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How AI Can Cut Your Business Costs

After surveying close to 1,000 of C-suite executives, BCG discovered that AI can significantly boost the productivity of manufacturing businesses. Although operational excellence indeed originates from production and factory realm, it can be easily projected to any business model in our times. You may be wondering why it is so. The point is machine learning is likely to be the right instrument to increase revenues rather than cutting down costs. But we know that the profit is the difference between sales and costs. Therefore, we are going to focus on the most popular use cases of this technology. However, there are much more areas of application of machine learning development and integration into existing business processes when it comes to revenue-boosting.

Its said that development of AI and machine learning software may become a genuine fix for operational excellence. Depending on the type of a business, however, algorithms and supporting infrastructure required to infuse the technology into day-to-day operations vary. To top it off, according to BCG, there is a significant gap between what companies plan to do in terms of AI-improvements and what is actually there in place at the moment:

ai and machine learning adoption by companis by industry

As we can see, the majority of AI-powered projects are still in progress globally.

For instance, transportation companies harness the potential of machine learning to estimate arrival times of cabs and pick-up points. Furthermore, with the help of certain algorithms, e-commerce players can predict what a shopper might be willing to purchase next and single out cases of abandoned cart bringing data to life.

The point is the success of machine learning and AI in the modern age is highly dependent on a domain and historical data ML models are trained on to recognize the patterns and help businesses cut down costs increasing operational efficiency.

Further, we’ll elaborate on some use cases of machine learning and AI in their relation to cash-flow improvement.

Chatbots and virtual assistants redefine customer support

Majority of businesses would claim their customer service is there 24/7. Still, not all the customers would be happy with the existing level of customer care. Have you ever found yourself in a situation when a helpline is hard to reach and when you actually do reach it, you’re being sent to an FAQ section with no specific solution for your query?

Chatbots are there to help companies deal with small concerns customers have before those blow up into a major problem. For SMBs, virtual assistants and chatbots can help cut down labor costs that would otherwise go salaries of a human support that’d have to answer common questions. Wouldn’t it be great if a computer program can just assist clients through a simple chat?

machine learning chatbot use cases

Along with a wide adoption of chatbots in support front in the US, this technology is extremely popular in developing countries. According to a last year observation of Statista, AI chatbots are most popular in e-commerce (34%), healthcare (27%), and telecommunication (25%) business verticals in the United States. Apart from North America, Singapore is the hub of chatbot adoption: the country hosts annual Intelligent Chatbot Summit assembling influencers from all around the world.

Clear segmentation and understanding of the target audience

Brands reap multifold benefits of AI when developing strategies to win new clients and coming up with ideas to retain existing ones. The ability of machine learning to get under the skin of a prospect and utilize his behavioral patterns is extraordinary.

After processing multiple sets of data e.g client’s purchasing history, machines can foresee what business issues on customer front that might arise and suggest on effective ways to deal with them.

When nearly everyone is on a quest for highly-personalized customer experience, machine learning development and AI algorithms development is quite justified to offer marketers a powerful toolset to slice the client pool into individual segments and tailor offerings accordingly.

Sorting and grouping with the help of machine learning and computer vision

To err is human, they say. But thanks to extensive development of machine learning solutions that minimizes human touch, the precision of insights received after processing is impressive. Fewer mistakes mean less time spent eliminating the consequences. The fewer hours of human labor means lower operational costs for a company.
With computer vision systems costing up to 60,000USD a decade ago, today these ML-based solutions come at 5,000-20,000USD. After such a relatively low investment, business owners can effectively deal with digital images and interpret high-dimensional data from the real world translating it into numerical or symbolic information necessary to make informed decisions.

Analysis of Big Data in a blink of an eye

What’s more, machine learning development taps into market forecasts, estimates, and predictions and changes the usual way complex market data is handled. Aggregating insights from multiple sources and dealing with large data sets, algorithms help businesses put the automation to use reducing operational costs.

In addition to that, according to Deloitte, time-consuming data exploration and feature engineering performed by data scientists could be automated to a greater extent allowing businesses test the concepts faster with less manual labor at hand.

One more thing: going beyond traditional client profiling with usual credit scores, algorithms and machine learning can be employed to generate credit reports for lenders. Assessing a potential borrower becomes simpler with digital credit evaluation systems powered by AI.

machine learning use cases

With the machine learning development evolution and growth financial institutions and banks started getting valuable insights from the piles of unstructured data e.g. social media profiles, browsing history, and speeding tickets number of potential clients that algorithms bring in order. Further, lenders can use it as an additional source of information necessary to determine the creditworthiness of a borrower.

Fraud prevention and identity defense

To begin with, FinTech players create all-in-one platforms for fraud prevention and emphasize the tremendous potential of machine learning in battling fraud with AI-powered authentication and biometrics put to use. Or even better, after their experiment with several security projects, MasterCard has come up with its own Decision Intelligence.

Mastercard’s top-tier solution brings artificial intelligence on board and assists financial players with real-time approvals of genuine transactions and cuts down on false declines. But you’re probably wondering how the whole process is orchestrated. Such authorization decisioning solution uses machine learning and assigns a certain score to each transaction that is further used to judge the payments.

Before we move to the next point, there is another interesting thing worth mentioning which is a biometrics-based method for counter fighting fraud on voice and digital channels. American Nuance Communication is creating a next-gen solution that processes close to 5 billion successful voice authentications annually. Dealing with clients worldwide, the company offers a layered approach fueled by AI to help battle fraud with face, voice, and behavioral biometrics.

Reducing the statistical noise across multiple business verticals

Machine learning can reduce the statistical noise that degrades the quality of signals and data in analytics, forecasts, healthcare, and capital markets. ML works to define relations among different elements of data and provide maximum context about malware threats. We’ve heard the horror stories like the recent one about Alabama’s medical practice that cost more than 6,000USD in damages. Preventing a threat is usually cheaper than dealing with its aftermath.

ML is about training programs to identify good and bad software in order to single out the bad ones and alert for more security. However, when models are trained with uncertainty toward identifying malware, tagging software as neither good nor bad, the chances they categorize good software as bad and vice versa are quite high.

According to Security Intelligence, an essential ingredient of contemporary machine learning is its ability to source the insights from new data and adjust fast. Such a situation creates a necessity for models to be quick to detect a particular software profile of each organization in order to prevent biases leading to false positives.

As we can see, AI and machine learning solutions vary depending on what problem a business needs to tackle. It’s important to remember that there’s no one-size-fits-all solution when it comes to complex industries, interrelated process webs, and better efficiency at stake.

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