What Machine Learning Can Do for Your Business in 2024

Machine learning (ML) is increasingly crucial in driving business innovation and efficiency. Large enterprises like Amazon, Google, and IBM are known for pioneering ML use to enhance services, streamline operations, and innovate products. However, ML isn’t just for corporate giants.

Whether you’re managing a startup, a small business, or a large enterprise, machine learning can provide invaluable insights, helping you predict customer behaviors, optimize workflows, and reduce operational costs. A recent Deloitte survey found that over 50% of small businesses already leverage some form of machine learning, with another 30% planning to adopt it soon.

The growing availability of big data, combined with cheaper processing power and advancements in cloud technologies, has made ML accessible to businesses of all sizes. By automating decision-making processes and analyzing vast amounts of data, companies are now able to solve complex problems faster than ever before.

However, implementing ML effectively requires the right development partner. You can read our blog to follow the criteria for choosing the right AI/ML development partner.

In this blog, we’ll walk through key applications of machine learning, its benefits, and a case study demonstrating its real-world impact.

What are the key applications of machine learning

Machine learning’s versatility makes it applicable across a wide range of industries and functions. Here are some prominent use cases:

• Customer lifetime value prediction: Machine learning can analyze customer behavior, purchasing patterns, and interactions to predict each customer’s long-term value. This enables businesses to optimize marketing efforts and improve customer retention.
• Predictive maintenance: Manufacturing and industrial companies use ML to predict when equipment is likely to fail, allowing for preventive maintenance and reducing downtime. This enhances operational efficiency and lowers costs associated with unexpected failures.
• Personalized product recommendations: E-commerce platforms like Amazon use ML to recommend products to users based on their past purchases and browsing history. These recommendations help increase sales and improve customer satisfaction.
• Financial forecasting: ML models can analyze historical financial data to predict future trends. This is invaluable in industries like banking, where portfolio management, loan approvals, and fraud detection rely heavily on accurate predictions.
• Cybersecurity and fraud detection: ML algorithms identify patterns in data that might indicate fraudulent activity or security breaches. These systems continuously learn and adapt, improving their ability to detect threats as they evolve.
• Human engagement via natural language processing (NLP): Tools like chatbots and virtual assistants (e.g., Alexa, Google Assistant) use ML to understand and respond to human language. Businesses can use these to automate customer service or improve user experience.
• Anomaly detection: ML’s pattern recognition abilities help detect anomalies in data, which is crucial for fraud detection, quality control, and process optimization. Companies can address issues proactively before they escalate into significant problems.

Benefits of using machine learning

Here are some key benefits businesses gain by incorporating machine learning:

• Improved decision-making: ML provides actionable insights by analyzing vast amounts of data in real-time, enabling businesses to make informed decisions faster.
• Enhanced customer experience: ML helps create a more engaging and relevant customer experience by personalizing content and services based on customer behavior. This can increase customer loyalty and retention.
• Operational efficiency: ML automates repetitive tasks like data entry, document classification, and inventory management. This reduces human error, speeds up processes, and allows employees to focus on high-value activities. Explore our AI solutions for every challenge to see how we can help optimize your operations.
• Reduced costs and increased profitability: ML-driven automation reduces operational costs by streamlining workflows, eliminating inefficiencies, and improving resource management. Predictive analytics also help businesses avoid costly mistakes and optimize their inventory and supply chain management.
• Future-proofing your business: As more industries adopt AI and ML, staying competitive requires businesses to keep up with technological trends. Integrating ML into your operations, you future-proof your business against industry disruption.

Case study: AI/ML-driven predictive maintenance for a leading industrial company

In this project, People10 helped a global leader in the mining and cement industry tackle serious challenges: frequent equipment failures, high maintenance costs, and operational inefficiencies. These ongoing issues were leading to significant production delays and financial losses.

Challenges faced

• Unplanned downtime: Equipment failures disrupted production and the supply chain, delaying deliveries.
• High maintenance costs: Their reactive approach to maintenance was costly and inefficient, with increased repair expenses.
• Operational inefficiencies: Manual interventions were slow, prone to human error, and causing further delays.

Solution provided

People10 implemented a comprehensive ML-driven predictive maintenance solution using Azure Machine Learning, Databricks, and Azure IoT Hub. Sensors were installed across the client’s equipment to collect real-time data, such as temperature, pressure, and operational status. This data was then analyzed by machine learning models to predict when equipment was likely to fail, allowing for proactive maintenance. Automated actions, triggered by programmable logic controllers (PLCs), optimized operational efficiency by responding instantly to predictive insights.

Business impact: AI-driven predictive maintenance

By leveraging machine learning, our client experienced transformative improvements in operational efficiency, cost reduction, and downtime prevention. To learn more about the detailed business impact of this project, including how we achieved a 25% reduction in maintenance costs, a 30% reduction in downtime, and a 20% boost in operational efficiency, you can explore the full case study.

Read the full case study to see how People10’s tailored ML solution delivered measurable results for a global leader in the mining and cement industry.