For a long time, enterprise AI was dominated by a simple question: Should we even invest in AI?

This question has been answered in practical terms.

New data from the latest CIO survey by RBC Capital Markets, as reported by Business Insider, reveals a clear shift in the market: Companies are already investing heavily in AI and are prepared to increase their spending even further. In the survey of more than 100 CIOs and technology leaders, 100% of respondents stated that they are allocating budgets for AI and large language model projects. In fact, 91% are even creating new budgets for this purpose, rather than simply reallocating existing IT spending. More than half of the companies are already using AI in production, and another 35% expect to move into production within six months. (Business Insider)

That's more than just a budget signal. It's a sign of maturity.

AI is no longer just an innovation project. AI is becoming an operational capability. And that is precisely why the strategic question is shifting:

No longer: Should we invest in AI?
But rather: Are we investing in the right AI capabilities?

For CEOs, CIOs, boards, and transformation leaders, this marks the beginning of a new phase. The winners will not be the companies that talk the loudest about AI or are the quickest to purchase new tools. The winners will be those companies that can objectively measure their AI maturity, compare it with their peers, and derive clear investment priorities from that analysis.

AI costs haven't gone away. But they are no longer the core problem.

One surprising finding from the RBC data: The often-discussed costs of AI appear to be less of a deterrent for many companies than expected. According to Business Insider, nearly nine out of ten respondents said their token budgets were manageable, even though nearly half have already exceeded their original spending plans. Rather than cutting AI spending, many companies plan to spend even more on AI tokens in the future. (Business Insider)

That doesn't mean AI is suddenly affordable. It means something else: Companies are increasingly accepting AI costs as a new reality. The focus is shifting from pure cost control to strategic allocation.

The critical management question is no longer:
“How do we avoid AI spending?”

Instead:
“Which AI expenditures actually increase our competitiveness?”

That is a fundamental difference.

A company may have a growing AI budget and still lack strategic vision. It may launch pilot projects, roll out copilots, test chatbots, and build internal automation systems—without knowing whether these activities are contributing to the right capabilities.

AI Activity is not the same as AI Maturity.

Enterprise AI has evolved from a pilot project to a production reality

RBC data suggests that enterprise AI is moving beyond the experimental stage. More than half of the companies surveyed are already using AI in production; another 35% expect to begin production within the next six months. (Business Insider)

This is an important turning point. As long as AI remains primarily confined to pilot projects, leadership can still treat it as an innovation option. Once AI becomes integrated into production processes, customer interfaces, decision-making logic, knowledge work, software development, operations, and governance, it becomes a management issue.

And control requires measurability.

A board cannot prioritize effectively if it does not know whether the organization has the necessary data infrastructure. A CIO cannot scale AI reliably if governance, security, talent, and technical requirements are not transparent. A CEO cannot allocate capital effectively if AI initiatives are not categorized according to a comparable maturity model.

This is exactly where the new category comes into play: Corporate Intelligence for AI Maturity.

The problem isn't a lack of tools. The problem is a lack of maturity.

Most companies today have no shortage of AI tools. On the contrary: Almost every software suite now includes AI capabilities. CRM, ERP, HR, BI, productivity tools, customer support, marketing automation, software development, analytics—new AI layers are emerging everywhere.

That sounds like progress. In practice, however, a new problem often arises: AI is being implemented in a decentralized manner, but there is no centralized understanding of it.

Which tool delivers real value?
Which department is AI-ready?
Which use cases should be discontinued?
Where are there gaps in data, governance, or accountability?
Where do risks arise?
Where does a sustainable competitive advantage arise?

Without a common measurement system, these questions often remain subjective. Decisions are based on individual cases, vendor narratives, internal opinions, or isolated KPIs. The result is not necessarily transformation, but often strategic uncertainty.

Current research confirms this gap. In June 2026, Accenture and the Carnegie Mellon University Software Engineering Institute published an AI Adoption Maturity Model designed to help organizations move from AI experiments to measurable, repeatable results. Accenture points to a clear execution gap: 86% of C-suite executives plan to increase their AI spending in 2026, but only 21% of organizations are redesigning end-to-end processes with AI at their core. Nearly half of executives also report that AI has had only a minor impact on profitability so far. (newsroom.accenture.com)

The key sentence from the publication: In most cases, the barrier is not the technology itself, but rather a mismatch between expectations, misguided use cases, and poorly executed implementation practices. (newsroom.accenture.com)

That is the essence of AI Maturity.

AI Maturity does not measure how much AI a company uses

A mature organization is not the one with the most AI tools. A mature organization is one that controls, integrates, and prioritizes AI and translates it into measurable business value.

The Carnegie Mellon University Software Engineering Institute puts it similarly: Sustainable AI value comes from discipline, not just speed. True AI maturity is not measured by how much AI an organization uses, but by whether it can build trustworthy and resilient capabilities, robust engineering practices, and governance approaches that are aligned with business objectives. Without maturity, AI adoption risks becoming experimentation on an enterprise scale. (sei.cmu.edu)

This is an important distinction for every level of management.

AI maturity refers to determining whether a company has what it takes to use AI productively over the long term. This includes, among other things:

• Data Quality and Data Accessibility
• Technical foundation and integrability
• AI Governance and Security
• Strategic responsibility at the executive level
• Employee Competence and AI Literacy
• capacity for cultural change
• Measurable prioritization of use cases
• Comparability with companies of similar size, industry, and maturity

Without this perspective, a dangerous pattern emerges: Companies invest more, but fail to gain a clear enough understanding of where they truly stand.

The next bottleneck is oversight

As AI budgets rise, so does the complexity of managing them. In a recent study, IBM describes an “AI Control Gap”: Two-thirds of the CIOs and CTOs surveyed are responsible for AI systems that they do not fully control. 70% report that teams within their organizations are deploying technologies faster than IT can keep track of them. At the same time, 77% of the organizations surveyed say that AI adoption is already outpacing their current governance capabilities. (IBM Newsroom)

The picture becomes even clearer when it comes to finances: IBM expects AI spending to rise from just under 15% of IT budgets in 2025 to nearly 25% by 2027. At the same time, 84% of the tech CxOs surveyed have not yet fully implemented AI financial management, and 85% lack complete real-time transparency into AI spending. (IBM Newsroom)

That is the real danger of the next wave of AI: not that companies are investing too little, but that they are investing more than they can strategically manage.

AI without maturity is expensive.
AI without governance is risky.
AI without benchmarking remains blind.

Europe doesn't just need more investment in AI. Europe needs AI comparability.

This question is particularly relevant for European companies. On June 29, 2026, Accenture published the AI Progress Barometer on Europe’s AI Readiness. The good news: Europe’s AI momentum is growing. The average AI readiness score of European companies improved by 1.6 points over the past six months—a greater increase than North America’s 1.1 points. The bad news: North America remains ahead overall, and within Europe, a gap is widening between large and smaller companies. (Accenture)

Particularly concerning: Europe’s largest companies are now only 2.1 points behind their North American peers. Smaller European companies, on the other hand, trail comparable North American organizations by 7.6 points. Accenture warns that this “long tail” gap could jeopardize Europe’s competitiveness. (Accenture)

This is a clear signal for the European mid-market.

When AI readiness becomes a factor in competitiveness, it is not enough to support individual flagship companies. Europe needs a broader measurement and benchmarking infrastructure that also includes small and medium-sized enterprises, industrial companies, service providers, banks, public institutions, and partner networks.

After all, companies can only improve what they measure. And they can only strategically prioritize what they can compare.

AI talent is becoming a visible indicator of maturity

Another recent data point comes from PwC. The Global AI Jobs Barometer 2026 shows that jobs requiring specific AI skills are growing about eight times faster than the overall job market: 69% growth versus 9%. The average salary premium for AI skills now stands at 62%. At the same time, companies that are particularly adept at using AI are growing faster in terms of headcount and wages than companies with less AI capability. (PwC)

This shows that AI maturity is not just a technological issue; it is an organizational issue.

Anyone who wants to use AI strategically needs more than just models and licenses. They need people, processes, roles, responsibilities, and learning systems. Talent signals, hiring patterns, and the distribution of competencies thus become visible indicators of whether a company is truly AI-ready.

A company that makes a big deal about AI but fails to demonstrate relevant signs of expertise sends a different signal about its maturity than a company that systematically embeds AI expertise in key roles.

The future of AI strategy will therefore not only be evident in the tech stack. It will also be evident in the labor market, in governance structures, in process designs, in data architecture, and in external signals.

From the AI Budget to the AI Investment Logic

The most important management task over the next twelve months will not be to find a way to finance AI. Many companies are already doing that.

The most important task will be to translate AI investments into a clear investment strategy.

This means:

First: Companies need to know where they stand today—not just based on a gut feeling, but in measurable terms.

Second: They need to understand how they measure up against relevant peers—not in general, but by industry, size, and level of maturity.

Third, you need to identify which capabilities need to be improved first. Not every AI problem is a technology problem. Sometimes data quality is lacking. Sometimes governance is lacking. Sometimes ownership is lacking. Sometimes talent is lacking. Sometimes strategic clarity is lacking.

Fourth: You must continuously track progress. AI Maturity is not a one-time workshop. It is a dynamic management system.

This is exactly where the difference between reporting and corporate intelligence lies.

Reporting shows what has happened.
Corporate Intelligence shows what should be prioritized.

Why an AI Maturity Index Is Becoming Relevant Now

If every company uses AI, AI alone will no longer be a differentiator. What will set companies apart is their ability to manage AI better than others.

An AI Maturity Index provides a common language for this purpose. It translates complex organizational realities into comparable metrics. It helps leadership teams discuss not only AI, but also maturity levels, priorities, risk, progress, and competitive positioning.

This is particularly valuable because AI decisions today often affect multiple levels simultaneously:

• The CEO and the board want to know whether AI investments make strategic sense.
• CIOs and CTOs want to know whether systems, data, and governance are scalable.
• CDOs and transformation leads want to know where use cases are truly making an impact.
• HR and People teams want to know whether the organization is building sufficient AI expertise.
• Risk, Legal, and Compliance want to know whether AI remains manageable.
• Investors, banks, and partners want to know how sustainable an organization is compared to others.

An AI Maturity Index is therefore not just another dashboard. It becomes a basis for decision-making.

From AI Maturity to Corporate Intelligence

The next phase of enterprise AI will not be achieved through more experimentation. It will be achieved through better control.

Current data shows that the market is heading exactly in that direction: budgets are rising, AI is making its way into production, costs are perceived as manageable, but governance, control, talent, process design, and ROI remain unresolved. (Business Insider)

That is the moment when AI Maturity transitions from a consulting concept to an operational management framework.

Companies need an answer to a simple but crucial question:

Where do we really stand?

Not based on your gut feeling.
Not compared to the last internal presentation.
But rather compared to relevant companies, industries, and maturity models.

CorpIn has developed the AI Maturity Index specifically to address this question: a Swiss corporate intelligence platform that makes AI maturity measurable, comparable, and manageable. The approach combines internal self-assessment, external signals, benchmarking, and prioritized recommendations for action—so that leadership teams not only know more about AI but also make better decisions.

After all, the next wave won't be led by the companies that invest the most.

It is generated by companies that know where to invest.

Key Takeaways

Enterprise AI is in the budget.
100% of the CIOs and technology leaders surveyed by RBC are allocating budget for AI and LLM projects, and 91% are creating new AI budgets. (Business Insider)

Cost is no longer the main obstacle.
Nearly nine out of ten respondents describe their token budgets as manageable, even though many have already exceeded their original plans. (Business Insider)

The execution gap remains wide.
86% of C-level executives plan to increase AI spending, but only 21% are redesigning end-to-end processes with AI at their core. (newsroom.accenture.com)

Governance and oversight are becoming more critical.
77% of the organizations surveyed by IBM report that AI adoption is already outpacing their governance capabilities. (IBM Newsroom)

AI maturity is becoming a competitive advantage.
Europe is building AI readiness, but smaller companies are lagging significantly behind their North American peers. (Accenture)

FAQ

What Does "Enterprise AI Spending" Mean?

Enterprise AI spending refers to companies' expenditures on AI-related technologies, models, platforms, infrastructure, services, governance, training, and implementation. By 2026, AI spending will increasingly shift from experimental pilot budgets to fixed operational budgets.

Why isn't a larger AI budget enough?

A larger budget does not automatically lead to greater impact. Without a clear data foundation, governance, talent, technical capabilities, and strategic prioritization, companies can invest heavily but achieve little measurable value. Therefore, it is not just the size of the investment that matters, but the organization’s AI maturity.

What is AI Maturity?

AI Maturity describes how well an organization is able to leverage AI strategically, technically, culturally, and organizationally. This includes data quality, governance, technical infrastructure, talent, AI literacy, strategy, security, and the ability to align AI initiatives with business objectives.

Why is benchmarking important in AI?

Without benchmarking, companies assess their AI readiness solely from their own perspective. An AI maturity benchmark shows how an organization stacks up against similar companies, industries, and maturity levels. This makes priorities clearer and investments easier to manage.

How does the CorpIn AI Maturity Index help?

The CorpIn AI Maturity Index makes AI readiness measurable and comparable. Companies gain a structured view of their maturity level, identify blind spots, understand how they stack up against their peers, and can prioritize AI investments more effectively.

Sources

Business Insider / RBC Capital Markets CIO Survey, June 2026: enterprise AI spending, AI budgets, token costs, production adoption. (Business Insider)

Accenture AI Progress Barometer, June 2026: Europe’s AI Readiness Divide and the Gap Among Smaller Companies. (Accenture)

Accenture & Carnegie Mellon University Software Engineering Institute, June 2026: AI Adoption Maturity Model and Execution Gap. (newsroom.accenture.com)

Carnegie Mellon University Software Engineering Institute, June 2026: Definition of AI Maturity and Discipline-Based AI Adoption. (sei.cmu.edu)

IBM Institute for Business Value, June 2026: AI Control Gap, governance, share of the IT budget, and visibility into AI spending. (IBM Newsroom)

PwC Global AI Jobs Barometer, June 2026: AI Skills Growth, Wage Premium, and Talent Signals. (PwC)

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