Operational discipline turns ambition into measurable gains
This thought leadership for midmarket organizations ($100 million to $1 billion) 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 organizations ($1.1 billion to $5 billion) and large organizations (more than $5 billion).
Executive summary
Although midmarket companies have some structural advantages that can speed AI adoption, many are struggling to scale AI and drive measurable returns. Grant Thornton’s 2026 AI Impact Survey finds that gaps in governance, strategy and operational controls are limiting progress, despite strong gains in AI-driven insights. Organizations that pair their agility with clear ownership, risk-based oversight and integrated governance are better positioned to move beyond pilots, scale AI effectively and achieve stronger revenue growth, cost savings and business performance.
Midmarket businesses should have an advantage in the race to meaningfully scale AI. They tend to move faster than larger organizations, with fewer layers between strategy and execution and less complexity to slow decisions down.
But Grant Thornton’s 2026 AI Impact Survey shows that many firms in the $100 million to $1 billion revenue range are still struggling to convert AI activity into measurable returns. These firms trail larger peers on the metrics that determine whether AI spending scales successfully.
The gap traces back to governance, strategy and controls:
| Survey finding | $100 million to $1 billion | $1.1 billion to $5 billion |
|---|---|---|
| Formal AI enterprise strategy implemented in operations | 14% | 30% |
| At least half of core applications are not AI ready | 70% | 39% |
| Extremely well-prepared for AI-related privacy/security challenges | 29% | 63% |
These obstacles reinforce one another. Without an enterprise AI strategy, organizations lack the framework to invest in AI-ready applications or address cybersecurity and privacy risks. Without security preparation, leaders lack the confidence to move core systems toward AI readiness. The cycle stalls progress at every stage.
Some midmarket leaders are uneasy about scaling AI because they are concerned about SOX, audit and risk exposure, while many forward-looking company leaders are finding that AI can also be part of the solution to compliance concerns. This is reflected in the high interest Grant Thornton has seen from clients in internal audit-specific AI use cases such as anomaly detection in expenses and payments.
Midmarket firms already have many of the structural advantages needed to move quickly on AI. The opportunity now is to pair that agility with the governance and operational discipline required to scale AI more effectively and turn early momentum into measurable business results.
Why the business should lead AI adoption
The midmarket’s agility advantage matters most when AI adoption is driven by the business. AI is a tool to support the execution of a business strategy, not an end in itself. If midmarket leaders can move faster to get to the core of the problem they are solving, and find the right tool to solve it, they will quickly move beyond AI hype into real returns.
Too often, organizations defer AI ownership almost entirely to IT. Technology teams play a critical role in implementation, security and infrastructure, but they are rarely the closest to the workflows AI is meant to improve.
“The best place to start is with well-defined use cases and thorough business requirements,” said Grant Thornton Advisory Services Partner Jonathan Eaton. “Then leaders can determine which technologies best support those goals, whether that’s generative AI, agentic AI, automation or existing applications already in place.”
Organizations see stronger results when functional leaders examine their own workflows and identify where AI solves specific operational problems. The 2026 AI Impact Survey report shows that leaders who adjust their workflows and operating models to use AI’s capabilities are the ones producing measurable outcomes.
Businesses in the midmarket have plenty of challenges: smaller budgets, legacy systems and (sometimes) hybrid operating models that arise after acquisitions. But they also often have fewer layers of bureaucracy and are more accustomed to changing direction quickly. That operational closeness also changes how AI adoption spreads through the workforce.
These companies often have a clearer path to workforce adoption because leadership can communicate more directly with frontline teams. The data backs up this trend as AI cascades through the workforce. Just 29% of organizations in the $100 million to $1 billion revenue band identified workforce skill gaps as a primary obstacle to their AI initiatives, compared with 46% in the $1.1 billion to $5 billion range.
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Capitalize on the insight advantage
Midmarket companies are outperforming their larger peers in one critical benefit category: insight generation.
| AI benefit experienced | $100 million to $1 billion | $1.1 billion to $5 billion | More than $5 billion |
|---|---|---|---|
| Cost reduction | 36% | 45% | 56% |
| Revenue growth | 30% | 48% | 43% |
| Improved insights | 51% | 43% | 46% |
Organizations in the $100 million to $1 billion revenue band report stronger AI-driven insight gains than their larger peers.
That advantage likely reflects the structural strengths already visible across the midmarket. Fewer decision layers can make it easier for AI-generated insights to influence operational decisions quickly.
But insight generation alone is not a competitive edge unless it translates into broader financial performance. Midmarket firms still trail larger peers on cost reduction and revenue growth.
The 2026 AI Impact Survey report identifies the AI proof gap — the disconnect between AI investment and the ability to explain, measure and defend what that investment produces. The market has moved past the initial rush to adopt. The question facing leaders now is whether they can stand behind what they've deployed. Organizations without strong governance and controls can’t build the discipline needed to move from piloting to full integration and measurable ROI.
Organizations in the $100 million to $1 billion range trail their larger peers on most of these governance metrics:
| Governance metric | $100 million to $1 billion | $1.1 billion to $5 billion | More than $5 billion |
|---|---|---|---|
| Very confident you could pass an independent audit of AI governance/controls | 18% | 27% | 20% |
| AI-specific incident response playbook developed and tested | 12% | 28% | 38% |
| AI risk/cybersecurity is underfunded | 21% | 11% | 15% |
These gaps outlined are not independent and instead they reinforce one another.
Without an enterprise strategy, organizations cannot prioritize AI-ready applications. Without security and risk readiness, leaders lack the confidence to scale adoption. The result is a cycle where AI activity increases, but measurable outcomes do not.
This governance gap is landing companies in the pilot trap. Midmarket companies are more than twice as likely as their larger counterparts to remain in the piloting phase of AI implementation, and they trail larger peers significantly on full agentic AI integration.
Aside from potentially squandering a structural advantage, this has a real cost: across the full survey, organizations with fully integrated AI are nearly four times more likely to report revenue growth than those still piloting.
For midmarket firms disproportionately concentrated in the piloting phase, closing the governance gap is the fastest path to that performance tier. Strong governance embeds the operating discipline to keep AI pilots aligned with business strategy, scale the pilots that deliver the most ROI and exit the ones that don't.
What scalable AI adoption looks like
Building the operational discipline to scale AI is becoming a defining challenge for midmarket firms.
Across organizations working to move beyond pilots, a few consistent patterns are emerging.
- Clear ownership and decision rights: Organizational simplicity is an advantage. Organizations are assigning accountable business leaders to AI initiatives and defining who approves, monitors and can halt AI applications when needed.
- Risk-based oversight: Firms are increasingly classifying AI use cases by risk and potential business value, applying tighter controls where exposure is high and right-sizing controls in lower risk areas to reduce costs and allow teams to innovate.
- AI inventory and evidence trails: An updated inventory of AI systems and use cases is helping many businesses enable responsible use. A central repository of approvals, testing results and monitoring reports gives leaders clear visibility into what’s running and how it’s performing.
- AI controls embedded into existing governance processes: Rather than building parallel governance structures, many firms are integrating AI oversight into existing security, compliance and disaster recovery processes.
- Greater scrutiny of third-party AI: Organizations are paying closer attention to vendor and partner AI usage to ensure external systems align with internal risk standards.
- Targeted external support: Organizations in larger revenue ranges were more than three times as likely to say they don’t need external support to scale AI as those in the $100 million to $1 billion range. When internal resources fall short, external expertise accelerates AI implementation.
Midmarket firms already have many of the structural advantages needed to move quickly on AI adoption. The next challenge is building the operational consistency needed to scale that momentum into measurable business performance.
Read the full findings in the 2026 AI Impact Survey report.
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This Grant Thornton Advisors LLC content provides information and comments on current issues and developments. It is not a comprehensive analysis of the subject matter covered. It is not, and should not be construed as, accounting, legal, tax, or professional advice provided by Grant Thornton Advisors LLC. All relevant facts and circumstances, including the pertinent authoritative literature, need to be considered to arrive at conclusions that comply with matters addressed in this content.
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