Artificial intelligence (AI), machine learning (ML), deep neural networks (DNN)… Those seem to be the buzzwords everyone has heard of. They all get along with the notion of an intelligent application.
While the notion and use cases of AI/ML solutions have been exponentially increasing, there is a huge amount of enterprises and organizations that have yet to adopt artificial intelligence technologies into their business strategies.
So what are all those, how smart an app can actually be, and what's there for enterprises? Let's make a quick overview of what is artificial intelligence, the ML definition, and what deep neural networks stand for in various business domains.
What is AI and its major subsets?
Artificial intelligence is a simulation of human intelligence performed by various electronic devices, computers in particular. There are many subfields of AI that are currently progressing in adoption.
The major subsets of artificial intelligence are:
- Machine Learning;
- Deep Neural Networks;
- Computer Vision.
Unlike robotics and computer vision that require the use of additional devices, machine learning and deep neural networks are greatly applied within just intelligent applications. Actually, every app can be transformed into an intelligent one using artificial intelligence technology and the best practices of its implementation that are performed by professional AI developers.
The benefits of AI for business
Why would these technologies be interesting for any business?
In fact, the adoption of AI in business is accelerating. Around 31% of worldwide companies are already utilizing some AI technologies and nearly 43% of them are exploring their options in the AI adoption for their business as IBM's Global AI Adoption Index suggests.
That's just the beginning! Where there are big amounts of data, the use of AI is crucial. Companies incorporate artificial intelligence in business to save money, increase efficiency, get important insights and create new markets with long-lasting and unimaginable business opportunities.
Just remember “the blue ocean strategy”! That's when a business produces an innovative product or generates a novel value, thus creating a new market where competition is irrelevant. It is one of the major benefits of AI adoption in business, however, there are a few more to name. It is one of the major benefits of AI adoption in business, however, there are a few more to name.
The Blue Ocean Strategy
What are the advantages of AI development in enterprises?
Boost Operational Performance
AI significantly automates manual processes and improves interdepartmental cooperation. For instance, ML, DNN, and natural language processing (NLP) allow organizations to get reliable, relevant, quickly generated, and intelligent amounts of data for faster and data-powered decision-making.
Decrease Functional Costs
By automating routine tasks AI technologies allow businesses to save time and focus on more complex things. Consequently, it saves lots of operational expenditures. For example, ML implemented in the customer service line allows to answer most customer questions by a chatbot and delegate only complex issues to humans which cuts costs for hiring CS agents.
Open Up New Channels of Revenue
By incorporating new opportunities, companies can not only save money or grow work efficiency but also introduce new lines of revenue. For instance, after a thorough analysis of user activities using ML the AGL energy company has created a new revenue channel via a new product. The virtual power plant allowed users to give back the energy to the grid that generated additional income.
Meet Customer Expectations
AI technologies help to gather valuable insights about customers, analyze and identify their needs even before the users themselves are able to define them. This way businesses can anticipate customers' future desires and best satisfy them. Much like automotive companies are now heavily investing in autonomous vehicle technologies that require image recognition, machine learning, and other AI tech.
Launch Innovative Products
Once some brand introduces a venture on the market, everyone starts copying it if possible. This way, the business niche becomes full of competitors very fast. New technologies and approaches to business allow creating new markets and novel demands where competition is irrelevant. As a result, AI technologies like any other innovations provide huge room for a bigger user base and more profits.
Top use cases of AI/ML technologies worldwide
Technological giants like Google, Apple, Microsoft, and others have long recognized the real value of artificial intelligence in business. Now they continue competing with each other on the number of acquired ML startups introducing broader opportunities in withstanding the market changes.
Other than that, more and more companies start following the trend and implementing the best practices of AI into their specific business domain. It all helps to streamline business processes, optimize efficiency, and facilitate data-driven decisions.
Let's see the most prominent applications of artificial intelligence and its major subsets like ML, DNN, etc.
Business analysis is vital in every part of enterprises no matter the domain. It helps to evaluate every in and out of existing business operations and find a new path for its improvement. Artificial intelligence allows businesses to find crucial insights and make data-driven well-informed decisions through predictive analysis, enterprise performance assessment, and business reporting.
One of the best examples of AI applications in business analytics is the DOMO business intelligence platform. It incorporates predictive analysis, machine learning, and AI to extract data from multiple sources (like Facebook, Shopify, etc.) and provide useful predictions after thorough analysis. The BI platform is used by such big companies as Mastercard, eBay, and Univision to boost their business performance.
Privacy and data protection is the biggest concern these days. Artificial intelligence accompanied by machine learning and deep neural network technologies allows organizations to detect fraudulent activities, protect from cyber threats and even prevent fraud before it happens. It is now possible to do much faster without any human intervention.
Machine learning helps to analyze behavioral patterns, detect anomalies, and this way secure crucial data. It is especially important in financial operations. But not just that.
Various spam actions can also be detected using AI-powered technologies. To name one of the outstanding solutions introduced by Facebook is DeepText. It is a DL-based text recognition engine aimed at reducing the amount of hate speech in social networks and introducing spam filters for a better user experience. Other giant companies like Google also use AI to enable spam filtering.
Artificial intelligence can also check the state of computing systems and balance the workload depending on the gathered and analyzed information. Not to mention, the impact it gives on the whole infrastructure of business IT services, collaboration solutions, and security systems.
The Cisco Systems company has been implementing the best practices of artificial intelligence and machine learning within the whole IT network infrastructure starting from collaboration solutions to security systems and other specific digital platforms.
Among their artificial intelligence implementations is an AI platform that helps to create the next-gen voice and chat assistants for internal collaboration within the company. Another serious AI implementation is a hyper-converged infrastructure to maintain heavy loads on IT systems. Not to mention, their ML solution aimed at increasing the system’s security.
Some companies have already introduced a new notion that lies in providing flexible billing plans for customers. The decisions on how much a client can pay for the platform's services are based on a number of factors and overall on the user's behavioral pattern.
This feature is able to provide various marketplaces with unique insights and advanced billing prospects. Moreover, this functionality can enhance the user experience and make the shopping experience much more fun.
For instance, The HOTH SEO company has introduced an ML-powered solution to billing plans that they offer for recurring subscriptions for their loyal customers. Machine learning allowed the enterprise to identify the trends in credit card declines and various fraud patterns leading to chargebacks. All this venture helped The HOTH to raise revenue with tiny human intervention.
Artificial intelligence along with overall automation allows businesses to enhance the retail experience in online stores and huge marketplaces. Amazon, eBay, Alibaba are just a few companies that have revolutionized their customer experience using AI.
Intelligent recommendation systems are the biggest driver for the e-commerce industry. For instance, an e-sales recommendation app Apptus analyzes online search user behavior and offers specific products based on allocated patterns of customers' likings and predictions. The technologies under this solution are machine learning and top big data techniques.
The AI-powered recommendation systems can be equally useful for any business. Youtube adopts AI technologies for the recommendation of videos. Spotify lists the preferable songs for its users. Bigbasket implies intelligent suggestions of groceries. The list can be continued!
How “Uinno” helped “OnlyFans” to prevent fraud
Fraud prevention is probably the most crucial AI/ML adoption use case required for many businesses. One of the reasons is financial operations. Finances are an important and essential part of any enterprise. Where there is money, there are always people searching for opportunities to commit fraudulent activities.
That's exactly what happened with a huge content subscription-based platform. With the growth of the social network, the number of fraudulent cases grew up significantly. Once the problem has taken away huge amounts of revenue, the company has turned to ML experts in Uinno, recognized as top artificial intelligence company by DesignRush.
After a thorough analysis of the challenge ahead and deep brainstorming, we have introduced a decent solution. Eventually, it helped our client to reveal more than 80% of fraud activities even before they happened. Machine learning and deep neural networks technologies allowed us to save tons of money for our clients. Find out more details in our ML solution case study.
The success of the introduced ML solution has tightened our cooperation. We continue developing other AI-powered solutions for such a big social networking platform as OnlyFans is while leveraging face recognition and other intelligent AI-powered technologies.
Likewise, every business can benefit from implementing fraud prevention techniques using AI technologies including machine learning, deep neural networks, NLP, etc.
Artificial intelligence, machine learning, deep neural networks, and other AI-powered technologies provide outstanding opportunities for cutting expenditures, growing profits, and much more depending on each particular business case.
Organizations that have implemented intelligent algorithms within the last few years, already experience the return on investments these days. And there are more opportunities to come in the future.
If you have yet to introduce AI/ML technology into your business development strategy, it is time to focus on its implementation in 2022 and the next years to come. It will greatly improve time- and cost-efficiency, uncover new lines of revenues while anticipating the most demanding customer expectations with intriguing features of brand new products.
Need a tech consultation? Let's discuss the tremendous opportunities that you can open after creating an AI/ML solution for your business!