Midmarket’s AI Opportunity: Move Fast, Out-Execute

Midmarket companies can’t outspend large ones on AI, but they also don’t need to. Although large companies have more capital and talent to deploy, midmarket firms have a clear advantage: execution. Their tighter investment discipline, faster decision making, and simpler change environments allow them to move more quickly from ambition to impact. These are critical advantages as companies move beyond AI experimentation to capturing value at scale.

Many midmarket companies haven’t yet exploited this advantage, but they should. The prize is substantial. BCG estimates that AI could free up $8 trillion in reduced costs and revenue growth for midmarket companies across 18 key markets over the next five years. Capturing it will depend less on how much they spend than on how well they execute.

Large Companies Have an Early Lead in AI

The AI imperative is growing. Inflation, geopolitical uncertainty, energy price volatility, and supply chain disruptions are creating headwinds for most companies worldwide. Midmarket companies are being squeezed: consider that nearly half of midmarket firms have seen costs rise faster than revenue growth over the past three years.

AI offers an opportunity for all companies to simultaneously drive cost efficiency and sales growth, leading to better returns for investors. Recent BCG research found that although the share of companies creating meaningful value from AI is still small (only about 6%), this group is outperforming their peers in total shareholder returns by about 9 percentage points. That same study found that the impact of AI adoption on value creation is not linear—it occurs in a step-change fashion.

So far, those benefits are more likely to be concentrated among large companies. BCG’s AI Transformation CEO Survey for 2026 shows that companies that execute AI effectively are beginning to realize measurable cost and revenue gains. (See “About the Research.”) Today, large-cap companies are 70% more likely to report significant revenue growth than midmarket peers and 40% more likely to cite significant cost efficiencies.

Why the difference? One element is sheer capital investment. So far, large companies are dramatically outspending midmarket companies, not just in absolute dollars but as a share of revenue. The typical large-cap firm invests about 1.7% of revenue on AI, compared with about 1.3% for midmarket companies. That’s significant, but when that higher investment rate is applied to much bigger revenue bases, the difference becomes even more pronounced. For example, a $20 billion company would invest about $340 million in AI, while a $1 billion company would invest just $13 million—a 26-fold difference. (See Exhibit 1.) Geographic variations in AI investment add another challenge for midmarket companies. (See “AI Investment Varies Significantly by Geography.”)

The gap in talent is nearly as significant. Large companies have the institutional resources to bring in more (and sometimes better) AI talent. In our analysis, large-cap companies recruit for AI-skilled roles at twice the rate of midmarket peers. That difference is consistent across both foundational and specialized AI skills. (See Exhibit 2.)

How Midmarket Companies Can Close the Gap

These gaps in investment levels and talent have given large companies an early lead in AI. But that doesn’t mean the race is over for midmarket companies. It just means they must compete differently. As many organizations are finding, capturing value from AI requires sizable changes in processes, workflows, and organizational behaviors.

Over the long term, winning with AI won’t come from simply spending the most and hiring the biggest teams. It will come from shifting quickly from pilots to enterprise-wide deployment and rapidly translating individual use cases to measurable outcomes. Speed is just as important as scale.

Speed and agility directly favor midmarket companies. Such firms have fewer layers between decision and execution, tighter alignment between leadership and frontline teams, and a more direct link between use cases and business impact. This leads to faster adoption and more immediate value.

Five Priorities for Midmarket CEOs to Out-Execute on AI

For midmarket firms, the advantage is not automatic. Competitive advantage only materializes when companies focus on executing with discipline. Five priorities can help.

Focus resources in areas where AI creates a competitive advantage. Midmarket companies cannot afford to differentiate through AI everywhere. Instead, they need to focus their attention and resources on a smaller number of high-value opportunities. Rather than spreading AI investment proportionally across functions, they should make fewer, more substantial bets in the areas with the greatest potential to create a meaningful impact on cost, revenue, or both.

In practice, that often means prioritizing opportunities based on value and feasibility, buying off-the-shelf solutions where they can capture value quickly, and directing custom AI investment toward the select areas where proprietary data, processes, or expertise can create additional advantage.

The sources of AI value can also vary by market. In developed markets, where human capital is more expensive, the business case for AI may lean more heavily toward productivity, helping employees work more effectively and focus their time on higher-value activities. In emerging markets, where human capital is relatively less expensive, the value equation may tilt more toward enhancing customer experience, augmenting capabilities, and driving growth.

One mid-sized pharma company took this approach as part of a digital and AI transformation. The company concentrated its resources on a smaller number of high-impact opportunities, prioritizing use cases based on value and feasibility. This strategy, supported by a phased implementation roadmap, helped the organization identify approximately $80 million in annual EBITDA potential across commercial, marketing, and operations functions.

Develop better investment discipline. With the right mindset, the relative scarcity of capital, talent, and other resources among midmarket companies can be an advantage rather than a constraint. When every investment matters more, companies are forced to make sharper choices about where to deploy capital and more rigorously track whether those investments are creating value. This is an area where some midmarket companies can improve. In BCG’s survey, midmarket companies lagged larger firms in key measures of investment discipline. Just 11% of midmarket CEOs said they had a clearly identified the financial impact of AI initiatives, and just 38% reported rigorous progress tracking, compared with 19% and 52% for large-cap CEOs, respectively.

That kind of discipline is essential to creating value from AI. Leading companies define the financial impact for every initiative, set baselines and KPIs, and have a good understanding of where value will come from, all before they green-light an AI investment. In our CEO data we found that:

• High performers that see both cost reductions and increased growth are roughly five times more likely to define the financial impact for every AI initiative.
• High performers are 40% more likely to embed finance teams into AI governance.

For midmarket companies, closing this discipline gap is critical to turning scarcity into an advantage. Every AI initiative should compete for capital based on clear economic value, not technological novelty, with accountability and KPIs in place to gauge financial impact. In addition, companies should manage AI efforts like venture portfolios, doubling down on proven winners while rapidly exiting underperforming initiatives.

Use organizational simplicity to create stronger accountability. Leadership accountability is one area where midmarket companies are already strong. Among survey respondents, 45% of leaders at midmarket companies hold business leaders accountable for AI outcomes, compared with just 28% at large-cap firms.

Midmarket CEOs should build on this strength. Smaller companies typically have fewer organizational layers and handoffs than large organizations, so they can make decisions faster. Those attributes can lead to clear ownership for AI initiatives and reduce the risk that accountability becomes diluted across functions, markets, or business units. At the same time, business leaders can shape a larger share of the company’s overall performance. More direct oversight for an AI initiative can lead to better financial results.

Leverage smaller scale to redesign processes and drive change faster. A consistent theme of AI transformations is that most of the value comes from operating model changes rather than technology. In BCG’s experience, only about 10% of the financial impact from AI comes from algorithms and 20% from data and the technology stack. The remaining 70% comes from making changes to people, processes, and workflows. (See Exhibit 3.) Yet in the transformation survey, only 39% of midmarket CEOs reported having realigned processes and decision making, including people and culture changes, compared with 59% of large company CEOs.

For midmarket CEOs, closing this gap starts with moving away from an incremental mindset. Rather than using AI to automate individual steps within existing processes, CEOs should look for opportunities to fundamentally redesign end-to-end processes and the operating model. Doing so also requires concentrating resources behind a few bigger, more transformative bets, rather than spreading investment across many incremental improvements.

Compared with larger firms that have massive workforces spread around the globe, midmarket leaders can redesign processes, change behaviors, and spur AI adoption much faster when they commit. They can embed AI into daily work, tailoring change management programs to the unique needs of their company and pivoting quickly when an initiative isn’t working. This means they can get to enterprise-level adoption—and start capturing value from AI—more quickly.

The example of a leading Southeast Asian bank with more than $1 billion in revenue illustrates the payoff from making change management a central pillar of an AI transformation. To support the program, leaders focused on redesigning processes and roles, implementing new KPIs, and rethinking ways of working. Frontline teams were involved in the design stage from day one, rather than having changes imposed on them afterward. The strong emphasis on operating model changes helped the transformation succeed. Conversion rates increased twofold to threefold, and customer query times accelerated by a factor of ten.

Make the CEO the catalyst for AI initiatives. At companies of all sizes, AI needs to be a CEO-level agenda item. But CEOs at midmarket companies have the advantage of proximity. They have more direct visibility into operational details, customer relationships, and frontline teams. That close proximity means that AI transformations can happen faster.

A global customer experience and digital services provider offers an example of this. It launched a 12-month AI transformation with a clear mandate from the CEO, who emphasized execution. The CEO acted as a catalyst, mobilizing business leaders around that ambition, while leaders across the organization translated the mandate into action through process redesign, new governance, role-based training, and a disciplined focus on linking AI initiatives to value. Once at scale, the transformation will reduce contact center operating costs by an estimated 20% to 25%, with automation pilots delivering up to 70% reductions in some workflows. Overall, the company is on track to deliver tens of millions of dollars in financial impact and positive ROI by year two.

To be clear, CEOs shouldn’t get directly involved in managing AI initiatives or they can risk becoming a bottleneck. Instead, the CEO’s role is to set the ambition, define what success looks like, and ensure AI is tightly linked to the company’s strategy. Execution should be carried out by business leaders and P&L owners, ensuring that there will be clear accountability for results.

By championing AI from the top while empowering the business, midmarket CEOs can create the organizational momentum needed to move from isolated pilots to enterprise-wide value creation.

AI still represents a significant opportunity for midmarket companies, but making the most of it requires an approach tailored to their strengths and not a carbon copy of large-cap firms’ approach. By focusing on fewer high-impact bets and executional certainty, midmarket companies can turn their size into an advantage and position themselves to win in the next era of AI.

The authors wish to thank James Maresco, Rachit Sharma, Joe Micciche, Anaam Raza, Peter Toth, Arthur Vicioso, TengNeng Huang, Sophie Thorup, Taina Puddefoot, Marcus Pinnau, and Kateryna Ulshyna for their contribution to this article.​