Artificial intelligence is changing how organizations build software, work with data, automate processes, and support employees and customers. On AWS, successful AI initiatives depend on more than choosing a model. The surrounding data, architecture, security, and implementation approach all shape what the technology can deliver.
The articles below cover AI on AWS, including Amazon Bedrock, generative AI, data ingestion, AI services, automation, and implementation considerations. You’ll find guidance on evaluating use cases, preparing data and infrastructure, choosing the right AWS capabilities, and moving AI initiatives from experimentation into real-world use.
Stratus10 helps organizations evaluate, design, and implement AI solutions on AWS, combining technical implementation with hands-on consulting to help teams turn promising ideas into working capabilities.
Most companies have more AI pilots than AI systems in production. According to Deloitte’s State of AI in the Enterprise 2026, only 25% of organizations have moved 40% or more of their AI pilots into production. Respondents reported that while their AI strategy is prepared, they felt less confident on infrastructure, data, risk, and talent.
Many Amazon Bedrock implementations start with what looks like a straightforward goal: connect structured and unstructured data sources, make them accessible to a foundation model, and enable natural language interaction. What becomes clear very quickly is that the quality, reliability, and cost of the solution depend heavily on decisions made at the data layer (not in the model or prompt design).
Cloud operations teams that automate their single most repetitive, highest-friction manual task before chasing more complex systems see the fastest and most durable gains, based on strategies shared by 13 technology leaders for this piece.
A data lake is a centralized repository that allows businesses to store structured and unstructured data at any scale. It provides a single source of data that can be analyzed and used to support a variety of business needs, such as reporting, analytics, machine learning, and more.
AWS offers the broadest and deepest set of machine learning services and supporting cloud infrastructure, putting machine learning in the hands of every developer, data scientist and expert practitioner. To remove the complexity of getting started with machine learning, AWS developed the Sagemaker platform to help customers get started using AI to their advantage, with no experience.
The business potential of artificial intelligence has been on the horizon for decades. But many businesses have found AI hard to implement, or to even start imagining. Thanks to some relatively recent breakthroughs, that potential is being realized now.
If you’ve been following technology news in recent months you’ve probably heard all the buzz about artificial intelligence and terms such as “machine learning” and “neural networks”. The truth is artificial intelligence has been around for a while. It is just being covered a little more in the news lately. One of the factors that have allowed artificial intelligence to be more relevant has been the public cloud. The advent of the public cloud has had an enormous impact on the advancement of artificial intelligence in recent years.
