4 Kinds of AI Enterprises Will Spend Money On


The market for artificial intelligence (AI) as a whole is attracting the interest of businesses that want to take advantage of its technologies, but certain kinds of AI are especially lucrative.

When companies and the engineers developing the respective technologies focus on those aspects, mutual gains should result.

4 Kinds of AI Enterprises Will Spend Money On

Kinds of AI Enterprises Will Spend Money On

1. Intelligent Data Capture Solutions

Whether it’s a doctor working with patient files or a company processing invoices, paperwork can take up a significant part of the workday. It can also be costly.

One person’s detailed experiment about processing data found it took from a half-hour to nearly three hours to process one invoice. And, the cost involved for processing was $13.89 per piece.

Until recently, working with the data on invoices and other kinds of forms required a significant amount of employees, but now, AI is reducing workloads. By some reports automated invoice processing solutions can cut your AP processing costs by 80%.

Impact of AI on GDP

A report from PwC about the impact of AI on GDP and society highlights that between 2017 and 2030, labor productivity improvements due to AI technology could account for over 55 percent of all GDP gains.

AI data capture solutions like docAlpha could reduce human staffing needs as well as the overall cost involved. AI options like these are particularly promising for enterprises that regularly process substantial amounts of data and depend on high levels of accuracy.

Some AI tools could catch errors fatigued staff members may miss and should handle them much faster than humans.

The intelligent data capture market is a niche sector, and analysts’ forecasts group this AI technology in with other kinds, such as image recognition, and sometimes do not segment spending at all.

For example, a report from Forrester aimed to gauge the overall spending on AI through 2025 with collective research and found the range could be between $644 million to $126 billion, though it’s believed the low end of that spectrum is unlikely.

2. Job Recruitment Tools

Statistics indicate 61 percent of company recruiters expect to hire more people in 2018. Although increasing the size of the workforce is a necessity for many HR professionals, their workload goes up as they strive to find the most appropriate candidates and not overlook people who’d be good fits.

Also, although reports don’t investigate the amount of AI-specific tools for hiring, an evaluation by Global Industry Analysts Inc. expects the recruitment software market to reach $2.7 billion by 2022.

The hiring process is time-consuming, which can affect organizations’ bottom lines. With the help of AI recruitment tools using automated screening processes, the time it takes to hire candidate could go down from 34 days to just nine.

Reduce HR Workload

Moreover, research shows there’s a growing availability of applications that reduce the HR department’s workload.

A February 2017 report from Deloitte notes the ways companies are experimenting with talent acquisition offerings is a substantial disruptor in the HR industry and clarifies 70 percent of those offerings come from third-party establishments.

In addition, it says AI and other cognitive technologies are among the most innovative available.

Arya is one example of an AI recruiting tool. It identifies the characteristics of high-quality candidates, finds people with those traits and feeds them into the recruiter’s pipeline.

Using AI assistants to meet hiring targets could be both a valuable short and long-term investment. It saves time for recruiters and increases the chances of hiring excellent members of the workforce.

3. Chatbots

People are already accustomed to using messaging platforms to get in touch with friends, family members and businesses, so it’s no surprise developers have leveraged that familiarity when creating an emerging kind of AI-powered technology — chatbots.

An Accenture report reveals the chatbots market is already worth more than $1 billion and should climb to $1.86 billion by 2020. Companies know they must provide excellent customer service at all times, and chatbots help them do that by answering questions even outside of normal operating hours.

Chatbots Improve Human Customer Services in AI Enterprises

Plus, chatbots relieve the burdens of human customer service representatives by speedily answering the most common queries. They enable the customers who want to know those things to get assistance without ever talking to people.

There are also some things AI can do better than humans, such as spotting patterns and correlations. Business leaders could rely on data collected by AI platforms about the frequency of certain questions or times of day people typically make contact and use it to make business decisions.

Chatbots capture leads, too. Apartment Ocean is a real-estate-centric chatbot that helps people find homes and determines what individuals are looking for before they speak to housing specialists.

4. Deep Learning Applications That Detect Suspicious Activities

Deep learning is a subset of artificial intelligence that inspired by how the human brain learns. The deep learning platforms get smarter through exposure to millions of data points, and they’ll likely play a major role in pinpointing potential security issues for a network or its data.

An analysis from Juniper Research predicts companies will spend $9.2 billion on online fraud detection solutions by 2020.

Furthermore, insights collected by the McKinsey Global Institute suggest deep learning applications will factor into thwarting the efforts of cyber-criminals, as well as those who do their misdeeds in the offline realm.

The McKinsey report highlighted almost 600 discrete uses for AI across various industries. It noted approximately 300 of them involved deep learning.

On going Deep Learning Projects

It brings up how governments are already using deep learning to detect activities that could harm people in public places or reduce the effects of cyber-attacks.

Similarly, banks use AI to find instances of money laundering or depend on it to notice shared characteristics of fraudulent transactions that humans might overlook.

Although not all those uses are exclusive to the Internet, this kind of AI will undoubtedly figure into the online fraud solutions figure mentioned above.

A company called Teradata recently assisted Danske Bank with analytic techniques enhanced by artificial intelligence to spot fraud. Using that new system, the financial provider decreased its false positives rate by 60 percent.

AI will aid in locking down networks to keep cybercriminals from orchestrating successful attacks, too. According to a 2018 global report from EY, cyberattacks are the top evolving risk for banks, and AI will play a key role in reducing it.

One of the companies making strides in AI-based cybercriminal detection is Cylance, a startup founded in 2012. It offers products that identify when malware infiltrates a network, then stop it before it can do damage.

Wrapping Up

Representatives associated with companies interested in investing in AI soon, along with people who develop AI technologies for commercial reasons, should keep an eye on developments in these four areas.

They show significant promise and could drastically alter corporate expenditures and the achievements gleaned from those investments.

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Author: Kayla Matthews