Real-Time Data Is the New Default: Why Batch Processing Holds AI Back

Picture this: You own a ticketing platform for major live events. A few years back, you had a fixed price per ticket according to each seating sector. Now, you adjust your prices based on demand.

Back then, you only needed real-time data processing to show current seating availability—everything else was batched for reporting. You would then use the tracked data to decide if you needed to launch some promos, lower the price, or offer bundles to get tickets to sell faster.

Now, using a batch processing pipeline would be leaving money on the table. You want to get real-time information and adjust your prices based on the number of people who are online at the same time. For that to happen, you need to train an AI algorithm to feed off real-time data and change prices dynamically.

This is just one example of how batch processing is the way to kill your AI initiatives, but there are many other reasons why you need real-time data for AI models. Let's explore this in more detail.

Table of contents

• Why you need fresh data for successful AI models
• AI’s kryptonite: Stale data
• Batch vs real-time data for AI
• Moving into a real-time AI architecture
• Industry examples of using real-time data for AI
• From batch processing to real-time response: How to build an AI-ready data architecture

Why you need fresh data for successful AI models

AI models are only as useful as the data they’re trained on. So, whether you're building AI agents, tools, or workflows, having minimal delay in ingestion and data processing could improve the model’s output. This becomes particularly important in scenarios where delays may lead to missed opportunities and reputational or security risks.

Building real-time data pipelines and using them to train AI models is also relevant because gaining access to live information allows you to have:

• Better inference accuracy. Fresh data means your models make decisions based on current conditions, not outdated views. For example, an AI agent can use historical data to identify triggers and anticipate when a user is about to abandon the cart.
• Shorter AI adaptation with smarter feedback loops. By continuously learning from live data, models can adjust to new patterns and behaviors immediately and increase model performance over time. For example, if you’re training a generative AI agent for customer assistance, it can pick up your preferences and autonomously adapt as it goes.
• More accurate reactions to user behavior. Real-time insights enable immediate personalization and optimization. For instance, if your AI agent infers that the customer is struggling to complete a task, it can prompt them with a tutorial video.
• Better output quality. Current data produces more relevant and contextually appropriate responses. For example, if a user is asking your AI agent about the status of their package, you need it to have access to current information. If not, it may share incorrect or outdated information.
• Faster and more accurate decision making. Getting access to live data allows you to make the right decisions and stay ahead of the competition. For example, if you can quickly re-engage a customer who abandoned the cart, you’re more likely to get them to complete the purchase than if you get this info 24 hours later.

Using real-time data to train your AI models is no longer a nice-to-have as all of these reasons have a direct impact on your ROI, customer satisfaction, and customer engagement.

AI’s kryptonite: Stale data

Outdated data is silently killing AI and ML initiatives. In fact, 85% of AI projects fail before they see the light of day due to poor data architectures.

This limitation becomes critical when you need your AI systems to respond to ever-changing conditions, like in the ticketing system example. Even if you set up batch jobs to execute multiple times per minute, this may cause your AI model to fail. On paper, this looks like a feasible solution to get data with minimal delay. However, this amount of async operations, on top of other batch requests you may have, could overload the system and create a queue of pending tasks, causing delays in the short term.

Let’s consider other scenarios where stale data could affect business value:

• Financial services. In this case, relying on batch data could cause you to lose a large amount of money. This could happen if trading apps showed an outdated price for even a second, an AI agent didn’t detect fraud signals on time, or if an account balance didn’t update in real-time.
• Cybersecurity. Threat detection systems relying on batch-processed data are essentially useless. By the time a security breach is detected and analyzed through traditional batch processing, attackers have already moved through multiple stages of their attack.
• E-commerce. Recommendation engines using yesterday's customer data miss the immediate intent signals that drive purchasing decisions. A customer searching for winter coats today shouldn't see recommendations based on their handbag searches from last month.
• EV industry. Automation for EV charging networks allows CPOs (charge point operators) to be immediately notified of issues so they can fix them remotely. If not, they lose customers’ loyalty and impact the station's reliability.

Apart from AI models making mistakes or relying on old data, batch processing also hints at a bigger factor: decision latency.

The hidden cost of decision latency

Decision latency is the time between identifying the need to make a decision and actually gaining access to the information required to support it. This has a direct impact on business operations and competitive advantage.

If you’re leveraging AI models and agents to make certain decisions—e.g., adjusting prices based on demand—you need to give them access to real-time data to reduce any decision latency. Also, on top of this, when an AI agent doesn’t have access to timely data, it tends to make stuff up and have hallucinations.

Additionally, companies experiencing high decision latency face:

• Revenue loss. Some examples include:
  • Missing opportunities to re-engage a customer and get them to complete the order
  • Failing to adjust prices based on demand
  • Identifying pricing mistakes too late
• Customer satisfaction decline. For instance:
  • Failing to respond to customers accurately or fast enough. Remember, 90% of customers expect an immediate response when they reach customer service.
  • Showing customers outdated recommendations
• Competitive disadvantage. For example:
  • Missing the opportunity to capture market opportunities first
  • Having lengthy processes to make decisions
• Operational inefficiency. Such as:
  • Allocating resources based on outdated information, like scheduling the team for peak working hours

Real-time data processing reduces this delay and allows people and AI agents to make faster and more accurate business decisions. This also helps give customers what they want: “Anything outside of using real-time data can frustrate end consumers and feel unnatural,” says Mindy Ferguson, VP of Messaging and Streaming at AWS. “Having real-time data always available is becoming an expectation for customers. It’s the world we’re living in.”

Batch vs real-time data for AI

The biggest difference between batch and real-time processing isn’t latency. Instead, it’s how you handle and make data available to decision makers.

Here’s a brief overview of the differences between batch and real-time data for AI models:

Batch processing

When you adopt a batch processing data architecture, your data runs on a set schedule. This makes data access predictable and spares costs. It’s the perfect approach for:

• Historical reporting and analytics, e.g., monthly sales, new quarterly subscribers, annual sales by product, or weekly reports
• Compliance and regulatory reporting, e.g., weekly phishing alerts, results of phishing drills, or monthly audits
• Large-scale data transformations, e.g., data cleaning and standardization, ETL pipelines in data warehousing, and transforming raw logs into actionable insights
• Model training on historical datasets, e.g., forecasting sales, predicting customers’ seasonal behavior, or classifying emails as spam or not spam

Real-time processing

When you process data in real-time, you enable a continuous flow of information to be streamed into your system. This allows you and your AI agent to access real-time or near-real-time information for processing, querying, and analytics. This enables:

• Immediate responses, your team or AI model can take action within milliseconds of data arrival
• Continuous learning, AI models update with every new data point, and get trained faster
• Event-driven decisions, your team members or AI agents can react to specific conditions immediately as they occur
• Adaptive behavior, your AI models can adjust to changing patterns in real-time

While real-time data processing has low latency, ensuring minimal delay between data input and output, it tends to be more expensive than batching architectures.

Moving into a real-time AI architecture

The world is moving at an even faster pace, making it almost imperative for companies to move from batched pipelines into a real-time data streaming architecture to power AI initiatives. To do so, you need to adopt these key components:

• Event-driven architecture. Process data as discrete events rather than batches. Meaning, each user action, sensor reading, or system event triggers immediate processing. This approach ensures your AI systems react to events as they happen rather than waiting for scheduled processing windows.
• Stream processing frameworks. Modern stream processing frameworks like Apache Kafka and Amazon Kinesis enable real-time data ingestion and processing. These tools handle:
  • Data ingestion: Capturing data from multiple sources simultaneously
  • Stream processing: Real-time transformations and analysis
  • Event routing: Directing data to appropriate processing systems
  • Backpressure handling: Managing data flow during high-volume periods
• Edge computing. For ultra-low latency requirements, edge computing brings processing closer to data sources. This is particularly important for:
  • IoT devices requiring immediate responses
  • Autonomous systems needing split-second decisions
  • Mobile applications with strict latency requirements
• Real-time data ingestion pipelines. Effective real-time pipelines require:
  • Multiple data sources: APIs, databases, message queues, and streaming services
  • Data validation: Real-time quality checks and error handling
  • Schema evolution: Handling data structure changes without downtime
  • Monitoring and alerting: Immediate notification of pipeline issues

Industry examples of using real-time data for AI

The industry has used real-time data processing to power AI models for way before generative LLMs became popular. Here are examples you’ve probably experienced in your daily life:

Uber’s dynamic pricing

Uber’s dynamic pricing

Uber, the ride-sharing app, has a base rate per mile, but it uses a pricing algorithm to adjust prices based on demand, time of day, and distance. This algorithm processes live data from multiple sources: current ride demand, driver availability, traffic conditions, and local events.

The system adjusts prices dynamically, sometimes within seconds, to balance supply and demand. This real-time approach has been crucial to Uber's ability to maintain service availability during peak demand periods.

Netflix content recommendations

Netflix content recommendations

Netflix processed over 500 billion events a day back in 2016. It’s fair to assume this number has increased exponentially since then.

Netflix uses this gathered information to power its recommendation engine. The system tracks user behavior in real-time, including what they watch, when they pause, and what they skip. Then, it immediately adjusts recommendations.

Also, Netflix users have reported online that the company is already testing different versions of shows to different users based on their preferences. This real-time personalization has been a key factor in Netflix's industry-leading engagement rates.

Tesla’s Supercharger network real-time optimization

Tesla’s Supercharger network real-time optimization

Tesla's Supercharger network allows drivers to see real-time occupancy of their nearby charging stations directly on vehicle maps. This way, drivers can know exactly how many available charging ports are at each location.

The system processes data from thousands of charging stations continuously, analyzing current occupancy, charging completion times, and local demand patterns. When a driver searches for charging stations, the AI system doesn't just show the nearest locations; it predicts which stations will have available stalls by the time the driver arrives. To do so, it factors in current traffic conditions and estimated charging completion times of vehicles currently plugged in.

This real-time optimization has helped Tesla maintain 99.95% uptime across their Supercharger network.

From batch processing to real-time response: How to build an AI-ready data architecture

Moving into an AI-ready real-time data architecture isn’t an easy task. To do so, you need expert data engineers who can design a new architecture without hurting your platform’s uptime.

You can choose to do it in-house if your team is well-versed in streaming technologies, real-time databases, and low-latency system design. They also need to have the time to manage their daily work on top of this migration. Additionally, you need to make an investment as real-time systems require robust infrastructure with redundancy, monitoring, and scaling capabilities.

Another option, preferred by many, is partnering with cloud native data engineering experts like NaNLABS. This approach ensures faster implementation and less weight on your in-house team. Also, NaNLABS understands real-time AI architectures and can accelerate implementation thanks to its previous experience and usage of existing frameworks.

This approach also mitigates risk as its team has done this kind of migration multiple times before and can help avoid costly mistakes. Lastly, these experts design systems with scalability in mind to meet current and future business needs.

At NaNLABS, we become your technical sidekicks and work alongside your team to develop risk-free, high-performing solutions. Want to transform your data architecture and get ready for the AI revolution?