ElectrifAi: Machine Learning Can Help Corporations Survive Economic Uncertainty

The global economy is currently in a state of uncertainty. According to Bloomberg Economics, it’s “effectively certain” the United States will go through a recession in the next 12 months. The severity and length of the recession are unknown, but organizations across all industries are scrambling to prepare now before the downturn hits. Fourteen years of cheap interest rates, labor, and input costs have ended and we are now on the reverse side.

“Labor shortages, shifting work patterns, and rising costs are a fact of life, and it is very difficult to put the genie back in the bottle,” wrote ElectrifAi CEO Edward Scott on spiceworks.com. “Certain tasks currently performed by humans, however, can be automated by leveraging computer vision. Frequently the quality is better, the throughput higher, and the costs are less.”

The good news is that companies can still thrive in times of uncertainty. They just need to use their data to make more agile decisions about their resources. According to machine learning and artificial intelligence innovator ElectrifAi, many enterprises are turning to machine learning for answers.

Despite the economic decline, machine learning, natural language processing, and computer vision are the future — in fact, according to professional services firm PwC, AI will contribute $15.7 trillion to the global economy by 2030. And no one knows the ins and outs of this innovation better than the ElectrifAi team, which shares how machine learning will help companies maximize profits during the recession.

How Corporations Can Use Machine Learning During a Recession, According to ElectrifAi

Each year, businesses generate massive amounts of data. Much of that data is unstructured, including call center voice recordings, IoT data, video, images, PDFs, emails, and even handwritten notes. Moreover, businesses invest billions of dollars annually trying to capture, store, and transform both the structured and unstructured data into something usable to create some value. The issue is that they generate and store this data — but rarely put it to optimal use. With machine learning, organizations can clean, transform, and organize data to find efficiencies and drive business value.

Many companies avoid ML, fearing its complexity, but ElectrifAi assures business leaders that prebuilt solutions make it a cinch to execute ML and AI. ML is a subset of Ai in which machines imitate intelligent human behavior and which learn without being specifically programmed.

Whether it’s customer-facing tasks or back-office operations, ML has so many uses. As the economy slows down, ElectrifAi sees organizations investing in ML for applications ranging from customer service to cost and operational optimization.

Natural Language Processing

Natural language processing is a type of machine learning that can gauge human sentiment. ElectrifAi sees enterprises using this technology to improve customer service in call centers. The technology not only helps organizations resolve calls more quickly, but it also reduces costs and increases customer satisfaction.

Computer Vision

Computer vision combines machine learning with cameras to glean data from images. With computer vision, manufacturers can detect defects on manufacturing lines more efficiently than with human quality control. In health care, this technology can save lives by spotting anomalies in scans better than the human eye can.

Computer vision makes it possible to take tedious, repetitive tasks away from employees to save time and significantly reduce errors. This isn’t about replacing all human employees, but rather freeing up their time so they can focus on more valuable tasks, such as serving customers.

Demand Forecasting

It is critical for companies to understand the fundamental demand for their products and services. Effective forecasting helps businesses avoid stocking too much or too little, and machine learning helps brands hit their Goldilocks number to make the most of their resources. But the fact is that many companies are struggling to properly forecast demand in the wake of the pandemic. Demand signals that worked in the past are suddenly no longer effective and, even worse, wrong. Many retailers are seeing this firsthand with distribution centers piled high with unsold inventory.

Enterprises can’t rely on pre-2020 data to make predictions in today’s environment, which means demand forecasting as we know it is skewed. However, ElectrifAi’s team believes that machine learning has the power to improve the accuracy of demand forecasting. It aggregates data from more sources and quickly analyzes the numbers to predict everything from economic performance to weather patterns, which can affect customer demand.

Dynamic Pricing

Dynamic pricing is closely related to demand forecasting. The two are inextricably linked. With machine learning, businesses can use data to dynamically price their goods depending on the business objective. Those objectives could include market share, profitability, or revenue, to name a few. Businesses can adjust prices dynamically depending on the objective. For example, when news about the recession hits, a dynamic pricing ML solution would automatically decrease prices to stand out to cost-conscious shoppers.

Cost Optimization

Enterprises have to spend money to make money, but they need to minimize their liabilities during times of economic uncertainty. Fortunately, machine learning makes it possible to find cost-saving opportunities that won’t harm a business or its customers.

Machine learning categorizes and classifies corporate expenses (direct and indirect) to help businesses find areas where they can save. For example, ML solutions such as ElectrifAi can show CFOs where their money is going, improve negotiating leverage over vendors, and spot maverick spend as duplication and waste. They can also help companies benchmark their financial performance by enriching internal financial data with external industry data.

Marketing Personalization and Automation

Customer churn is a big deal, especially in an uncertain economy. Personalization is the key to engaging and retaining these customers, but it isn’t easy at scale. With machine learning, brands can efficiently scale up their personalization and promotion efforts through automation.

This is about more than simply dropping the customer’s name into an email. ML makes it possible to personalize the content shoppers see on a website and offer personalized discounts based on their order history.

Risk Assessments

Risk is always present in a business, but without a proper understanding of this risk data, enterprises don’t understand their true risk levels. Machine learning makes it possible to respond to early warning signs that human analysts might miss. Early action can help businesses course-correct before it’s too late, so machine learning models are a must for mitigating risk during a recession.

Recession-Proof With Machine Learning

The pandemic taught businesses some hard lessons. Collectively, enterprises learned that forecasting, automation, and investing in the customer experience can make or break their business in uncertain times. Cost-cutting is on everyone’s minds right now, and while ML does require a modest upfront investment, it saves enterprises millions every year. The ROI is compelling.

Machine learning innovators such as ElectrifAi are transforming what it means to use ML and AI in a business context. It’s impossible to see the future, but ML is the best alternative to a magic crystal ball. In the near future, ML will be more accessible to all industries, making it possible for businesses to spend less and do more.