Logo Header Menu

5 ways to use AI to improve business efficiency

Regardless of a company's size or type, its executives typically look for ways to help it operate as efficiently as possible. They understand the link between efficiency and profitability. If employees waste too much time with drawn-out processes or complicated tasks, it'll be hard for the enterprise to remain profitable and adapt to challenges. Fortunately, artificial intelligence (AI) supports the need for effective business operations. Here are five ways enterprises can use AI for help: 5 ways to use AI to improve business efficiency image

1. Use AI to answer queries and support customer engagement

Chatbots are an increasingly popular option for businesses to try, and they use AI to work. Companies often build chatbots that can answer any questions from customers that come through outside of business hours. Some identify the nature of a person’s problem, then either attempt to tackle it with preprogrammed answers or pass the communications to a human support worker. The retail industry, in particular, saw success by deploying chatbots. Global data collected by Juniper Research shows an estimated 2.6 billion retail-based chatbot interactions in 2019, and the company forecasts the number to rise to 22 billion in 2023.

Chatbots are excellent for answering simple questions like “How late are you open today?” or “Do you have gluten-free menu options?” Getting quick answers to queries like those increases the chances customers will choose to do business with one company over another. Equally importantly, when chatbots can give responses in a matter of seconds, there’s no need for humans to stop what they’re doing and address the questions.

2. To enhance reporting speed and accuracy

Company reports reveal things such as which products are selling the fastest and where they’re most popular. They can also confirm the impacts of marketing campaigns on product sales, break down the costs of a new packaging choice or shipping method, and much more. However, as anyone that files reports knows, creating them is a painstaking task, and trying to rush through the process could cause mistakes. Some forward-thinking companies are combining AI with big data analytics. Doing this brings better forecasts and takes some of the burdens off the people who prepare the reports. AI also helps conquer the inevitability of mistakes. Even the most careful people make blunders, often because of mental fatigue.

AI learns to spot patterns in data and gets smarter with time. This means reports get finished faster and contain more-reliable information. The reliability aspect is crucial, especially since recently published research indicated two-thirds of the senior executives polled had no confidence or trust in big data.

Using AI does not mean companies can do without data scientists. However, depending on the technology allows them to reduce the uncertainty that may otherwise exist. It also prevents employees who work with a company’s data from being asked to recheck the findings, even if they initially took appropriate precautions to ensure accuracy.

3. To improve data transfer speeds

Fast data transfers help AI technology work. Concerning some information-intensive applications like virtual reality (VR), any slow transmissions greatly interfere with the realism, and content immersion people should enjoy after strapping on a VR headset. As it turns out, AI can improve data transfer speeds, too.

For example, services exist that boost speeds across any wide-area network (WAN). Users enjoy consistently accelerated rates regardless of the kind of information transferred. Some companies have solutions that can reduce WAN job times by up to 98%. These AI-driven options work particularly well when companies need to move information between data centres or cloud environments.

What is artificial intelligence? Defining it in business — a CTO guide

In this guide, seven CTOs and AI experts provide their view on what is artificial intelligence; and how they define the technology in the context of business. Read here

4. To assist the IT team with identifying genuine cyberthreats and anomalies

One of the ongoing challenges faced by IT teams of all sizes is to separate the true cyber threats from false alarms. The difficulties associated with categorising the two types may mean cybersecurity professionals waste time getting to the bottom of things that are ultimately nonissues. They might miss the actual threats that could derail a company’s operations.

Besides detecting possible intrusions associated with a network, AI can screen for software abnormalities that may make it easier for cybercriminals to orchestrate their attacks successfully. It can also find malicious software hackers installed. Due to this kind of information and the advantages of receiving it through real-time updates, IT security teams can work more productively. They can use the majority of their resources on the threats that matter most to the company’s stability.

Some organisations have even used AI to help them conquer the substantial skills shortage in the cybersecurity industry. At Texas A&M University, the Security Operations Center deals with about a million attempted hacks each month. The facility has some full-time workers, but students comprise most of the staff. They work alongside AI that aids in threat monitoring, detection and remediation.

Before students see possible threats, the smart technology finds and groups them. This approach saves time and lets the team get to work investigating the problems and deciding how to handle them.

5. To streamline the time-to-hire metric when filling new positions

Statistics show the average time required to hire a person for an open position ranges from 12.7 to 49 days, depending on the industry. The timing also varies based on the type of work a job requires. For example, it takes a shorter amount of time overall to find someone for an administrative or human resources position than one associated with a creative or advertising role. Then, of course, interviews are more extensive for high-profile work.

Human resources professionals increasingly use AI to cut down on the time between first posting a job and finding the ideal individual to hire. For example, an AI platform could look for particular desired keywords in submitted resumes, saving hiring managers from poring over the documents themselves. AI can also pitch in during interviews. A company called VCV recently raised $1.7m to further develop its AI tool that has voice and facial recognition components. Candidates are asked to record videos of them answering interview questions, but they can’t prepare for the specific content in advance.

How will artificial intelligence impact UK consumers lives?

A report from Accenture, which was released yesterday, has found that rapid advancements in technologies – including AI– are enabling companies to not just create innovative products and services, but change the way people work and live. Read here

Then, VCV’s tool assesses the interview based on several characteristics. It could see how nervous a person seemed or if they displayed certain mood or behaviour patterns that could indicate they fit the company’s culture. The startup behind this product says its recruiting technology saves companies more than 20 hours of work.

Human resources teams still have to remember that technology can make mistakes like people. Amazon halted development on an AI recruitment tool after realising it showed bias. That’s why users cannot assume any AI tool is foolproof, but they can use it to speed up the parts of the hiring workflow that often take the most time.

AI Can Boost Efficiency at All Types of Companies

The examples here highlight why so many company leaders conclude that if they use AI, they could cut down on inefficiencies. Getting the best results from AI means looking at where bottlenecks exist, then figuring out if and how it might remove or minimise them.


Related articles

Gartner: debunking five artificial intelligence misconceptions

AI: A new route for cyber-attacks or a way to prevent them?

Enterprise AI adoption hampered by lack of skilled experts, says survey

Understanding the viability of blockchain in supply chain management

Driving business value with responsible AI

Emerging technologies, are they set to transform business?

UK tech sector leads Europe in AI — but what about the rest of the world?

EU artificial intelligence guidelines will help unlock potential of AI technology


 

Latest news

divider
Cloud & Edge Computing
Edge computing: a game changer for service providers?

Edge computing: a game changer for service providers?

21 October 2019 / Today’s centralised cloud computing architectures mean unprecedented speed, scale and elasticity are at our fingertips. [...]

divider
Automation
What UiPath’s most recent acquisitions mean for its evolution

What UiPath’s most recent acquisitions mean for its evolution

17 October 2019 / Over the past few years, UiPath has been on a trajectory of rapid growth. In [...]

divider
Automation
Why RPA? Blue Prism chairperson tells Information Age why he thinks RPA is a game changer

Why RPA? Blue Prism chairperson tells Information Age why he thinks RPA is a game changer

17 October 2019 / Why RPA? For that matter, what is RPA — robotics process automation? Jason Kingdon, a [...]

divider
Cloud & Edge Computing
How edge computing will benefit from 5G technology

How edge computing will benefit from 5G technology

17 October 2019 / 5G will help edge computing grow Analysts believe that the arrival of 5G will result [...]

divider
AI & Machine Learning
How to put machine learning models into production

How to put machine learning models into production

16 October 2019 / Machine learning is a race. Those companies that can put machine learning models into production, [...]

divider
Cybersecurity
Why your organisation should deploy threat hunting teams

Why your organisation should deploy threat hunting teams

16 October 2019 / Deploying threat hunting teams, what does it take and does it matter? Increasingly, organisations (approximately [...]

divider
Data Analytics & Data Science
Moving from passive to active analytics for data innovation: the use cases

Moving from passive to active analytics for data innovation: the use cases

15 October 2019 / Moving from passive analytics — looking at insights after the fact — to active analytics [...]

divider
Business Skills
How to approach regulatory change management in financial services

How to approach regulatory change management in financial services

15 October 2019 / Regulatory change management is a component of governance, risk and compliance. In its simplest definition, [...]

divider
Automation
UK financial services firms trailblazing on automation efforts

UK financial services firms trailblazing on automation efforts

14 October 2019 / Financial services firms in the UK are integrating robo-advisers into their operations quicker than their [...]

Do NOT follow this link or you will be banned from the site!

Pin It on Pinterest