Scale turns large firms’ governance cracks into chasms
This thought leadership for large (more than $5 billion) organizations is part of a three-article feature examining the intricacies of AI adoption for organizations in different revenue bands, using data from our AI Impact Survey. The other articles explore AI for emerging enterprise ($1.1 billion to $5 billion) and midmarket ($100 million to $1 billion) organizations.
Executive summary
Large enterprises are investing heavily in AI, but bigger budgets alone are not delivering stronger results. Grant Thornton’s 2026 AI Impact Survey shows that governance and compliance challenges often prevent the largest organizations from translating AI investment into measurable performance.
Scale introduces complexity, but it also provides advantage. Organizations that combine strategic AI investment with distributed governance, use-case traceability and active board oversight are better positioned to convert scale into sustained innovation, growth and insight.
Scale changes the AI equation. The larger the organization, the more expensive AI mistakes become and the more difficult it is to coordinate, govern and adopt AI consistently.
Midmarket firms can often move quickly, experiment aggressively and absorb mistakes more easily. But organizations operating at global scale face a different challenge. AI deployments touch more systems, more regulators, more customers and more operational risk.
That does not mean large enterprises can afford to move slowly. But they do not need to compete on startup-style speed alone. Their advantage comes from scale, reach, capital and operational depth. The challenge is deploying those advantages consistently across complex organizations without allowing governance and coordination bottlenecks to slow progress.
Grant Thornton’s 2026 AI Impact Survey of 950 business leaders shows that, despite significantly larger AI investments, organizations with more than $5 billion in revenue are not consistently translating those advantages into stronger performance on innovation, revenue growth or insight generation.
Large enterprises have the resources and ambition to drive AI performance, but their scale changes the governance challenge entirely. Large enterprises that adopt AI successfully are pairing aggressive investment with governance and deployment models built around the realities of enterprise complexity.
0:48 | Transcript (PDF - 188.99KB)
Many large organizations are struggling to build that strong governance. This is a problem that frustrates executives in all revenue categories, but manifests most prominently in underperformance at enterprises with more than $5 billion in revenue. In our survey, although the largest firms were most likely to say their AI was not underperforming, they also were most likely to blame governance or compliance barriers when AI did underperform.
| Factors that have contributed to underperformance | $100 million to $1 billion | $1.1 billion to $5 billion | More than $5 billion |
|---|---|---|---|
| Governance or compliance barriers | 46% | 45% | 52% |
| AI is not underperforming | 5% | 10% | 16% |
The reason has everything to do with scale. If you go bigger, there’s a chance you will fail bigger, and these organizations are going big on their AI investments just like their smaller peers. Across all organizations, 83% of finance leaders say their AI budgets have increased in 2026.
Some of the differences where larger organizations are trailing their smaller competitors are small. For example, 52% of the largest firms say governance or compliance barriers are contributing to underperformance, compared with 45% of emerging enterprise companies and 46% of midmarket firms.
That’s a small gap, but if you are at one of the largest firms, you are likely to have more resources, better talent and more mature processes than your smaller competitors. You don’t want to be trailing in anything. The goldfish shouldn’t eat the shark, and the freshman basketball team shouldn’t beat the varsity.
With AI, if you want to be at the top of the food chain, you need to establish and maintain governance that gives you the confidence you need to scale AI effectively.
Governance gaps are becoming bottlenecks
Lack of AI governance drives more underperformance in the largest organizations because scale turns governance gaps into structural bottlenecks rather than isolated issues. The effect compounds as AI use expands across functions, geographies and risk profiles.
Oversight itself also can be a bottleneck. Many large enterprises rely on centralized review or approval bodies for AI oversight. Often, these models were not designed for the volume of AI initiatives now underway. As use cases multiply, centralized controls can become overwhelmed, slowing deployment without improving confidence or results. Smaller organizations with fewer initiatives and tighter decision loops experience less friction and can adapt governance more quickly.
That’s not to say larger organizations are totally ineffective at governance and controls. They are more likely than their smaller peers to say:
- They are very confident they could pass an independent audit of AI governance and controls
- They have a tested incident response plan for AI-specific business disruptions
Nonetheless, emerging enterprise organizations with between $1.1 billion and $5 billion in revenue are slightly more likely than the largest firms to have fully integrated AI and agentic AI across their organizations. The largest firms can improve their AI integration by developing traceability for their use cases.
“You need a formalized, traceable, compliance-oriented approach to developing your use cases that informs you about which ones are really risky and which ones are not,” said Grant Thornton Technology Modernization Partner Adam Wengert.
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Turning strategic focus into disciplined deployment
More than half (52%) of the business leaders at the largest organizations said strategic decision-making during times of uncertainty will be essential for thriving in an AI-driven environment.
This reflects the confidence you develop at a large firm. If you have yearly revenue of more than $5 billion, your C-suite executives almost certainly have strong track records of success.
Leaders of these organizations also are most likely to say strategy is the biggest driver of AI ROI:
| $100 million to $1 billion | $1.1 billion to $5 billion | More than $5 billion | |
|---|---|---|---|
| Strategic decision-making is a key leadership trait in an AI-driven environment | 39% | 43% | 52% |
| Biggest driver of AI ROI is strategy | 49% | 52% | 60% |
But you need to pair strategy with strong governance to deliver AI results, and your strategy needs to be deep rather than broad. AI strategy is most effective when you pay attention to detail throughout the operating model, with AI use cases applied to specific job functions and workers trained to use them.
A depth of strategy also requires a disciplined process of aligning AI pilots with business strategy, scaling only the pilots that demonstrate results, and abandoning the ones that aren’t working. This requires a continuous feedback loop of piloting, learning, evaluating, budgeting, scaling, adapting and eliminating — a process that leaders at the largest organizations seem to understand, even if complexity makes it difficult to implement.
Leaders in this revenue group were most likely to place a high value on continuous learning, adaptability, creativity and innovation in AI development:
| Key leadership traits in an AI-driven environment | $100 million to $1 billion | $1.1 billion to $5 billion | More than $5 billion |
|---|---|---|---|
| Continuous learning and adaptability | 38% | 35% | 44% |
| Creativity and innovation mindset | 33% | 39% | 46% |
Those qualities will serve you well as you push for AI performance at your company, but only if you develop the governance needed to scale effectively. You can do this by:
Implementing distributed governance: Centralized AI review bodies were designed for a smaller portfolio of initiatives. As use cases multiply across functions and geographies, adopt federated governance models that distribute decision-making authority while maintaining enterprise-wide standards. This reduces bottlenecks without sacrificing oversight.
Making every AI use case traceable and documented: A formalized, compliance-oriented framework that documents the rationale behind each deployment builds the traceability needed to scale AI integration with confidence.
Activating the board as a governance catalyst: Our survey shows that boards at the largest organizations were least likely to have integrated AI risk and opportunity into their ongoing oversight. Getting boards to fulfill this basic duty can strengthen accountability across the enterprise and accelerate AI performance.
Large enterprises have the scale, talent and capital to lead in AI. The differentiator will not be how much they invest but how effectively they govern and deploy those investments. Organizations that build governance models aligned to enterprise complexity will convert scale into sustained AI performance. Those that do not risk allowing complexity to limit the value of their investment.
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