remains inconsistent. Only 51% of organizations report quantifying results from AI adoption. At the same time, outcomes from broader digital transformation strategies have plateaued: 40% of executives report that they are producing expected results from their digital strategies compared to 41% last year.

Organizations are still working to connect AI investments to measurable business outcomes because AI initiatives are not sufficiently aligned to business priorities, modernization efforts and clear success metrics. As AI investment accelerates, organizations need to move beyond measuring technical outputs and focus on the AI-to-ROI connection, understanding how AI contributes to financial performance, operational effectiveness, customer outcomes and strategic goals.

Increasingly, organizational and foundational readiness, not technology, is emerging as stronger predictors of AI success. 

Building the foundations for AI value

Our experience working with organizations across industries shows that successful AI transformation requires more than deploying technology. It requires a disciplined approach that connects strategy, governance, operations, data and execution.

Organizations that consistently generate measurable AI value focus on six interconnected areas.

1. Intent: Start with business outcomes

Successful AI initiatives begin with a clear understanding of where AI can create meaningful business impact. Rather than pursuing technology for its own sake, organizations identify high-value opportunities aligned with strategic objectives and measurable business outcomes.

Case in point: Large federal agency modernizes a mission-critical financial system with AI-powered insight.

2. Architecture: Define and measure value

Leading organizations establish a clear framework for defining, tracking and measuring AI-to-ROI. This means establishing business metrics before implementation and continuously measuring outcomes throughout the AI lifecycle.

Case in point: Global insurance leader elevates its software delivery speed, accuracy and efficiency with our multi-layered generative AI solution.

3. Governance: Build trust and accountability

As AI becomes embedded within critical business processes, governance becomes increasingly important. Organizations are adopting controls, policies and accountability mechanisms that enable responsible AI adoption while building trust among stakeholders, employees and customers.

Case in point: Swedish Board of Agriculture develops ethical guidelines, risk management processes and clear accountability structures for its AI lab.

4. Operations: Embed AI into the business

Many organizations achieve success with pilots but struggle to operationalize AI at scale. AI creates lasting value when it’s embedded into redesigned workflows and operating models and adopted at scale, rather than treated as an isolated capability.

Case in point: Ministry of Defence prepares the workforce, roles, and operating model for transformation, ensuring new capabilities could be embedded into day-to-day operations and adopted at scale.

5. Foundations: Strengthen data and platform capabilities

Governance, modernization and data foundations are key enablers of AI success. Organizations can unlock the full value of AI by modernizing legacy systems and data platforms that often constrain scalability, interoperability and decision-making. High-quality data, modern platforms and scalable technology environments remain essential for effective AI deployment.

Case in point: Finnish Food Authority advances agricultural field monitoring using space data and AI to improve farm subsidy efficiency and equality.

6. Scale: Industrialize what works

Sustainable value is achieved when organizations can replicate successful AI initiatives across business units, functions and ecosystems. Scaling requires standardized processes, reusable assets and a consistent approach to implementation that enables enterprise-wide adoption.

Case in point: Large telco builds a self-service business intelligence platform delivered in just four months with a GenAI-powered text-to-SQL query tool.

Governance, data and modernization are becoming competitive differentiators

As AI matures, foundational capabilities are becoming increasingly important sources of competitive advantage.

Organizations that invest in modern technology, trusted data foundations and robust governance frameworks are better positioned to scale AI while managing risks. These capabilities address common barriers, including fragmented data, legacy systems and regulatory complexity, while building trust among employees, leaders, citizens and customers.

Moving from AI ambition to AI value

Organizations are embracing AI at an unprecedented pace, but moving from AI to ROI requires more than scaling experimentation. Success depends on strengthening data foundations, governance, operating models and workforce readiness while aligning AI initiatives to measurable business outcomes.

The organizations that create the greatest value from AI will not necessarily be those that adopt it first. They will be the ones that build the enterprise capabilities to scale AI responsibly, measure outcomes consistently, and realize greater value from AI.

The opportunity is significant. The challenge now is ensuring that enterprise readiness catches up with enterprise ambition.