The most important artificial intelligence examples for today's enterprise leaders are the ones that have a tangible impact on the bottom line.
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Artificial intelligence (AI) isn't "new" by any stretch of the imagination. The technology is now a fundamental part of our professional and personal lives, in applications that range from the routine to the spectacular.
Here, we'll look at the state of AI and its role in business, and then examine seven distinct artificial intelligence examples—all of which are transforming today's enterprises.
AI is everywhere in modern businesses, and it shows up with varying degrees of complexity. On the "simple" end of the spectrum, there are the robots and automation systems found on the assembly lines of many manufacturing facilities. At the spectrum's other end, there are complex deep learning algorithms that predict future market developments and power the decision-making processes at the executive level across financial organizations. Between those poles exist hundreds of other applications, in virtually every major industry.
AI is nothing short of transformative. It accelerates and optimizes so many processes, and has the potential to improve operational efficiency, production, product and service quality, cost efficiency, and so much more.
Before we go further, it's important to quickly review the most important terms under the broad category of AI.
With these fundamental definitions established, we can dive into the details of several use cases where AI capabilities are powering—and transforming—major enterprise operations. Keep in mind that the tools used in these examples aren't necessarily just AI.
The applications of AI technology detailed below refer to concepts in use throughout various industries. Levels of AI application adoption vary between sectors, but currently, there are few verticals that would not benefit—or benefit only minimally—from these uses of AI. Going forward, the value of these AI tools is only expected to increase by numerous AI experts and business leaders, according to research by McKinsey.
Much like AI, data analytics has been well-established as a critical discipline in the business world for much of the last decade. As such, it was inevitable that data analysis and data science experts would collaborate with AI engineers to develop AI applications and algorithms that broadened the utility of analytics.
An advanced AI or machine learning model facilitates major analytics efficiency improvements, automating and significantly accelerating critical steps like data processing, analysis, and report generation. An AI foundation is also ideal for organizations that need real-time data analytics.
But even more importantly, these systems can facilitate predictive analytics and prescriptive analytics, because AI and ML can efficiently process the vast volumes of data necessary for such calculations. The ability to forecast possible outcomes of key business initiatives and receive recommendations on alternate courses of action is invaluable to any enterprise. These capabilities help organizations embrace the emerging fusion of quantitative and qualitative analysis known as decision intelligence.
Whether they are dual-layered or have enough layers to qualify as deep learning, neural networks represent a massively important area of AI. With that said, many of the most exciting applications for neural networks involve those that feature one or more hidden layers.
Augmented reality (AR) platforms use cameras and sensors to collect data from an environment and then replicate it in a virtual setting. These are prevalent in video games, but have also found various business applications in recent years throughout e-commerce, healthcare, education, manufacturing, and aerospace.
With the support of an ML or AI algorithm and a wealth of data, AR systems create more effective and immersive user experiences. For example, high-level computer vision could allow a surgeon at a teaching hospital to realistically simulate the most complex procedures for residents even before they start working with cadavers, using object labeling, detection, and recognition features to make the process come alive.
Within the broad field of preventive maintenance—which has become quite popular among facilities managers in manufacturing and other heavy industrial sectors—there are several subcategories. Condition-based maintenance is one of these. In this framework, equipment health is steadily monitored and repair or upkeep tasks are scheduled well before machines show warning signs of malfunction or imminent failure.
AI algorithms for condition-based maintenance ensure that equipment status data is processed in real time and readily available to maintenance teams. Using information from vibration analysis, infrared thermography, and ultrasound scanning, management can prioritize the most urgent repairs. The AI will automatically send work orders and adjust maintenance schedules as needed.
The following case studies showcase examples of AI applications in action across a variety of industries—from financial services and healthcare to telecommunications.
U.S. Bank initially adopted AI for fraud prevention, but then recognized AI's potential for strengthening the customer experience (CX).
Now, U.S. Bank leverages its AI to create highly personalized accounts for customers at scale, actively directing them toward financial products and solutions best suited to their individual needs. At the same time, this level of personalization makes it easier for the bank to detect and respond to abnormal, potentially fraudulent account activity. This creates better, safer experiences for customers while reducing operating costs and mitigating fraud.
When changes to Japanese data regulations made real-world data (RWD) more broadly accessible in the late 2010s, Chugai Pharmaceutical was ready to unlock the power of its RWD archives. The company just needed the right tools to effectively process and leverage the data it had acquired in recent years.
Ultimately, Chugai adopted cutting-edge AI tools to analyze its RWD—a broad spectrum of healthcare information ranging from basic patient electronic medical records (EMRs) to genomic data. With the help of Vantage, Teradata's connected multi-cloud data platform for enterprise analytics, the organization ensured RWD was readily accessible to its AI systems and could be accurately validated. This helped bring significant improvements to the research and drug discovery processes.
Saudi Telecom Company (stc) adopted a new corporate strategy in 2019 called DARE—digitize, accelerate, reinvent, expand. Big data and robust analytics would be crucial to this initiative, as stc wanted to gauge performance using more than 400 key performance indicators (KPIs). Data points related to the customer experience—call setup times, speech connection quality, bandwidth speeds, and dropped calls—were particularly important.
Using a variety of AI and ML tools—including Vantage's nPath feature—stc could comprehensively map customer interaction data across multiple channels and make sure customer service issues were addressed as quickly as possible. AI also helped the organization craft more personalized marketing campaigns and sales efforts to bolster the bottom line.
It's clear that there are major AI benefits for enterprises, both in general and industry-specific use cases. But as with any technology, it's also critical to consider the complexities facing AI.
Even at its most advanced levels, AI is only as good as the data that goes into it and the algorithms with which it's programmed. If bias is introduced at any point, the resulting analysis can't be trusted.
Enterprises still using legacy software or infrastructure may find it difficult to implement AI at first due to the high processing speeds required and the need for skilled personnel to oversee it.
AI and its subsets are all works in progress, with engineers constantly working to improve them. Tools and solutions that are up to date one month may be near-obsolete the next.
Every AI platform requires massive data sets to function effectively. This not only means organizations need appropriate data storage, but will also have to rethink security so that it properly addresses the volume and complexity of AI-associated data.
Despite the potential complexities of AI, with a well-planned and thoroughly tested strategy, enterprises can solve their most complex business challenges with this powerful, transformative technology. These four steps will help you make the most of your AI investments.
Ethical guidance, in the form of a concrete ethics policy, should be a cornerstone of your enterprise's AI usage, with a strong focus on mitigating bias.
Although it's not always feasible to upgrade technology across an enterprise in one fell swoop, you must work as quickly as possible to overhaul any legacy tech that would impede AI usage. The same goes for talent—HR must pursue AI experts aggressively if these professionals aren't already on staff.
It's crucial to keep track of the latest AI advances—but you also can't react to every new development. The "next big thing" may end up becoming obsolete long before your current system. Data teams, AI experts, and other relevant stakeholders should work together to shape the organization's AI strategy so it's properly proactive, not hastily reactive.
Use technologies that leverage the cloud's scalability and flexibility to store, process, integrate, and analyze data, including object storage and a cloud-ready data and analytics platform like Teradata VantageCloud. Be sure to adopt security tools that comprehensively protect AI data in the cloud and data center, like firewall-as-a-service (FWaaS).
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