“The global Artificial Intelligence market is expected to reach $190.61 by 2025.”
Modern problems require modern solutions, and modern businesses require AI-based solutions to grow their revenue and keep their customers satisfied. For sure, the business environment has never been this competitive before in history. But leveraging modern technologies that never existed before do have the potential to help you survive and thrive.
75% of executives fear losing their businesses in five years if they do not incorporate Artificial Intelligence into their business processes.
In this article, we will discuss nineteen amazing ways businesses can leverage artificial intelligence.
But before diving into the nitty-gritty, let’s first talk in broader terms. There are three main business needs that Artificial Intelligence can fulfill.
The automation of repetitive tasks cuts down on costs and sets the human staff free to focus better on revenue-generating activities.
There are a lot of administrative and financial activities that we can automate with the help of Robotic Process Automation. The word ‘Robotic’ here, does not imply the actual robots you watch in movies, but the codes on servers that just behave like humans consuming information from various systems and inputting somewhere.
You may think of RPA as the branch of Artificial Intelligence that deals with addressing the automation needs of the businesses.
They do not replace humans, but augment human efforts and help them with tasks that need no or very little creativity and intelligence.
Here are some examples:
- Updating customer files with things like an address change.
- Replacing lost credit cards or ATM cards, and updating records with new card numbers, etc.
Read legal documents and contractual documents with the help of Natural Language Processing, and extract provisions.
RPA is easy to implement and brings a high return on investment.
NASA was the first federal agency to officially implement RPA.
Analytics is one of the most important modern business needs. We need to predict what some customers are likely to buy. Based on that, customers can be targeted personally by digital ads.
It requires machine learning algorithms that can detect patterns in vast volumes of data and their meanings. This differs from traditional data analytics in three ways.
- These models get better with time.
- Machine learning models are trained on some parts of the data set.
- They are much more data-intensive and detailed.
You may think of Machine Learning as the branch of Artificial Intelligence that deals with addressing the analytics needs of the business.
Other than predicting customer behavior, machine learning algorithms also help identify credit fraud in real-time and detect insurance claims fraud. Moreover, they can read warranty data to identify safety issues in automobiles.
Deloitte’s audit practice uses cognitive insight to extract terms from contracts. This way, an audit can address a much higher proportion of documents, often 100%, without human auditors’ having to painstakingly read through them.
The third business need AI can address is customer engagement. Although it is the least used by businesses, statistics show a bright future.
Here are some examples.
- Intelligent agents that offer 24/7 customer service(Chatbots). They interact with customers in their native language and help them resolve a broad array of problems. It might be a password change request or any technical support question.
- Health care recommendation plans. They consider a patient’s health status, check on previous treatments, and create customized care plans.
- The product and service recommendation systems for retailers. They include personalization, engagement, and sales. Rich language or images are also part of that.
It is time to discuss different ways businesses can leverage AI.
Cognitive Engagement Use Cases
AI Helps Improve Recommendations for Customers
Brands can use Artificial Intelligence to analyze vast amounts of data to predict the behavior of customers, and offer them the relevant products. Hence, the ultimate personalized shopping experiences can be created to keep customers loyal.
AI Provides Chatbots To Answer Queries 24/7
Chatbots are one of the most extensively used AI applications. They allow you to deliver customer services and sales messages anytime a customer visits your website.
Chatbots can be trained to answer a few frequently asked questions and learn the different ways customers ask questions.
Facebook Messenger chatbots have gained much recognition in the past few years. They also make specific product recommendations.
37% of Americans say they have bought products after interaction with chatbots.
LEGO, a danish toy production company, has generated two times higher average order value with the help of chatbots.
Enable Voice Search On Your Website
The trend of consumers searching for products or finding the answers to their queries through voice commands is increasing. What makes this possible is Natural Language Processing, a subset of Artificial Intelligence.
You do not need to invest any heavy amount to leverage this amazing AI application. Optimize your website to cater to voice search.
The language must be direct, simple, and conversational. All information including your contact must be easily accessible. Google’s data from 2016 shows that 20% of its searches are voice searches. That is 4 years old stats, while the trend is on the rise.
‘My Starbucks Barista’ is a new feature launched by Starbucks. It uses AI to let the customers place orders using voice commands.
Better Dialogue System
With the help of machine learning algorithms, Amazon is now able to convert speech (spoken by customers) into text. This way, businesses can address issues with dialogue systems.
Cognitive Insight Use Cases
Use AI to Build Cloud-based Virtual Assistants
We are all familiar with Apple’s Siri, Amazon’s Alexa, and Google Now, the masterpieces of Artificial Intelligence. They have successfully opened the doors of human-like interaction with devices like our phones, our laptops, and even household devices.
The number of popular virtual assistants is still not impressive. However, sooner or later, businesses will realize their importance. They could reduce the workload of employees by gathering more information online.
Big Data Synthesizers
Big Data analytics is usually the first time companies get to work closely with Artificial Intelligence. AI acts as a data synthesizer by reviewing the data you currently have and trying to add context that relates to your goals.
Businesses can use data synthesizer tools to look for trends popular among their target audience. Data synthesizers are the primary use case cognitive insights, as they analyze data to identify patterns and reveal opportunities. You may think of opportunity as a gap that the potential product could fill and instantly grab attention.
Businesses need to listen to their customers and understand their sentiments, to figure out how they perceive their products.
Today customers share their opinions and feelings on social media. In the end, there is a lot of unstructured data to progress.
AI provides such sentiment analysis tools to identify the emotional tone in comments and gain fast, real-time insights from vast customer data. Of course, the foundation of sentiment analysis is on Natural Language Processing and Machine Learning. Both of them are the subsets of Artificial Intelligence.
Perhaps, the term is new for many of us. Competitive Intelligence is the process of collection and analysis of actionable information about competitors and the marketplace to form a business strategy.
Use AI-powered tools to collect competitive intelligence and understand the strategies that your competitors use. Tools are powerful enough to scroll over millions of web pages and reveal valuable business insights. Manual collections of the same information could require laborious effort and an infinite period. While the tools give you a complete digital footprint of competitors in a shorter duration. The human error element just disappears.
Online buying decisions heavily rely on customer reviews. According to a study from Dimensional Research, positive reviews influence 90% of online buying decisions. But the same research suggests that 86% are influenced by negative reviews as well.
Customer reviews are a cornerstone for trust in the online world.
Astroturfing is the practice of creating false and deceptive reviews that the customer thinks to be genuine. They believe it to be a neutral third-party review. Even Amazon is not safe from this.
AI can help combat fake reviews in two ways. First, it can ensure that the review comes from verified purchases. Second, we can use it to mark positive reviews as helpful.
Robotic Process Automation Use Cases
Businesses can use Artificial Intelligence’s immense potential to automate many of their mundane tasks that just consume a lot of time. Here are some examples of such tasks.
Employee On-Boarding & Exit
To let one candidate join the team, several people have to collaborate. You have to grant access, assign email addresses and IT equipment. With the help of robotic process automation, all procedures can be streamlined.
It can generate the template for onboarding workflow. The bots can make rule-based decisions about which documents to send where.
The exit process in most cases is as hectic as onboarding. Tasks like revoking system access, generation of exit documents require extra care and attention.
The bots can define processes to streamline procedures. They capture relevant details from exit reports and update the same in finance applications for validation. The email is also sent to all relevant departments.
It becomes difficult because of careless behaviors of employees while punching and a huge staff of organizations. RPA can facilitate by cross-checking the self-reports against the time logged in the company record. If found any inconsistency they report the HR managers.
This, perhaps, is one of the most monotonous HR processes. Chances of errors are always there. Changing tax laws further complicate the situation.
Businesses can use RPA to collect data between multiple systems. For example, between employee management, general ledger, account tracking, and accounts payable. Bots can verify the employee’s working hours recorded in systems.
The AI-based software bots generate the complete report. They just show a high number of registered hours, missing hours, overtime, or excessive usage of a timeout, if any.
Read more: Artificial Intelligence in Agriculture: Top 8 AI Use Cases
Putting It All Together
Enterprises can leverage the power of AI to manage customer relation data, engage their customers and automate and streamline their daily processes. Its immense power allows you to analyze millions of daily interactions and target your offer down to a single customer.
Apply AI today and personalize your sales cycle to engage the right prospects at the right time. Audiences are aware of it and looking for more modern shopping experiences. If you do not provide, someone else will.