AI in the blink of an eye: Real-time decisions redefined

Artificial intelligence (AI) has redefined the landscape of decision-making processes, particularly in real-time applications. With businesses striving to enhance efficiency, customer experience, and competitive advantage, AI-driven real-time decision-making is increasingly pivotal. Here’s how AI helps. 

The evolution of decision-making

The history of business decision-making is marked by technological advancements. From the early days of digital technology to the internet era, each step has helped us make decisions faster.  

The real breakthrough, however, has come with the integration of AI and real-time data, which transforms how decisions are made at every level of business operations. Consider that it takes about 20 milliseconds for a thought to traverse a synapse in our brain. In comparison, today’s web pages load in two seconds or less—but the ads on those pages load in less than 200 milliseconds, which is literally the time it takes a human eye to blink. Within that time, there's an auction for your eyeball to determine which ad gets placed for you, an individual viewer, out of the hundreds of millions of possible eyeballs, and a business has milliseconds to place a bid—50 milliseconds to be precise.

Historical context

The journey of technology-aided decision-making began in the 1950s with the introduction of digital technology in business. Early computers enabled accurate monthly accounting, laying the foundation for data-driven business decisions. “If you go back to the 50s, it was the first time we saw digital technology come into businesses,” Aerospike CEO Subbu Iyer states in his Real-time Data Summit keynote presentation. Companies use computers for accurate monthly accounting information.

Later decades brought other advancements, from personal computing to the internet era, progressively shortening decision-making cycles from annually to daily. “Then we move into the personal computing era,” Iyer elaborates. “All of a sudden, we are able to really start making business decisions on as little as a weekly basis. So we go from annually-monthly-quarterly to quarterly-monthly-weekly.” As the internet came along, companies could collect data on a nearly live basis, meaning we can make decisions about our businesses on a day-to-day basis. This is all just the result of better access to information and being able to make these decisions much faster.

However, despite technological advancements, human cognitive processes remained the bottleneck. Human decision-making is inherently limited by the speed at which our brains process information. “The thing that ultimately blocks us, the ultimate bottleneck, is that our brains are in the loop,” Iyer says. “It's technology giving us all the information, but it's us making the decisions, and that makes it very hard to get to less than, say, a daily basis.” This limitation has historically constrained the speed and efficiency with which businesses could respond to new information.

AI and real-time data: A game changer

Integrating AI with real-time data platforms marks a pivotal shift in decision-making capabilities. By processing large amounts of data almost instantaneously, AI supports businesses in making faster, more informed decisions, thereby outpacing humans' natural cognitive limitations. This section discusses the specifics of how AI and real-time data work together to revolutionize decision-making.

"Increasingly, we are no longer forced to choose between being really thoughtful and being really fast,” Iyer says.

The speed of AI

Companies such as Quantcast and PayPal exemplify this capability.

Central to this capability is the use of real-time data platforms. These platforms, such as Aerospike, ingest millions of data points per second, supporting high-frequency reads and writes with consistently low latency. “Aerospike is ingesting millions of data points and millions of writes per second,” Iyer says. The decision-making pipelines are reading millions of reads per second from the same data platform simultaneously.”

Real-time data platforms are intended to handle the rigorous demands of AI applications, meaning data is processed and available for decision-making in milliseconds. This is critical for applications that require immediate responses, such as personalized advertising and fraud detection.

Case studies: AI in action

Here are some examples of how AI helps in real-time decision-making. These case studies highlight how AI is applied in real-world scenarios to drive efficiency and improve outcomes.

Quantcast: Real-time advertising

Quantcast uses AI to conduct real-time auctions for ad placements. With AI/ML techniques and real-time data from millions of online sources, Quantcast endeavors to serve the most relevant ads to users almost instantaneously—at scale, Iyer says.

"Quantcast serves some of the largest media properties around the globe,” Iyer explains. "The Quantcast platform processes data from over 100 million online destinations and generates trillions of online signals.”

The ability to serve ads in real-time lets Quantcast improve the relevance and effectiveness of advertising campaigns, driving higher conversion rates. This rapid decision-making process is a testament to the power of AI in transforming digital marketing.

PayPal: Fraud detection

PayPal's real-time AI-driven fraud detection system is the gold standard in financial transactions. It processed 6.5 billion transactions in the first quarter of 2024 alone. 

Its graph database model identifies and prevents fraudulent activities. “PayPal uses a two-sided network with buyers and merchants for sending each other transactions,” Iyer explains. “It encodes this network as a graph with buyers and sellers modeled as vertices in the graph. The real-time graph platform returns graph query results very quickly or in sub-seconds, and the return query results are used in machine learning models for immediate fraud prevention.”

Real-time fraud detection helps maintain the integrity and security of PayPal's transaction network. By identifying and mitigating potential fraud, PayPal protects its clients and customers, improving trust in its platform.

The future of real-time AI

The potential of AI in real-time decision-making is only beginning. As we look to the future, several emerging trends will shape the next generation of AI applications. 

Using enterprise data

Enterprises need to secure their corporate data for privacy and competitive reasons. At the same time, they want to include that proprietary data in AI apps for more contextualized, granular results. We're already seeing retrieval augmented generation (RAG)-based AI apps as a common model for doing this. “RAG allows the use of enterprise data to provide context to large and small language models, resulting in better quality of AI-based decisions,” Iyer says.

Multi-model capabilities

The future of AI applications also lies in multi-model capabilities. Real-time AI applications will increasingly use multiple data models—key-value, graph, document, and vector—within one platform. This multi-faceted approach allows for the development of richer AI applications that handle complex data types while continuing to deliver real-time insights with low latency.  "You can build a powerful application—key-value vector and graph within the same application—all from a single Aerospike Database that supports these multiple data models and get the same predictable real-time performance, low latency, and high availability,” Iyer says.

By integrating multiple data models, businesses develop more sophisticated AI applications that provide deeper insights and more effective real-time decision-making. This multi-model approach is poised to drive AI's next wave of innovation.

Continuous learning

Finally, as AI models evolve, continuous learning becomes important for maintaining accuracy in real-time decision-making. Traditionally, models are trained offline, sometimes updated periodically. But that’s not enough. “To get the most up-to-date information and make the most accurate real-time context-rich decisions, we see the adoption of continuous learning as a necessary step,” Iyer predicts.

Continuous learning means AI models are always up-to-date, helping them to support the most accurate and relevant decisions. This shift towards continuous learning represents a significant advancement in the AI field.

Make critical decisions in real time

The integration of AI and real-time data is revolutionizing decision-making processes across industries. By processing and acting upon data faster than humans can, AI lets businesses make informed decisions almost instantaneously, driving efficiency, enhancing customer experiences, and beating their competitors. As technology advances, adopting enterprise data, multi-model capabilities, and continuous learning will further propel the capabilities of real-time AI, heralding a new era of rapid, data-driven decision-making.