Small businesses do not fall behind because they lack ambition. They fall behind because the systems that enable growth are not built for how they operate. Access to customers, capital, and technology increasingly depends on navigating platforms designed for scale, where success is shaped as much by operational capability as by product quality. For many SMBs, the constraint is not awareness of digital tools or AI platforms. It is the ability to adopt and use them within the realities of limited time, limited expertise, and limited financial flexibility.
That gap is becoming more consequential as the digital economy expands. Small businesses account for 99.9% of all firms in the United States, employing over 62 million people and contributing 43.5% of GDP. At the same time, the digital economy has grown into a $4.9 trillion engine, where AI-powered advertising, data-driven targeting, and automated customer acquisition define competitive advantage. The question is no longer whether these tools exist. It is whether the infrastructure behind them is designed to make participation possible at the scale and pace SMBs require.
Stuti Mohan brings over a decade of experience working at the intersection of strategy, analytics, and large-scale monetization systems, with a focus on how growth mechanisms operate across diverse business segments. As a Head of Strategy and Operations, Monetization GTM Initiatives, and a former Wharton Small Business Development Center consultant who continues to mentor small business founders, her work centers on designing and optimizing incentive frameworks that influence how businesses access and adopt digital growth tools, particularly in environments where scale, data, and decision systems intersect. Her background spans strategy consulting, business planning, and marketing analytics, shaping a perspective grounded in both system design and real-world application.
We spoke with her about how incentive design can expand SMB access to AI-led growth, and what it will take to ensure that the next phase of digital innovation does not leave Main Street behind.
What explains the gap between rising AI adoption among SMBs and their ability to translate that adoption into consistent growth outcomes?
Adoption trends suggest that SMBs are moving faster than expected. Small business use of generative AI increased from approximately 40% in 2024 to 58% in 2025, placing them within close range of enterprise adoption levels. This rate of convergence is significantly faster than previous technology cycles, where SMB adoption lagged for years. The acceleration reflects growing accessibility of tools and a clearer understanding of their role in customer acquisition and revenue growth.
However, adoption does not translate directly into effective use. A majority of small business owners continue to report difficulty in reaching customers and driving sales, while a large percentage lack confidence in their marketing strategy. These challenges persist because the barriers are operational rather than informational. Limited capital constrains experimentation, limited technical expertise slows adoption, and limited understanding prevents sustained learning. Without addressing these structural constraints, adoption will continue to rise while outcomes remain uneven.
This gap reflects a deeper issue. Access to AI-powered systems is not determined by availability alone, but by the conditions required to use them effectively. For SMBs, those conditions are often absent, which creates a persistent divide between adoption and impact.
You’ve spent the last few years redesigning a large-scale incentive portfolio aimed at exactly that gap. How did it address the structural barriers SMBs face in adopting digital growth tools?
The work began with restructuring incentives from fragmented programs into a unified system aligned with how businesses actually grow. Historically, incentive portfolios were built independently across regions, products, and customer segments, and were typically awarded to the biggest spenders, resulting in inconsistent experiences and limited visibility into performance. This fragmentation reduced effectiveness, as it failed to account for the varying needs of businesses at different stages.
The redesign focused on creating a customer-first framework where incentives are mapped to specific growth stages. For SMBs, this required pairing financial support with structured guidance. Financial incentives act as a form of growth capital, allowing SMBs to experiment with new tools without taking on disproportionate financial risk. Educational support ensures that once adopted, these tools can be used effectively, increasing the likelihood of sustained engagement.
This approach reflects a broader shift in how growth systems are designed, where incentive precision becomes the mechanism that determines whether advanced tools are actually adopted. In my HackerNoon article, How Retail Coupon Data Changed the Way I Think About Growth Systems, I explored how high-precision targeting models from retail show that incentives are most effective when they are context-specific and aligned with actual user behavior. When that same logic is applied to digital advertising, incentives move beyond one-time offers and become part of a structured system that supports sustained adoption and long-term growth.
Can you walk us through how a generic ad credit becomes scalable growth Lever? What does it take to move from one to the other?
The transition from ad credit to growth infrastructure depends on how incentives are positioned within the system. A generic ad credit functions as a transactional discount, encouraging short-term activity without influencing long-term behavior. In contrast, a structured incentive operates as part of a decision system, identifying which businesses should receive support, at what stage, and in what form.
This requires moving from broad distribution to targeted allocation. The system must evaluate business context, determine potential value – for both the provider and the recipient – from specific tools, and provide both financial and educational support aligned with that context. These systems enable SMBs to test and adopt AI-driven marketing tools that would otherwise remain inaccessible due to cost and complexity. The effectiveness of this approach is not driven by model complexity, but by clarity of application. Incentives must be interpretable and actionable within the constraints SMBs operate under.
Scalability emerges when this logic is applied consistently across large populations. Instead of treating incentives as isolated campaigns addressing bespoke business needs, they become part of an integrated system that supports customer acquisition, retention, and growth. This is what allows small businesses to operate with capabilities that were previously limited to larger advertisers, narrowing the competitive gap rather than widening it. At that point, incentives are no longer promotional tools. They become an enabler of innovation and participation in digital ecosystems.
What are the primary failure points in large-scale incentive systems when applied to SMB segments?
The most significant challenge lies in signal imbalance. Smaller businesses generate less data, making it more difficult to model behavior and predict outcomes accurately. Without careful design, systems tend to optimize toward businesses with stronger data signals, which are typically larger advertisers. This creates a bias that undermines the original objective of expanding access.
Addressing this requires deliberate intervention in both modeling and program design. Systems must be calibrated to operate effectively in low-signal environments, and incentives must be structured to ensure meaningful engagement rather than generic distribution. Without this, programs risk reinforcing existing inequalities rather than reducing them.
A second challenge is organizational alignment. Incentive systems operate across finance, sales, marketing, and engineering functions, each with distinct priorities. Maintaining a consistent objective across these functions requires continuous coordination.
How do incentive systems influence SMB competitiveness at a macro level, particularly in the context of U.S. economic resilience?
The implications extend beyond advertising systems. Small businesses contribute significantly to employment and GDP, making their ability to compete central to economic resilience. When access to growth tools is uneven, the effects compound over time, leading to reduced competitiveness and diminished economic diversity.
Incentive systems provide a mechanism to address this imbalance by lowering barriers to entry for advanced tools. By enabling SMBs to adopt AI-driven marketing systems, these programs help narrow the gap between smaller businesses and larger enterprises. The impact is not limited to individual outcomes. It influences the broader structure of the economy.
The shift toward measurable outcomes reinforces this perspective. Programs are increasingly evaluated based on their ability to improve acquisition efficiency, expand reach, and support sustained growth. This ensures that incentives are aligned with real economic impact rather than surface-level engagement.
What structural gaps remain in expanding access to SMBs?
The most persistent gap remains funding. Many small businesses operate with thin margins and limited working capital, which constrains their ability to commit budget to AI-driven marketing tools long enough to see meaningful return. Without sustained investment, even well-designed tools fail to deliver compounding outcomes. Closing this gap requires incentive structures that reduce financial risk during the adoption phase, allowing SMBs to test, refine, and scale without absorbing the full cost upfront.
A second gap lies in learning resources. Most educational content is still designed for general audiences rather than the specific context of an individual business, and SMBs operate with too little time for generic guidance to translate into operational improvement. This is changing rapidly as AI agents make it possible to deliver personalized, context-aware support at scale. The opportunity is to embed this capability directly into adoption pathways, so that learning becomes continuous and tailored rather than episodic and abstract.
A third gap is the lack of expertise and bandwidth for continuous testing and iteration. Larger advertisers run structured experiments to refine performance, but SMBs rarely have the time, expertise, or data infrastructure to do the same. Without this feedback loop, gains plateau quickly. Building lightweight, automated experimentation into the tools SMBs already use would help close this gap, enabling them to improve outcomes without requiring dedicated analytics resources.
As the SMB advertising market continues to grow, reaching hundreds of billions of dollars in the coming years, closing these gaps will define the next phase of access. Without progress on funding flexibility, personalized learning, and continuous experimentation, a significant portion of SMBs will remain excluded from AI-driven growth systems, reinforcing the very disparities these platforms aim to solve.
