AI Isn’t Just for Big Firms: Why Smaller Practices May Have the Real Advantage

SMEs are better positioned to experiment, deliver value, and redefine what AI success actually looks like.

Does meaningful AI adoption require scale, budget, and the dedicated innovation teams embedded in large accounting groups? In this episode of Accounting ARC, Donny Shimamoto, CPA.CITP, CGMA; Byron Patrick, CPA.CITP; and Liz Mason, CPA, take on a familiar—but increasingly flawed—narrative in the accounting profession: that large accounting teams will define AI success

The reality, they argue, looks very different on the ground.

What starts as a reaction to a “big firm” innovation discussion quickly turns into a broader reframing of how small and mid-sized organizations should think about artificial intelligence—not as a race for efficiency, but as an opportunity to increase value, improve quality, and deepen client relationships.

The Disconnect Between Big-Firm Strategy and Everyday Reality
Patrick, senior product manager for Karbon and co-founder and educator for TB Academy, opens the conversation with a candid observation. After attending a meeting with representatives from large firms—what he jokingly calls the “Final Four”—he walks away with a sense that the conversation has drifted away from reality for most of the profession.

Large firms, he notes, are investing heavily in internal AI teams, proprietary tools, and large-scale infrastructure. But for most accounting teams, that model is neither accessible nor necessary. Instead, smaller firms are operating in a completely different environment—one where impact is high, resources are lean, and decisions happen quickly.

That difference matters.

“I think there’s a little bit of a break from reality,” Patrick says, pointing to the disconnect between enterprise-level innovation and the day-to-day needs of smaller organizations.

A Different Definition of Success
Where large teams often focus on efficiency—saving time, reducing costs, optimizing workflows—Mason, CEO of High Rock Accounting, challenges the premise altogether.

Small firms, she explains, are asking a different question: not “How much time can we save?” but “How much value can we create?” That distinction is subtle but critical.

Rather than chasing ROI calculations or hour reductions, Mason describes a model centered on amplifying the knowledge accountants already have. AI becomes a tool for better communication, stronger insights, and more effective advisory—not just faster output.

“We’re not looking at it from… what’s going to save the most hours,” she says. “We’re looking at what value can this create?”

Shimamoto, founder and managing director of Intraprise TechKnowlogies LLC and founder and inspiration architect for the Center for Accounting Transformation, reinforces the point by reframing the entire efficiency conversation. Efficiency, he notes, is fundamentally a minimization strategy. It reduces cost—but it does not inherently increase impact.

Effectiveness, on the other hand, is about outcomes.

For teams focused on growth, client engagement, and advisory work, effectiveness—not efficiency—should be the primary goal.

Why Smaller Groups Can Move Faster
If AI adoption is about experimentation, iteration, and learning, smaller firms may have a structural advantage. They do not need to roll out new tools across thousands of employees. They do not need to navigate layers of approvals or align across global teams. They can test, adapt, and refine in real time.

Rolling out a new tool to 12 people, as Patrick points out, is fundamentally different from rolling it out to 12,000. That speed creates opportunity.

It also allows teams to tailor their technology stack to individual clients rather than forcing standardized solutions across a broad base. Mason describes this as a “driver’s seat” moment for small firms—one where flexibility and focus outweigh scale.

Just Start: The Case for Experimentation

The one message the hosts repeat most emphatically is to stop waiting. Many professionals, Patrick observes, feel stuck because they do not fully understand what AI can do. They are waiting for training, templates, or a clear roadmap before getting started. That approach, he argues, is backwards.

“You have to just jump in,” he says. “The only way to find out what’s possible… is to use it.”

Trial and error is not a flaw in the process—it is the process. Each interaction builds understanding. Each experiment reveals new possibilities. And over time, that hands-on experience becomes far more valuable than any predefined training program.

Beyond ROI: Rethinking the Value Equation
One of the most pointed critiques in the episode is directed at how the profession evaluates technology. ROI calculators, vendor pitches, and internal business cases tend to focus almost exclusively on cost savings. But that lens captures only part of the picture.

Mason calls this a “philosophical problem” within accounting—a long-standing tendency to justify decisions based solely on financial return, even when the full value is not yet understood.

Patrick agrees, noting that most ROI models ignore the “return” side of the equation entirely—focusing only on reducing cost rather than increasing capability. The result is a narrow view of what technology can do. In practice, AI is enabling entirely new ways of working. It supports brainstorming, accelerates problem-solving, and improves the quality of outputs—even when it does not reduce time.

Mason shares a practical example: using an LLM to work through a complex compensation model. The tool did not necessarily save time, but it helped organize variables, validated assumptions, and produced a better final result.

“That was huge value,” she says. “Is the quality better? Absolutely.”

AI as a Thinking Partner
Across the conversation, AI emerges less as an automation tool and more as a collaborative one. The hosts describe using large language models as brainstorming partners, sounding boards, and external processors—tools that help refine thinking rather than replace it.

For Mason, the ability to “talk through” ideas without requiring another person’s time is a breakthrough. For Patrick, the conversational nature of AI unlocks new ways of working through complex problems. Shimamoto adds that this requires a shift in mindset. Accountants are trained to ask a question and expect a correct answer. But AI interactions are iterative, requiring context, refinement, and dialogue. That shift—from static answers to dynamic exploration—may be one of the most important changes AI brings to the profession.

The Talent and Culture Advantage
Beyond technology, the hosts highlight another area where smaller firms can differentiate: people. As consolidation, private equity investment, and large-firm structures reshape the profession, many professionals are seeking alternatives—environments where they can see the impact of their work, build closer client relationships, and maintain greater flexibility. Smaller organizations are well positioned to meet those expectations.

Shimamoto points to factors like local engagement, direct client access, and workplace flexibility as key advantages. Patrick adds that many professionals value the ability to see the results of their work firsthand—a connection that can be lost in larger organizations. That human element, the hosts argue, remains the true differentiator.

A Shift in the Profession
AI is not leveling the playing field—it is reshaping it. Organizations that succeed will not be the ones with the largest innovation budgets, but the ones willing to experiment, rethink value, and lean into what makes them unique. For small and mid-sized firms, that is not a disadvantage. It may be their greatest strength.

Listen to the full episode