Welcome to the AI-first era. Your readiness for rapid change is your new competitive differentiator. And smaller businesses, designed to hustle, may understand this more than anyone.
A landmark October 2025 report* from the Wharton School and GBK Collective reveals that multi-billion dollar giants struggle to translate AI use into real cash. Companies with less than $2 billion in annual revenue report speedier financial gains.
Box CEO Aaron Levie posits that the reason is they don’t get bogged down in experimentation.
“What’s more fascinating, and revealing, is the delta between companies that are < $2B in revenue vs. those that [are] > $2B in revenue. Much higher rates of seeing significant gains and much lower rates of still being stuck in pilot phase for the smaller company cohort,” he observes.
Key takeaways:
- Smaller companies may achieve faster AI financial returns because their inherent agility allows them to quickly adopt and implement necessary workflow changes, avoiding the complexity and inertia that slow down large enterprises.
- Redefining business workflows helps maximize AI gains, because simply integrating AI incrementally into old processes fails to capture the radical transformation the technology offers.
- Underinvesting in employee training creates a critical bottleneck, since human capital sets the pace. Ignoring skill atrophy (or the need for new talent) prevents organizations from effectively scaling AI’s potential.
Smaller firms see faster ROI
Corporate scale is now actively slowing down conversions of AI investments into profit.
The Wharton study divides the corporate landscape, identifying companies with over $2 billion in annual revenue as Tier 1 Enterprises. These behemoths are weighed down by complexity. They are more likely to report that it’s “too early” to calculate their AI returns, despite heavy spending. In contrast, mid-sized Tier 2 ($250M-$2B) and smaller Tier 3 (<$250M) firms are reporting faster ROI realization.
“This is certainly intuitive, as small companies can always move faster and adopt new technology quicker,” Levie explained. “But AI magnifies this classic difference because it changes the nature of work vs. just is another technology that you implement and move on from.”
The report reinforces that smaller enterprises continue to lead in AI use. They see themselves as more agile, showing steeper gains than large enterprises on “much quicker” organizational adoption.
AI magnifies this classic difference because it changes the nature of work vs. just is another technology that you implement and move on from.
Reinventing starts with reengineering
“Big or small, the companies that get ahead with AI will be those that start to redefine their workflows in an AI-first way,” Levie says.
Wharton predicts a major turning point in this effort in 2026, when companies with structured ROI will reach “performance at scale.” For that to happen, large enterprises must move past organizational gridlock and commit to a complete overhaul of business processes. For smaller enterprises, the directive is a bit clearer: maintain agility.
“The amount of output you get from AI agents will be directly correlated to how much you change (or reset) your workflow,” Levie says. “It’s a continuum from [businesses] designing a process from scratch at one end to those not willing to change a thing at the other.”
Big or small, the companies that get ahead with AI will be those that start to redefine their workflows in an AI-first way.
Box research shows there are five stages of AI maturity, with “AI managing full functions” being the highest attainable level. Getting there means reengineering work from the ground up — not one task at a time.
Smaller companies, Levie says, “can change their processes more easily.” While this is clearly harder for big companies, he said, it’s key to fully harnessing AI.
As large enterprises close the overall AI usage gap (with 82% of leaders now using GenAI weekly), they still need to translate that activity into structured, measurable positive returns. Company size, once a major advantage, may prove a liability if decades of established rules and siloed systems stifle radical change.
Your people set the pace
Getting teams acquainted with AI is key to driving agility.
Wharton reports that 43% of surveyed leaders warn of skill atrophy in the absence of role design, coaching, and practice — even as 89% believe GenAI tools augment work. Meanwhile, only 69% of mid-managers are optimistic about ROI (compared to 81% of senior managers).
Closing the gap between executive views and ground-level realities can start with a clear, proven strategy to getting employees involved. For this reason, Box adheres to a four-phase approach that empowers teams to learn about AI and pilot experiments across the business. Leadership then selects strategic “big bets” to avoid stalling in the ideation phase.
“While there might have been this belief that you can just spin up your own agent and get going—yes, that’s true for some smaller tasks,” Box Chief Operating Officer Olivia Nottebohm explains. “But if you’re really trying to transform your business, you probably want to be a little more intentional about it.”
Companies must redefine workflows
If AI ROI is a function of speed, not spending, then capturing the same gains as your peers hinges on the right strategy. To get started on your transformation, consider starting with a few research-backed suggestions:
- Give AI work that’s repeatable and requires complex reasoning — “the type of work that is relatively frequent and requires a level of critical thinking that only recent developments in AI can deliver.” (Box AI-first report)
- Focus adoption where it’s proven to be successful, areas that the Wharton report notes include data analysis (73% usage), document summarization (70%), and document editing/writing (68%).
- Establish robust governance frameworks to ensure responsible AI use (Box State of AI Report 2025). The Wharton report notes that business guardrails are tightening, with 64%, +9pp YoY, having adopted data security policies.
- Identify your AI transformation priorities with Box’s complete methodology, from frameworks for AI principles to governance structures and value realization.
