Businesses with fewer than 500 employees account for 43.5% of GDP and 48% of American jobs1, yet small to midsize businesses have faced disproportionate hardship from the economic fallout accompanying the COVID-19 pandemic. According to analyses from McKinsey & Company, 5.7 million small businesses are reporting large negative effects from the crisis, and without intervention, 25% to 36% risk permanent closure2. Small businesses have suffered losses at a time when savings were already limited; only 35% of small businesses were financially healthy when COVID struck3.

Large enterprises are getting back to growth with AI and data analytics. SMBs, on the other hand, have historically faced a disadvantage in the digital transformation space because of their inability to invest in expensive, resource-intensive technology and a shortage of usable data. Now, however, businesses of all sizes generate enough data to harness into meaningful insight thanks to advanced POS systems, reliance on website and app usage, and readily available external data sources. Additionally, companies can operationalize insights from customer service records, order information, customer reviews, and other in-house data in ways that were previously impossible without highly involved technical solutions. As a result, AI and machine learning have even more potential to help small businesses at a time where competition with large enterprise is essential.

Small to midsize businesses can reap valuable benefits from machine learning by setting targeted goals for implementation and leveraging flexible, no-code tools. SMBs are more agile than larger companies; they can act more quickly on insights gained through analytics in a few key areas. Here are some examples—

Customer Segmentation

In the wake of the pandemic, customer spending habits and preferences have shifted drastically4. Businesses have had to adapt to new order and delivery methods, all while demand has skyrocketed for some products and plummeted for others. Customer loyalty, which is crucial for SMBs, is no longer a guarantee.

These changes underscore the importance of understanding and predicting customer behavior for any business. They also bring an opportunity to target customers more directly by tailoring advertising messages and mediums based on customer behavior and demographics. Understanding which products appeal to certain market segments and when these groups are inclined to purchase allows SMBs to get more mileage out of a limited marketing budget while increasing revenue.

Customer Retention

SMBs can also use AI to drive customer retention and predict churn before it occurs. Data on customer demographics, spending, and feedback can all offer promising insight on which customers are at most risk of leaving a provider, allowing the company to target its outreach efforts more directly.

Supply Visibility

End-to-end visibility into sourcing, inventory, and delivery is more important than ever as SMBs respond to historic supply chain disruption. Businesses can build responsive supply chains with predictive analytics based on inventory and customer data. Forecasting demand and predicting supplier delay can help mitigate problems before they occur while also preparing companies to face unanticipated challenges as effectively as possible. Businesses can compensate for issues by locating alternate suppliers, optimizing inventory levels based on shifting demand, or pivoting marketing strategy. While some POS systems offer basic business intelligence capabilities for inventory management, large enterprise shows us that predictive analytics powered by AI have the most power to drive intelligent business decisions.


Reducing delivery times is another essential focus area for SMBs looking to compete with large companies. Businesses can use location-based data from sensors and GPS tracking devices as well as customer feedback to generate insight around opportunities to increase delivery efficiency. A combination of data sources can even help companies predict delivery dates.

How C3 AI Ex Machina Can Help

As more companies try to incorporate data-driven problem solving into their business processes, many come to the realization that their data is siloed across several different data stores—SMBs are no exception. Integrating and preparing data for analysis has historically been the most time-consuming component of data analytics; however, with C3 AI Ex Machina, data can be seamlessly integrated from many sources and rapidly made ready for analysis.

Changes in the wake of the COVID-19 pandemic present not only challenges, but opportunities for small to midsize businesses to implement new and exciting innovation strategies to stay viable. Ex Machina’s data preparation, analytics, and ML capabilities make it even easier to derive meaningful insights in real time and operationalize them at scale, all with a highly intuitive and approachable interface. With Ex Machina’s template library, businesses can employ workflows that reflect best practices for ML algorithms, incorporate a variety of industry use cases, and accelerate insight without dedicated data science personnel. With tools accessible to any business leader or analyst, Ex Machina brings AI and ML to any company looking to harness the power of data analytics.