5 AI Business Solutions You Can Build with AWS

AI is often discussed in broad, futuristic terms — “AI is revolutionizing everything” or “Every company needs an AI strategy” — but how does it work in practice? Many businesses assume that cutting-edge AI is either too complex, too costly, or only accessible to tech giants. In reality, AI is already driving real business impact across industries in ways that many don’t expect.

At PCG, we’ve worked with companies of all sizes to implement AI-powered solutions using AWS—without the need for massive budgets or in-house data science teams. From optimizing logistics and energy management to transforming customer experiences, AI isn’t just the future of business—it’s already here, solving real problems today.

Here are five real-world AI solutions we’ve built with AWS that challenge common assumptions about what’s possible.

1. Serverless AI for Logistics Optimisation

The logistics industry is an ideal proving ground for AI. With countless moving parts—shipments, routes, warehouse operations—small inefficiencies can quickly add up. The first example on our list shows how AI and serverless technology can work together to automate logistics without requiring massive infrastructure investments.

• What it does: Streamlines logistics and supply chain workflows with real-time data processing and predictive analytics.
• How it does it: The solution uses AWS Lambda for event-driven automation, Amazon SageMaker for predictive modelling, Amazon S3 for scalable data storage, and AWS Step Functions to orchestrate workflows.
• Why it’s cool: Logistics optimisation is often seen as a domain requiring large on-premise resources, yet serverless AI on AWS can reduce operational overhead and improve shipment routing without hefty infrastructure costs.

2. ML-Powered Price Comparison and Consumer Insights

Once AI is handling complex logistics, it’s easy to see how it can also optimize decision-making in real-time. The next example highlights how AI-driven machine learning models can power price comparisons, detect fraudulent activity, and personalize recommendations—tasks that once required entire teams of analysts.

• What it does: Analyses customer data to provide optimized pricing, personalized recommendations, or fraud detection in real time.
• How it does it: The solution leverages Amazon SageMaker for ML model training, AWS Glue for data integration, and Amazon Kinesis to process and analyze streaming data in real time.
• Why it’s cool: Machine learning is often perceived as complex; AWS offers managed services that make advanced ML accessible to mid-tier or niche businesses.

3. AI-Enhanced Hydropower Management

AI isn’t just for digital industries—our next example proves its potential in renewable energy. Hydropower companies rely on precise monitoring to maximize efficiency, and AI-powered predictive analytics can make a measurable difference in energy output and sustainability efforts.

• What it does: Applies predictive analytics to improve energy production efficiency, sustainability, and real-time monitoring of hydropower stations.
• How it does it: The system collects sensor data using AWS IoT Core, processes it with Amazon SageMaker to predict energy output and maintenance needs, and stores large-scale operational data in Amazon S3.
• Why it’s cool: Renewable energy projects don’t typically come to mind when thinking of cloud-based AI, but harnessing AWS provides the strength required for faster innovation and cost savings in traditional sectors.

4. Predictive Maintenance for Rail Infrastructure

Transportation infrastructure is another sector where AI is making a surprising impact. Railways have operated on schedules and inspections for decades, but AI is introducing predictive maintenance—enabling real-time monitoring of track conditions, detecting issues before they cause delays, and improving passenger safety.

• What it does: Monitors railway systems in real time, using anomaly detection and predictive analytics to reduce downtime and enhance passenger safety.
• How it does it: The solution deploys Amazon Monitron for real-time condition monitoring, Amazon Lookout for Equipment to detect anomalies, and AWS IoT Greengrass for edge computing on railway devices.
• Why it’s cool: Railways are often considered a traditional industry where you probably wouldn’t expect AI to be making a difference; AWS-based AI solutions transform maintenance and planning to be proactive rather than reactive.

5. Cloud Migration for AI-Ready POS Systems

While AI is transforming logistics, pricing, energy, and transportation, its potential doesn’t stop there. Even industries like hospitality and retail are rethinking their digital foundations to prepare for AI-driven growth. Our final example shows how migrating a point-of-sale (POS) platform to AWS opens the door for real-time sales forecasting, customer behaviour analysis, and future AI-driven features.

• What it does: Moves a point-of-sale platform to AWS, paving the way for real-time sales forecasting, customer behaviour analysis, and future AI-driven features.
• How it does it: The migration uses Amazon EC2 or AWS Elastic Beanstalk for scalable hosting, Amazon RDS or Amazon DynamoDB for handling transactional data, and Amazon Forecast to predict future sales trends.
• Why it’s cool: Migrations are typically about cost-saving and uptime, but leveraging AWS also sets the foundation for rapid AI experimentation and deployment — helping to make this an exciting time for Orderbird.

AI in Action: Real-World Impact, Not Just Hype

As we’ve seen in these examples, AI is no longer just a futuristic concept—it’s already transforming industries in practical, measurable ways. From optimizing logistics and energy production to improving infrastructure and retail operations, businesses of all sizes can leverage AI on AWS without massive budgets or in-house expertise. These real-world solutions prove that AI isn’t just for tech giants—it’s an accessible and valuable tool for companies ready to innovate.

If you’re wondering how AI could fit into your business, the possibilities are likely broader than you think. Whether you want to streamline operations, improve decision-making, or unlock new revenue streams, AI on AWS can provide a competitive edge. The question isn’t if AI can help—it’s where you should start.