Data & AI Series | Part 1 — Why Large Enterprises Are Leading the AI Charge

Adoption of AI models has broken the traditional technology adoption cycle

Artificial intelligence isn’t just another technology wave. It’s a fundamental shift in how businesses operate, compete, and grow. Unlike past innovation cycles, AI adoption has upended expectations. Enterprises are leading the charge, startups are scrambling to catch up, and every organization faces critical choices about cost, control, and capability. At RedCloud, we believe success in this era depends on cutting through the hype and building practical, future-ready strategies.

This three-part blog series offers a perspective on AI adoption, including a look ahead to the next frontier—natural code, and a playbook for building agentic AI on your own terms. Along the way, we’ll highlight how RedCloud’s AI services help clients to unlock the potential of big data, gain valuable insights, automate processes, and accelerate transformation.

Why Large Enterprises Are Leading the Charge

For decades, the technology adoption curve followed a predictable pattern: scrappy startups and nimble small businesses would embrace new innovations first, while large enterprises lagged behind, weighed down by bureaucracy, legacy systems, and risk aversion. From personal computers to cloud computing, from social media to mobile-first platforms, the story was always the same—small businesses led, and large enterprises followed.

But, so far, artificial intelligence is rewriting this narrative entirely.

The Great Reversal

Today's data reveals a striking paradox: when it comes to AI adoption, large enterprises are not just keeping pace with small businesses—they're leading the way.

According to June 2025 transaction data in Ramp’s AI Index, only 37% of small businesses have deployed AI in some form, compared to 49% of large enterprises. Looking a level deeper, a 2024 study by Boston Consulting Group revealed only 26% of companies have developed the necessary capabilities to move beyond AI proofs of concept and generate tangible value, and they are predominantly large enterprises. 

The AI adoption patterns we’ll discuss in this post may represent more than just a temporary market dynamic; it could be a signal of a permanent shift in how innovative new technologies proliferate throughout the world economy. Because AI offers such transformative potential, it seems the large enterprises that have pulled ahead of the pack are likely to stay there.

According to MIT’s Sloan School of Management, organizations in the first two stages of their AI maturity had financial performance below their industry’s average, while organizations in the last two maturity stages performed above their industry’s average. The result is a vicious cycle, where large enterprises that can afford to invest heavily in AI capabilities are creating distance from their small business competitors, which in turn enables them to only further increase that disparity over time.

There are signs that small businesses are fighting to catch up. According to Goldman Sachs, small business AI adoption has jumped to 17% from where it was two years ago. What this fails to capture, however, is that this growth is masking a crucial gap in the sophistication and maturity of implementation for AI solutions.

Stated plainly, large enterprises are getting more from AI solutions because they can invest more and scale the value from their solutions further.

The Resource Reality

The primary driver of this technological reversal lies in the fundamental nature of AI adoption itself. Unlike previous technologies that could be implemented incrementally with modest investment, successful AI deployments have required substantial upfront resources and organizational capabilities.

In practice, this means that large enterprises have had a natural advantage in building and implementing AI Solutions. They’ve invested in centralized AI governance models, established centers of excellence for risk and compliance, and created hybrid organizational structures that balance centralized oversight with distributed implementation across their business units. Smaller businesses simply lack the organizational complexity to support such sophisticated deployment efforts.

We’ve also seen large corporations fiercely competing for people with AI skills. Research from IBM shows that 33% of companies cite limited AI skills and expertise as the top barrier to successful AI adoption. Large enterprises can afford to hire teams of AI specialists, data scientists, and implementation managers, whereas smaller businesses often rely on as few as one qualified professional or a handful of self-taught employees to guide their AI adoption journey.

So, large enterprises have a superior infrastructure and a deeper talent pool to draw from.  What has been the result?

The Implementation Maturity Gap

Beyond raw resources, large enterprises are demonstrating superior strategic sophistication in their AI adoption approaches. The data shows clear differences taken by companies of different sizes.

Asana’s report on the State of AI at Work 2025 finds "there’s a widening gap between the 29% of organizations we call AI Scalers and everyone else. AI Scalers don’t just “try AI.” They redesign work around it—and see a 91% productivity boost.". Further, less than one-third of companies report following most of the 12 key AI adoption and scaling best practices identified by McKinsey. Within that, however, large enterprises consistently outperform small businesses across these practices, including establishing dedicated AI teams, creating internal communications about AI value, and embedding AI solutions into business processes effectively.

So, we’re seeing large enterprises focus on AI Solutions that affect their core business processes, rather than just experimenting with support functions, and putting the practices in place that are necessary to be successful. They identify and prioritize their most impactful use cases for AI transformation, whereas small businesses often scatter their limited resources across various experiments with AI solutions. 

The Challenge for Small Businesses

If you’re part of a small business or a startup, what can you do about this? You have good reason to be skeptical that you stand a chance of catching up and reclaiming your role as the innovators/early adopters.

According to Goldman Sachs, about 42% of small businesses say they still don't have access to the resources and expertise necessary to successfully deploy AI. Of those businesses, 60% cite a lack of expertise in applying AI to their specific business context. So, it’s not just about hiring AI specialists; it's about understanding how to transform existing business processes effectively using AI solutions.

As AI becomes increasingly more essential, the traditional startup advantage of speed and agility appears to have been neutralized by AI's complexity and resource requirements. Unlike deploying a mobile app or setting up a social media presence, implementing AI effectively seems to fundamentally favor large enterprises.

The Generative AI Exception

One notable exception to this trend is generative AI adoption, where small businesses have shown stronger initial adoption in certain areas. A 2024 survey from BizBuySell showed that over 75% of respondents reported they use AI tools for Marketing functions. The reason is simple: AI excels at tasks like content creation, copywriting, social media management, email marketing, and analytics, and the tools available for performing these tasks can be used cheaply and immediately without an extensive technical infrastructure.

Will we see more examples like this in the future?  Or perhaps we’ll see a wave of acquisitions of these cheap and easy AI services, putting them behind a barrier of premium licenses within enterprise suite applications?

Don’t Give Up Yet

The AI revolution has been fundamentally different from previous technology cycles in its initial phases. While the internet and mobile computing democratized access to powerful tools, AI has thus far consolidated its advantage among those with the resources to implement it effectively. Being a large enterprise hasn't been a disadvantage in AI adoption; it's been a prerequisite for early leadership.

However, just as the internet eventually became accessible to garage startups, and mobile apps empowered individual developers to compete with established software giants, AI solutions may soon be approaching their own democratization inflection point.

Coming in Part 2

The current corporate dominance in AI adoption has profound implications for entrepreneurship, competition, and economic opportunity for all of us.

In Part 2 of this series, we’ll provide our point of view about how the pendulum may swing back in favor of the nimble innovators who have historically been the drivers of technological disruption.

Small businesses and startups, it turns out, do have an opportunity to reclaim their traditional role as technology pioneers.