Artificial intelligence (AI) has captured the attention of every business leader in the last decade, promising to transform industries. And now that ChatGPT has become a household name, we’re all collectively intrigued about how AI can and will affect our lives and jobs. Commonly suggested applications include automating complex tasks, optimizing business processes, and even replicating human decision-making. Global management and consultancy firm McKinsey estimates AI’s effect on productivity and revenue opportunities by 2030 could add between $13.6 and 22 trillion in economic value to today’s global gross domestic product.
Although the opportunity for innovation with AI is undeniable, many businesses still don’t understand the nuts and bolts of the technology under the hood. So, for today’s AI lesson: the potential of AI is dependent on one critical factor — a continuous stream of real-time data. In fact, no large language model (LLM) will be smart without real-time, business-specific, highly contextual knowledge provided by continuous data streams of real-time data.
What Is Real-Time Data?
Real-time data refers to information that is available for use as soon as it is generated, instantaneously. Ideally, data is passed instantly between the source and the application.
AI algorithms, especially machine learning models, require a constant stream of fresh data to learn, adapt, and stay relevant in our demanding, real-time world. In most instances, cut off an AI system from live data inputs, and its knowledge will rapidly become outdated, rendering it fairly useless.
How Does Real-Time Data Play Out in Today’s World?
Although we can easily communicate and visualize the value of real-time data, setting up the infrastructure necessary to support it is incredibly complex. This is especially true for a global, geographically dispersed organization. Bottlenecks in data infrastructure, bandwidth issues and data security all present obstacles to prevent organizations from making use of real-time data. Unfortunately, when your data stack isn’t set up correctly, has bandwidth issues, is unable to scale with your organization, or just outright fails, outdated intelligence can lead to catastrophic consequences.
Let’s examine autonomous vehicles as an example, which is one of the most high-stakes applications in which AI and real-time data must work in harmony. Self-driving cars rely on a multitude of sensors and cameras to continuously ingest data about their surroundings, from road conditions and traffic patterns to the movements of pedestrians and other vehicles. If real-time data flow were to be disrupted, even momentarily, the autonomous vehicle’s understanding of its environment would immediately become obsolete. In a matter of seconds, it could miss a pedestrian crossing the road, a sudden obstruction, or a traffic light change, with potentially disastrous results.
Life Without Real-Time Data
The risks of AI operating on stale data aren’t limited to physical scenarios like autonomous vehicles. In business contexts, when AI makes decisions that aren’t grounded in the latest market intelligence, customer data, and operational metrics, those decisions are likely to be misguided and potentially outright wrong.
The world of AI thrives on information. Unlike the pre-AI era, where inquiries were limited to predefined questions or required a phone call to a human agent, today’s infrastructure must be ready to answer virtually any question based on available data. Effective use of LLMs in domains like customer service demands that the models are adapted to handle industry-specific queries and have access to real-time data.
For example, an AI assistant designed to manage airline delays must be updated with current specifics, not just general data about an airline. Many airlines use public data sets to train LLMs. Unfortunately, historical data cannot answer questions like, “Is my flight delayed?” In the age of generative AI, people expect to ask complex questions such as, “Can I upgrade to first class for my upcoming trip?” Answering this requires access to reservation data, customer mileage records, booking details, and more. It necessitates both public and domain-specific, real-time data.
This requires a system where data flows in real-time to the LLM at the moment of request, enabling truly intelligent, automated responses. Integrating real-time data unlocks AI’s full potential in domain-specific applications. But what if you have all the necessary data but lack a connector to provide real-time information about seat availability?
Under the Hood of Real-Time Data
Although we’ve painted clear opportunities and use cases, it’s important to understand that AI’s potential and success are directly connected to completeness and accuracy of the data it is trained on. This is why organizations must prioritize building robust, low-latency data streaming platforms to ensure their AI systems have access to continuous, real-time streams of relevant data. From edge computing for ingesting IoT sensor data to massive-scale stream processing platforms for handling high-volume events, the infrastructure and technologies for enabling real-time data streams for AI have become critical capabilities.
Building Real-Time Data Streaming Infrastructures
The biggest problem with obtaining real-time data in many organizations is that they choose their real-time infrastructure based on the immediate use case. For instance, it might be crucial to get machine sensor data, so developers quickly implement RabbitMQ to create a messaging system. At another point, streaming customer data into a data warehouse takes precedence, leading developers to build custom connectors and create a streaming data pipeline. As a result, you end up with a chaotic set of connectors and streaming protocols.
The real problem arises when you need to scale these systems. Each protocol and technology has a different way of scaling. When your data volume suddenly increases, you cannot avoid downtime or system failures. That’s why it is crucial to take a platform approach to data streaming.
Consider the following questions before deploying a data streaming infrastructure that will become the backbone of your AI systems:
- Does the platform allow multi-tenancy so that various departments can request data in and out?
- Does the platform support multiple protocols?
- Can the platform scale up and down without manual intervention from the SRE team?
These are essential questions to ensure your data streaming infrastructure is robust, scalable, and efficient.
AI Is Only Intelligent With Data
As AI systems become more deeply embedded across industries — from healthcare and finance to entertainment and education — their reliance on real-time data will only intensify. In a world where AI is ubiquitous and touches every aspect of our lives, having models operate on outdated information is simply untenable.
To meet these demands, organizations must adopt a robust, scalable, and efficient data streaming infrastructure. By taking a platform approach that supports multi-tenancy, multiple protocols, and automatic scaling, businesses can ensure their AI systems have the real-time data they need to perform optimally and reliably. This strategic investment in a well-architected data streaming solution will be essential for staying competitive and innovative in the AI-driven future.
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