Empowering Small Businesses: Training AI Models for Smarter Decisions

Richard Williams, Head of Digital Transformation at Kinore, has seen how artificial intelligence (AI) is levelling the playing field for small businesses. A recent survey by the U.S. Chamber of Commerce found that 98% of small businesses are already using AI-enabled tools, with 40% leveraging generative AI for tasks like content creation (U.S. Chamber of Commerce). While tools like chatbots and marketing platforms are valuable, small businesses can unlock even greater potential by training custom AI models to make data-driven decisions. This approach, once reserved for large corporations with dedicated data science teams, is now accessible to companies with immature IT departments thanks to no-code and low-code AI platforms.

Off-the-shelf AI tools, like chatbots or content generators, are excellent for general tasks, but they may not address your unique business challenges. Training a custom AI model allows you to tailor solutions to your specific data and goals, enabling more precise decision-making. For example:

• Predicting Customer Churn: Identify customers likely to stop purchasing, allowing you to offer targeted promotions to retain them.
• Sales Forecasting: Predict future demand to optimise inventory, reducing costs from overstocking or stockouts.
• Customer Support Automation: Classify incoming inquiries to route them to the right team, speeding up response times.
• Fraud Detection: Flag suspicious transactions in real-time to prevent financial losses.

These applications can lead to significant benefits. According to Forrester, companies using predictive analytics are 2.9 times more likely to achieve revenue growth of 15% or more (Forrester). Additionally, IDC and Microsoft report that for every $1 invested in generative AI, businesses see an average return of $3.70 (Microsoft Blog). By training custom models, small businesses can capture these benefits, even with limited technical resources.

Small businesses with immature IT departments often face hurdles when adopting advanced AI:

• Limited Expertise: Lack of data scientists or IT staff to build and manage models.
• Resource Constraints: Tight budgets and small teams limit investment in complex solutions.
• Data Quality Issues: Inconsistent or incomplete data can undermine AI effectiveness.
• Integration Needs: Connecting AI models to existing systems like CRMs or websites can be challenging.

Fortunately, no-code and low-code AI platforms, such as Obviously AI, Levity, and Google Cloud AutoML, simplify the process, making it accessible to non-technical users. These platforms handle much of the complexity, allowing businesses to focus on defining problems and preparing data.

AI models can address a wide range of business challenges, enhancing efficiency and decision-making:

• Sales Forecasting: Predict future sales based on historical data, market trends, and seasonality. This helps optimise inventory, staffing, and marketing budgets, reducing costs by up to 20% (McKinsey).
• Customer Segmentation: Group customers by behaviour or demographics for targeted marketing, increasing campaign effectiveness by 20-50% (HubSpot).
• Fraud Detection: Identify suspicious transactions in real-time, reducing financial losses. AI-driven fraud detection can lower fraud rates by 30% (IBM).
• Sentiment Analysis: Analyse customer feedback from reviews or social media to gauge satisfaction, enabling proactive improvements and boosting retention.

These applications demonstrate the versatility of AI in solving real-world business problems, even for small teams.

To ensure success when training AI models, keep these best practices in mind:

• Data Quality: Accurate, complete, and representative data is critical. Poor data leads to unreliable predictions, so clean your data by removing duplicates and standardising formats.
• Model Interpretability: Understand how the model makes decisions, especially for critical applications like financial forecasting. Platforms like Obviously AI provide explanations of key factors influencing predictions.
• Ethical Use: Be aware of potential biases in your data or model outputs. For example, ensure churn predictions don’t unfairly target specific customer groups. Regular audits can mitigate this risk.
• Start Small: Begin with a pilot project to test the platform and process before scaling to more complex applications.
• Monitor Continuously: AI models can degrade over time as data patterns change. Retrain models regularly to maintain accuracy.

Additionally, consider data privacy regulations like GDPR when handling customer data, and maintain human oversight for critical decisions to ensure fairness and accountability.

The future of AI is bright for small businesses, with no-code platforms and embedded AI features in tools like Google Workspace and Microsoft 365 making adoption easier. As AI evolves, we can expect more advanced applications, such as real-time predictive analytics or hyper-personalised customer experiences. Early adopters will gain a competitive edge, capturing a share of the $13 trillion AI is projected to add to the global economy by 2030 (McKinsey).