Artificial intelligence has moved from promise to practice in record time—and business leaders are racing to harness it. McKinsey reports that 92% of companies plan to increase their AI investments over the next three years. At the same time, early adopters are already seeing meaningful improvements: Among small businesses currently using AI, 80% report increased efficiency, and nearly half say that AI has improved their data-driven decision-making, according to Goldman Sachs.

Yet, AI isn’t a magic bullet. It takes careful scoping and thoughtful implementation to deliver value. AI isn’t a replacement for the creativity, judgment, or intuition that small businesses rely on. Instead, it’s expanding what small teams can accomplish: According to a 2025 Shopify survey, 69% of business owners who use AI tools do so to generate content. Other popular use cases include helping with data analysis and insights (32%), improving customer service quality (29%), and assisting with product development (23%).

More businesses are discovering that AI’s value truly emerges when it augments human creativity and capability rather than completely replacing it. These are the ways savvy businesses are putting AI to work—and where they’re finding the most success.

Democratizing data science for smarter campaigns

What once required focus groups, surveys, and weeks of analysis can now happen in a matter of minutes with AI. Small businesses are increasingly using AI for market research tasks like analyzing customer reviews, social conversations, and search behavior, uncovering emerging needs before competitors can respond.

Jones Road Beauty uses tools like OpenAI’s Deep Research to analyze thousands of product reviews, Reddit threads, and YouTube comments. From that analysis, the clean-beauty brand’s team identified five real-world personas—such as busy parents and frequent travelers. Those insights informed its Just Enough tinted moisturizer campaign, helping the team refine messaging, select appropriate models, and shape the overall creative direction.

AI-powered analysis isn’t just speeding up data analysis—it’s making it available to anyone at the company. Wallet and accessories brand Ridge is using AI to remove internal bottlenecks that used to slow data-driven decision-making. “We have a data warehouse and all these Shopify reports,” says Ridge CEO Sean Frank. “Instead of doing anything manually, I can take a screenshot, drop it into ChatGPT, and it runs the analysis for me. My entire team can operate like data scientists.” Rather than waiting hours or days for a specialist to crunch numbers, anyone on the team can pull their own insights instantly.

These examples illustrate a broader shift: AI is giving small teams the analytical horsepower of larger organizations. By lowering the barrier to data analysis, brands can move faster, experiment more often, and build campaigns rooted in what customers actually think and do.

Building personalized products

For small businesses with limited engineering resources, launching a new product can be costly and time-consuming. AI is shifting that calculus. By accelerating research, content generation, and user-testing cycles, AI enables teams to bring new digital offerings to market with unprecedented speed. In many cases, it isn’t just accelerating development, but making entirely new categories of personalized, adaptive products possible.

Loftie, a wellness company that designs sleep products, used AI to develop and launch the Loftie Rest app, a digital companion to its signature alarm clock. The app broadened Loftie’s reach and unlocked a new revenue stream, creating a subscription business from the ground up that now has roughly 15,000 members. “We wouldn’t have released this product without AI,” founder and CEO Matthew Hassett says. “It was the initial seed of what our subscription app became.”

Personalized content is the backbone of the Rest app, beginning with Storymaker, which generates tailored bedtime stories using a brief survey and adjustable voice profiles powered by OpenAI and Eleven Labs. Extending personalization even further, Loftie’s Night School feature analyzes correlations between users’ Apple Health data, screen time habits, alarm settings, and self-reported sleep quality. When patterns emerge—like midnight scrolling leading to poor sleep—the tool recommends habit changes or prompts users to block distracting apps. “We use AI to look at patterns and make proactive suggestions to help you ditch your phone at night,” Matthew says.

At every stage, Loftie pairs AI insights with human-created content, from educational modules to meditation flows. AI determines what a user needs, while humans help craft what is delivered. The result is a digital product that continuously adapts while maintaining a distinctly human tone, something that would have been prohibitively complex to build without AI.

Scaling ad creation and testing

Scaling creative output is becoming an essential part of a strong paid advertising strategy. For success, brands need an increasing number of ad variations—often more than a creative team can realistically produce on its own.

Ridge is using AI to close that gap. It created a custom GPT trained on its best-performing ads, then connected it to automation tools that generate hundreds of new static assets each day. “We’ve built a static ad factory,” says Sean. “I can get 500 ads a day—no hands on keyboards.” 

These assets flow into a shared drive for review, but most won’t make it into rotation. And that’s OK; the point is volume. “Out of those 500, 450 are horrible,” Sean notes. “But the top 10% are between five out of 10 and seven out of 10. They’ll get spend behind them.” The brand plans to extend this process to video next, generating more hooks and variations for testing.

AI is not replacing Ridge’s creative team. The company’s highest-performing ads still come from the human design team, which consistently produces 10-out-of-10 winners. AI just enables more concepts, more iterations, and more opportunities for platforms like Facebook to match the right ad to the right person at the right time.

“The future of advertising is just shots on goal,” explains Sean. “What you see and like is going to be totally different from what I see and like.” By pairing human-crafted assets with high-volume AI-generated variations, Ridge can scale experimentation far beyond what manual efforts would allow—turning its paid strategy into a continuous, data-driven loop of testing and refinement. 

Improving customer service

Early AI tools like chatbots and interactive voice response (IVR) were a natural fit for repetitive use cases (like “Where’s my order?” and “What are your hours?”). This makes customer service one of the most mature applications of AI in small businesses. However, the stakes of balancing human and machine intervention remain high. A 2024 study from Acquire Intelligence found that just one bad AI-assisted support experience would make 70% of consumers consider taking their business elsewhere.

At Loftie, AI agents now answer over half of incoming support emails. “It’s difficult to standardize responses across human agents—AI can be much more reliable,” Matthew says. “It’s answered the same question 1,000 times before.” The team also uses AI to surface trends from what Matthew calls a “graveyard of data,” turning thousands of customer interactions into insights that inform product and experience improvements. “I’m honestly surprised when brands are reticent to adopt AI for customer service,” he notes.

Ridge has seen similar benefits. “Customer service is a super easy use case,” Sean explains. “Around 60% of our tickets are being answered by AI.” The company has also seen a 10% to 20% lift in customer satisfaction scores over human-only workflows. “Customers love talking to the AI,” he adds. “It’s faster, quicker, more accurate.”

These improvements are driven by a shift from rule-based chatbots to agentic AI—tools that can understand intent, reference past interactions, access customer data, and take simple actions like processing refunds or replacing items. Once limited to large enterprises, agentic AI is accessible today via tools like Zendesk and HubSpot.

When implemented thoughtfully—and understanding when to escalate emotional issues and complex problems to a human agent—AI expands what teams can handle without sacrificing quality. Routine questions are resolved quickly and consistently, and human agents can spend more time on the conversations that matter most.

As AI continues to evolve, the opportunity for small businesses will come from this kind of selective adoption: using AI where it elevates human capabilities and keeping people at the center of the work that defines the brand.