When it comes to weather events that may affect operations, today’s enterprises have great insights into the future — thanks to satellites and advanced forecasting systems that continue to advance technologically. The same holds true for sales and revenue forecasting, as companies leverage sophisticated predictive analytics to gain a clearer view of their financial future.
Now, enterprises are taking their predictive capabilities to new heights, thanks to the power of artificial intelligence applications driven by high performance computing systems. This new breed of predictive applications is a cornerstone to making better business decisions, keeping systems and equipment in top shape, understanding the movement of markets and much more. In many cases, these forward-looking applications are both predictive and prescriptive, meaning they tell you what’s likely to happen and recommend steps you can take to address emerging issues and influence outcomes.
Let’s look at some specific use cases for AI-driven predictive applications across a range of industries.
Enabling predictive maintenance
In many industries, predictive systems driven by machine learning techniques are helping operators keep equipment up and running at an optimal performance level while reducing maintenance costs. These systems monitor the performance and condition of equipment to anticipate failures and enable proactive maintenance.
A few examples:
- Smart manufacturers are using AI systems in conjunction with data from sensors and the Internet of Things to predict and prevent machine failures. The goal is to use predictive maintenance to avoid issues on the manufacturing line, resolve problems quickly and proactively, and minimize disruption to operations.
- Wind-energy producers are using AI systems in conjunction with data from sensors and the Internet of Things to predict the likelihood of wind turbine failures and proactively address issues that may arise.
- Telcom providers are using machine and deep learning systems to guide preventative and predictive maintenance related actions to reduce downtime of mission-critical systems, such as telephone billing clusters.
The payback for these applications can be huge. A report by McKinsey & Company notes that AI-driven predictive maintenance can increase asset productivity by up to 20 percent and reduce maintenance costs by up to 10 percent, while greatly reducing machine downtime caused by maintenance work.1
Predicting healthcare outcomes
In healthcare settings, the ability to predict the likelihood of patients developing certain complications and conditions can help clinicians work proactively to prevent problems and improve patient outcomes.
Penn Medicine, which operates a network of healthcare facilities in Pennsylvania and New Jersey, proved this point by using a collaborative data science platform it created with Intel. In its first trials of the platform, the healthcare provider developed algorithms to help predict and prevent two of the most common and costly issues for hospitals: sepsis and heart failure.
The results were amazing. For example, Penn Medicine was able to correctly identity about 85 percent of sepsis cases and to make these identifications as much as 30 hours before the onset of septic shock. These AI-driven results were far better than the expected outcomes with conventional methods. With these more accurate and timely predictions of the sepsis risk, clinicians can deliver treatments sooner, speeding time to recovery for the patient and saving resources for the hospital.2
Assessing credit risk
Financial services companies are using AI to sharpen their ability to predict the credit worthiness of loan applications and accelerate the credit risk assessment process. These capabilities can be key to reducing the losses that come with loans that go into default, according to the research firm McKinsey & Company.
“With machine learning and other technologies, risk models can become more predictive, which suggests that credit losses may fall by up to 10 percent,” McKinsey notes. Even better, a McKinsey survey indicates that over half of risk managers expect credit decision times to fall by 25 percent to 50 percent with the power of AI on the backend.3
The systems that drive the software
Machine and deep learning systems typically require large amounts of data, fast compute, fast storage, abundant memory and high‑bandwidth networking. Together, Dell EMC and Intel meet this need with a range of products and solutions optimized for AI-driven applications.
The new Dell EMC Ready Solutions for AI – Deep Learning with Intel provides a ready-to-go solution for training deep learning models. The solution, based on tested and validated configurations, includes all the hardware, software and services that organizations need to get an AI environment up and running quickly.
And to help meet the distinct demands of data-intensive applications, the Deep Learning with Intel solution provides documented and supported integration of Dell EMC Isilon storage to accelerate the movement of data. Isilon All-Flash scale-out NAS is designed to deliver the analytics performance and extreme concurrency at scale to consistently feed data-hungry analytic algorithms and eliminate I/O bottlenecks.
Thanks to the power of AI, predictive applications are giving enterprises an unprecedented view of trends, issues and risks, so they can proactively prevent potentially disruptive problems and achieve better business outcomes. And today, the required HPC systems are readily available to power the data-intensive applications that deliver the predictive capabilities.
Now, the challenge is to imagine new and innovative ways to use predictive applications across the enterprise.
To learn more
- To explore leading-edge solutions for powering AI-driven applications, visit Dell EMC Ready Solutions for AI.
- For a broader perspective on the state of data and the technologies that make sense of it, read the “Welcome to the Data Era” interview with Dell EMC executive John Roese.
Advancing the Frontiers of AI
Dramatic advances in data analytics and high performance computing capabilities have created a foundation for the adoption of AI-driven applications in the enterprise. However, these enabling technologies are only part of the AI story. The other part is the rise of smarter algorithms that can glean insights from massive amounts of data. In this series of posts, we explore these building blocks for AI solutions in enterprise environments.
- McKinsey & Company, “Smartening up with Artificial Intelligence (AI) — What’s in it for Germany and its Industrial Sector?” April 2017.
- Intel, “Predictive Analytics in Healthcare,” September 2017.
- McKinsey & Company, “The future of risk management in the digital era,” December 2017.