TechnologyJan 04, 2023

Benefits of AI: Increase Efficiency, Enhance Safety, and Improve Reliability

Naveen Krishnaraj, Josh Bae, Xing Meng, Yannick Heard, and Carter Caldwell

Recently, artificial intelligence (AI) has been hailed as the ultimate catalyst in propelling the incredible growth of companies across the globe. This data-driven technology has helped many of them automate their slow, manual processes and scale their day-to-day operations at an increasingly rapid pace. Additionally, they are not sacrificing quality over quantity in any way; AI has allowed for more reliable and safer practices in a capacity previously thought impossible. Its practical use cases are growing as companies harness AI to make better-informed business decisions.

We’ll highlight three key ways AI benefits various industries in their efforts to innovate: increase efficiency, enhance safety, and improve reliability.

1. Increase Efficiency

"Getting more done with less." This statement has long been the mantra of one of AI's most powerful skills: automating tasks to cut down on manual effort. Here, we outline a few ways companies have benefitted from the unparalleled efficiency of AI, which include supporting customer service, text comprehension, and image creation.

Supporting Customer Service

One lingering disadvantage of human-assisted customer service is the long wait times required to contact a service representative. This unpleasant situation could be remedied if AI automated the assessment of customer data to improve quality assurance. Rapid identification of product issues results in faster product improvement, which would in turn lead to fewer frustrated customers waiting in long queues.

Companies experiencing high call volumes are using similar technology to completely automate certain elements of the process. When engaged with EmployBridge to help increase the capacity of their call centers, Credera developed a chatbot to serve as the first touchpoint for applicants. The chatbot was successfully deployed and is now handling up to 71% of applicant concerns, freeing up call center agents to focus on more complex issues. In another example of freeing up valuable human resources, Credera and a leading athletic retailer partnered to create an innovative augmented reality tool using machine learning. The tool allowed shoppers to identify products in-store and pull up additional information on their mobile devices without needing a sales associate. The tool had an 85% success rate in identifying products correctly.

Text Comprehension

AI’s ability to unearth valuable information among layers of data demonstrates the efficiency it provides. As the internet becomes saturated with data, even simple Google searches have become less precise amid the widespread digital noise. One practical use for AI is to improve the online search experience.

A fascinating model that is free for public use is the GPT3, an advanced text generation model. This AI system takes input text and generates thoughtful continuations of stories or responses to questions using a neural network that is trained to predict the next word in a sequence of words. Below is an example of asking the model a simple factual question. This is just the tip of the iceberg: Many use cases lie beyond this model, including code generation, summarizing articles, translating languages, correcting grammatical mistakes, and generating stories.

Image Creation

Another efficient use case of AI is digital media creation. Models are trained with vast numbers of images to predict and generate new images accurately. This allows for the creation of a large number of images from the same prompt, saving creators substantial time developing new images. AI, in fact, created the images below. This use case can benefit writers or businesses who want to include images in their articles or create website designs without hiring an expensive digital artist. Additionally, it saves them time, as digital artwork can sometimes take weeks or months to reach the desired result.

2. Enhance Safety

AI is not only a tool to make our lives easier and more productive, but it also has the potential to create a safer environment for us all. AI sets up safety guardrails to reduce human errors by automating or aiding complex tasks. We examine a few ways that AI supports humans by enhancing safety, tracking employee fatigue, automating hazardous tasks, and detecting anomalous health symptoms.

Accident Prevention

People are likely to make mistakes, and this is especially impactful in a workplace where the consequences may be serious. For example, in the manufacturing space (which often involves large, heavy machinery), human errors pose one of the biggest risks to the well-being of employees. The U.S. Department of Labor noted that accident and injury rates spike in the evenings and night shifts by 18% and 30% respectively, while extended workdays of 12 hours is related to a 37% increased risk of injury.

Fatigue has often been associated with workplace incidents but responding with the appropriate AI system could dramatically lower this risk. AI has come a long way in detecting fatigue in the workplace using facial expressions; technologies such as computer vision have been used for analyzing facial expressions to detect fatigue by noticing of sleep-related movement (e.g., yawning, shutting eyes). These technologies have been adopted by industries such as mining, which require the operation of heavy machinery and involve working long hours filled with monotonous tasks.

Similar technology is used to address another major safety hazard: driver fatigue. According to Softweb, driver fatigue (or drowsiness) contributes to about 25-33% of fatal and serious accidents. Many trucking and transportation companies have taken advantage of similar AI systems using live webcams to record and monitor videos of the faces of drivers to detect fatigue and exhaustion.

Automating Dangerous Tasks

Many hazardous tasks could be automated using AI; for example, leveraging AI-powered robots for manufacturing which are much more efficient at executing precise tasks at scale. These robots utilize complex algorithms to automate decision-making and perform repetitive tasks in manufacturing industries. Since these robots are self-learning, the efficiency and accuracy of the process can vastly improve over time. These robots perform all the heavy lifting, which leaves humans to deal with safer tasks. Robots can also be used in environments that are hazardous for humans (e.g., potential exposure to harmful chemicals), doing away with the need to sacrifice the employee's health and improving the overall safety of the workplace.

Anomaly Detection

Another important application of AI in enhancing safety is in healthcare using anomaly detection; this technology is extensively used to detect any deviation in the health trends of an individual. It is used often in diagnosis, for example in medical imaging, to identify anomalies missed by the unaided human eye. Accurate early detection of diseases saves lives and enables proper management of diseases before they worsen. Additionally, AI provides the healthcare industry with tools such as symptom trackers to assist in prescreening.

3. Improve Reliability

We’ve seen how AI can help businesses and now we’ll look at an additional way that implementing AI can help reduce errors. In many business operations, it can help mitigate errors that would otherwise be missed by human observation. Implementing AI in software solutions for adding data, reviewing records, and other administrative tasks seeks to improve accuracy and reduce errors.

Increasing Accuracy

In manufacturing, AI uses technology to automate complex tasks and identify previously unknown patterns in workflows and processes. For example, Fourth Industrial Revolution (4IR) technologies, including machine learning, automation, advanced and predictive analytics, and internet of things (IOT) devices, are a kind of artificial intelligence allowing manufacturers to monitor their facilities in real-time. By collecting vast amounts of operational data, the 4IR enables companies to forecast accurate delivery dates, predict potential supply chain disruptions, troubleshoot production bottlenecks, and spot fix equipment inefficiencies. Examples of AI increasing accuracy in manufacturing include:

  • French food manufacturer Danone Group uses machine learning to improve demand forecast accuracy, resulting in a 20% reduction in forecasting errors, a 30% decrease in lost sales, and a 50% reduction in demand planners' workload.

  • BMW uses automated image recognition to perform quality checks and inspections, and to eliminate pseudo-defects (deviations from the target without actual faults). As a result, they've achieved high levels of precision in manufacturing.

  • Porsche uses autonomous guided vehicles (AGVs) to automate significant portions of automotive manufacturing. Using AGVs, vehicle body parts can be moved from one processing station to another without human intervention, making the facility more resilient to disruptions.

In this deep dive into the manufacturing industry, it is evident that the role of AI has been significant in developing more predictable and reliable machinery to handle everything from simple to extremely complex jobs. AI has offered the most value in planning and production floor operations using intelligent, self-optimizing machines; they can automate production processes, forecast efficiency loss, and detect quality defects for predictive maintenance.

Preventative Maintenance

The powerful tool of anomaly detection mentioned earlier can also be applied to the use case of preventative maintenance. Preventative maintenance is a procedure used to inspect and repair machinery assets before they fail, which is often determined by the time they have been in service.

Preventative maintenance strategies include condition-based, time-based, predictive, and risk-based strategies. It is more cost-effective and efficient than corrective maintenance when the reliability of assets is mission critical. Its implementation would keep the mechanical system operating reliably and profitably for a longer time by reducing downtimes, improving run times, and ensuring fewer premature replacements. Some other benefits of AI to the future of manufacturing include:

  • End-to-end visibility of manufacturing operations.

  • Cost savings and revenue growth.

  • Opportunities to tackle operational challenges and disruptions.

  • Reduce labor costs and stay resilient despite supply chain disruptions.

  • Ensure efficiency and reduce waste.

The Future of AI

Data has been generated at an exponential rate over the past decade, and businesses that have utilized their data with AI technology have benefited the most from it. This understanding is starting to catch on across many industries; according to the annual New Vantage Partner survey from 2012 to 2022, "92.1% of companies say they are achieving returns on their data and AI investments, which is up markedly from 48.1% in 2017. 26.0% of companies have AI systems in widespread production—more than double the 12.1% last year." Among those surveyed, 16% reported a 10-19% decrease in costs, and 18% reported an increase of 6-10% in revenue.

While the spread of AI-related buzzwords in recent news may seem overwhelming and even outright confusing, we hope this article sheds some light on how AI has the potential to create value in whatever industry it lives in. The evidence shows that with proper safeguards established and privacy concerns addressed, AI can provide tangible, substantial gains, benefitting companies and individuals alike to be more productive, safe, and reliable.

Many more benefits of AI are being developed as we speak by great minds in academia and industry with even more positive changes to come soon. At Credera we help organizations unlock the benefits of AI and empower them to achieve more. If you interested in learning more, reach out to us at findoutmore@credera.com.

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