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Artificial Intelligence and Intelligent Applications

Artificial Intelligence and Intelligent Applications


Technology has become the embedded component of applications and the defacto driver for growth in industries. With the advent of AI, new milestones are being achieved each day. We are moving towards an era of more and more integration, making it an indispensable mediator between systems and humans. The rapid strides taken by the mobile industry seems like an overwhelming convergence of multiple worlds. The innate ability of such systems to improve itself, strengthened by data analytics, IoT and AI have opened new frontiers. To reap the unbound merits of AI, software application vendors are integrating it into software applications.

In this blog, we will understand what exactly are these intelligent apps. What all does it require to make an intelligent app? Also, we will look into the real world applications of these intelligent apps.


What are Intelligent Applications?

So what exactly are intelligent apps? These are apps that not only know how to support key user decisions but also learn from user interactions. These apps aim to become even more relevant and valuable to these users.

In other words, intelligent apps are those that also learn and adapt and can even act on their own. Much like all of us, these apps learn and change behaviour. We are already seeing this at work. Have you noticed how e-commerce websites show you the right recommendations at the right time?

Intelligent apps are becoming a thing thanks to the strides being made in Artificial Intelligence (AI) and Machine Learning. Machine learning gives systems the ability to learn and improve from experience without being specifically programmed. There is an increase in the popularity of conversational systems and the growth of the Internet of Things. Therefore, we are seeing machine learning applied to more things in our everyday life.

Using AI algorithms, intelligent apps can study users’ behaviour and choices.  Furthermore, it can sort through this data to use the relevant information to predict your needs and act on your behalf. For example, Smart Reply enables you to quickly respond to emails by giving you auto-generated replies. Productivity apps like Microsoft Office 365 and Google’s G Suite also use AI. Chatbots such as Meziuse machine learning to study user’s behaviour and provide them with choices they would like.

Features of Intelligent Applications

1. Data-driven

Intelligent apps combine and process multiple data sources — such as IoT sensors, beacons or user interactions — and turn an enormous quantity of numbers into valuable insights.


2. Contextual and relevant

Intelligent apps make much smarter use of a device’s features to proactively deliver highly relevant information and suggestions. Users will no longer have to go to their apps. Instead, the apps will come to them.

3. Continuously adapting

Through machine learning, intelligent apps continuously adapt and improve their output.

4. Action-oriented

By anticipating user behaviours with predictive analytics, smart applications deliver personalized and actionable suggestions.

5. Omnichannel

Progressive web applications are increasingly blurring the lines between native apps and mobile web applications.


1. Health Care Benefits

We are exploring AI/ML technology for health care. It can help doctors with diagnoses and tell when patients are deteriorating so medical intervention can occur sooner before the patient needs hospitalization. It’s a win-win for the healthcare industry, saving costs for both the hospitals and patients. The precision of machine learning can also detect diseases such as cancer sooner, thus saving lives.

2. Intelligent Conversational Interfaces

We are using machine learning and AI to build intelligent conversational chatbots and voice skills. These AI-driven conversational interfaces are answering questions from frequently asked questions and answers, helping users with concierge services in hotels, and to provide information about products for shopping. Advancements in the deep neural network or deep learning are making many of these AI and ML applications possible

3. Market Prediction

We are using AI in a number of traditional places like personalization, intuitive workflows, enhanced searching and product recommendations. More recently, we started baking AI into our go-to-market operations to be first to market by predicting the future. Or should I say, by “trying” to predict the future?

4. Customer Lifetime Value Modeling

Customer lifetime value models are among the most important for eCommerce business to employ. That’s because they can be used to identify, understand, and retain your company’s most valuable customers, whether that means the biggest spenders, the most loyal advocates of your brand, or both. These models predict the future revenue that an individual customer will bring to your business in a given period. With this information, you can focus your marketing efforts to encourage these customers to interact with your brand more often and even target your acquisition spend to attract new customers that are similar to your existing MVPs.

5. Churn Modeling

Customer churn modelling can help you identify which of your customers are likely to stop engaging with your business and why. The results of a churn model can range from churn risk scores for individual customers to drivers of churn ranked by importance. These outputs are essential components of an algorithmic retention strategy because they help optimize discount offers, email campaigns, or other targeted marketing initiatives that keep your high-value customer’s buying.

6. Dynamic Pricing

Dynamic pricing, also known as demand pricing, is the practice of flexible pricing items based on factors like the level of interest of the target customer, demand at the time of purchase, or whether the customer has engaged with a marketing campaign. This requires a lot of data about how different customers’ willingness to pay for a good or service changes across a variety of situations, but companies like airlines and ride-share services have successfully implemented dynamic price optimization strategies to maximize revenue.

7. Customer Segmentation

Data scientists do not rely on intuition to separate customers into groups. They use clustering and classification algorithms to group customers into personas based on specific variations among them. These personas account for customer differences across multiple dimensions such as demographics, browsing behaviour, and affinity. Connecting these traits to patterns of purchasing behaviour allows data-savvy companies to roll out highly personalized marketing campaigns. Additionally, these campaigns are more effective at boosting sales than generalized campaigns.

8. Image Classification

Image classification uses machine learning algorithms to assign a label from a fixed set of categories to an image that’s inputted. It has a wide range of business applications including modelling 3D construction plans based on 2D designs, social media photo tagging, informing medical diagnoses, and more. Deep learning methods such as neural networks are often used for image classification because they can most effectively identify relevant features of an image in the presence of potential complications like the variation in the point of view, illumination, scale, or volume of clutter in the image.

9. Recommendation Engines

Recommendation engines are another major way machine learning proves its business value. In fact, Netflix values the recommendation engine powering its content suggestions at $1 billion per year and Amazon says its system drives a 20–35% lift in sales annually. That’s because recommendation engines sift through large quantities of data to predict how likely any given customer is to purchase an item or enjoy a piece of content and then suggest those things to the user. The result is a customer experience that encourages better engagement and reduces churn.


1. Email Filters in Gmail

Google uses AI to ensure that nearly all of the email landing in your inbox is authentic. Their filters attempt to sort emails into the following categories like primary, social, promotions, updates, forums and spam. The program helps your emails get organized so you can find your way to important communications quicker.

2. LinkedIn

AI is used to help match candidates to jobs with the hopes of creating better employee-employer matches.

On its talent blog, LinkedIn explains that they use “deeper insights into the behaviour of applicants on LinkedIn” in order to “predict not just who would apply to your job, but who would get hired…”

3. Google Predictive Searches

When you begin typing a search term and Google makes recommendations for you to choose from, that’s AI in action. Predictive searches are based on data that Google collects about you, such as your location, age, and other personal details. Using AI, the search engine attempts to guess what you might be trying to find.

4. Tesla Smart Cars

Talking about the AI, there is no better and more prominent display of this technology than what smart car and drone manufacturers are doing with it. Just a few years back, using a fully automatic car was a dream, however, now companies like Tesla have made so much progress that we already have a fleet of semi-automatic cars on the road.

5. Online Ads Network(Facebook/Microsoft/Google)

One of the biggest users of artificial intelligence is the online ad industry which uses AI to not only track user statistics but also serve us ads based on those statistics. Without AI, the online ad industry will just fail as it would show random ads to users with no connection to their preferences what so ever. AI has become so successful in determining our interests and serving us ads that the global digital ad industry has crossed 250 billion US dollars with the industry projected to cross the 300 billion mark in 2019. So next time when you are going online and seeing ads or product recommendation, know that AI is impacting your life.

6. Amazon Product Recommendations

Amazon and other online retailers use AI to gather information about your preferences and buying habits. Then, they personalize your shopping experience by suggesting new products tailored to your habits.

When you search for an item such as “Bose headsets,” the search engine also shows related items that other people have purchased when searching for the same product.

Current trends and explorations

Intelligent things are poised to be one of the important trends that have the potential for ‘disruption’ and large-scale impact across industries. According to Gartner, the future will see the utilization of AI by almost all apps and services, making these apps discreet yet useful and intelligent mediators between systems and humans. AI will be incorporated into various systems and apps in some way and is poised to become the key enabler across a variety of services and software systems. As mentioned at the Google conference, very fast, we are moving from mobile-first to AI-first world.

It won’t be an exaggeration to say that all the new applications built in the coming years will be intelligent apps. These apps use machine learning and historical as well as real-time data to make smart decisions and deliver a highly personalized experience to the users. These apps combine predictive and prescriptive analytics, customer data, product insights, and operational vision with contemporary user-focused design and application development tools to create a highly impactful experience for users.

The intelligent apps undoubtedly have the potential to change the face of work and structure at companies in the coming years. According to Gartner’s prediction, companies will increasingly use and develop intelligent apps and utilize analytics and big data to enhance their business processes and offer top class customer experiences.


As companies are charting their digital transformation initiatives, they need to add intelligent apps to their blueprint. The development of the right intelligent apps needs to consider the new growth areas, internal and external data sources, real-time data acquisition, processing, and analysis and putting the right technology to use.

Intelligent apps are undoubtedly paving the way for speedier business decisions, better business results, greater efficiency of the workforce, and long-term gains for all — they just need to be utilized right. Companies which are diving in intelligent apps now will have a considerable competitive advantage in the near future.

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