AI solutions have the potential to improve business’ decision-making processes significantly. Put simply, it has moved towards becoming a basic component of enterprise applications (EAs) towards a crucial determinant of successful business strategies. According to a survey conducted by PwC.
“Over 72% of business leaders believe that using AI can enable humans to focus on meaningful tasks”
Continuing on the same lines, EAs have already begun to show a keen interest in gaining competitive advantages via AI-based enterprise apps. Online shopping and payment processing apps are most popular in the business vertical, including computerized billing systems, salesforce automation, office productivity suits, resource planning, process management, and IT compliance. Therefore, the revenue of AI-based enterprise apps is expected to grow by up to $31.2 billion by 2025.
AI can enable smarter decision-making, detect wrong decisions, and speed up the whole process of decision making. In this article, we’ll discuss some top ways AI helps to make better business decisions.
Let’s get started!
Let’s look into Enterprise AI-based apps to make better business decisions.
Making proper marketing decisions based on changing customer behaviour is mandatory these days along with customer’s needs. These days artificial intelligence helps businesses to make proper marketing decisions through real-time available data.
From browsing history to time spent on apps artificial intelligence algorithms suggest and recommend related emails to customers to get them converted.
Customer Relationship Management (CRM)
CRM and artificial intelligence are a compelling combination. With the help of artificial intelligence, businesses can identify customer needs and making complex decision AI can handle and control different factors.
A large amount of data can be processed by AI within a couple of minutes. CRM with AI gives users these things
- Natural language search
- Predictive lead scoring
AI in CRM is the main thing where AI can handle all the major analysis and make smarter decisions about customers based on available data.
“Salesforce, Zoho, SugarCRM, and Microsoft are some of the main examples of Artificial Intelligence-based CRM.”
Organizations utilizing their CRM system’s full power will likely find many useful things by integrating an AI tool; however, if businesses are struggling with CRM adoption, it can be overly complicated and unnecessary.
This mainly deals with how websites communicate with users to get maximum ROI based on gathered information from customer’s preferences and purchases.
This technology mainly recommends products or other items to users. Even though recommendation systems were initially used for music content sites, its use has expanded to various industries.
Recommendations have become the primary solution for businesses to see through the lens of customer’s experiences, behaviours, preferences, and interests. A recommendation engine provides an efficient way to offer consumers customized information and solutions.
For example, Netflix presents you with a much narrower selection of items you are likely to enjoy. Such capabilities save you time and deliver better user experiences. With this scenario, each year, Netflix achieved lower cancellation rates and saved billions of dollars.
For decades, recommender systems have been used by Goliaths like Amazon and have become profitable to other industries like finance and travel during the last few years.
The term “data mining” has already turned into the industry like anything, but have you ever heard of opinion mining? Opinion mining is also referred to as “sentiment analysis” or “emotion AI,” which is all about the use of NLP (Natural Language Processing), text analysis, computational linguistics, and biometrics to identify, extract, quantify, and subjective information systematically.
While manual mining and analysis take long hours, NLP and qualitative factors like customer feedback can be processed and used as quantifiable data. AI has helped shorten this via reliable search and analysis functions.
For instance, if a specific smartphone model witnesses a higher number of sales in any year, the manufacturers try to include features of that mobile phone to other phones and increase the sales of other models’ sales where they miss making upgrades customer feedback.
According to Gartner report, this augmented analytics uses both machine learning and artificial intelligence techniques mostly to automate the data preparation.
By having access to reliable data top business executives and decision-makers can make wise business decisions.
The vast experience and common sense are not enough to use AI at its maximum potential while building future-ready enterprise-grade applications. However, it’s all-important to hire a leading company that provides exceptional enterprise application development services and helps you stand out your business from your rivals.