“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.

Decision-Making

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

• Forecasting
• Recommendations
• 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.

Recommendation System

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.

Opinion Mining

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.

Augmented Analytics

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.

Conclusion

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.

Also Read, Inspiring AI Techs that Transformed Digital World

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Instead of paying for a person to do all of this for you, you can download a free personal assistant for your computer or mobile phone. These AI-based pieces of software have come a long way, and, soon, it will be hard to imagine our lives without them.

The guys and gals at TechJury, have made an interesting infographic covering the revolution in the AI industry. However, in this article, we will focus on the top 5 artificial intelligent personal assistants in the world today. Read on!

Siri

Siri is Apple’s personal assistant tool that was launched in 2011. By using voice recognition, Siri can answer all of your questions and send you notifications on the stuff you’re interested in. You can ask it to play music and even add your favourite songs to the playlist. Of course, it can do much more, and it constantly goes through updates.

Every Apple device is compatible with Siri, from your iPhone and iPad to the Apple TV device.

Alexa

A little over four years ago, Amazon launched its own AI virtual assistant called Alexa. Apart from having voice recognition and the ability to make to-do lists, set alarms, and provide information of all kinds, Alexa can also control other smart devices that you might have in the house.

Alexa is available on all Amazon devices, but it can also be used on your Android and iOS gadgets.

Cortana

Microsoft also has its own personal voice assistant. Cortana was first released in 2014, and it’s available on all the latest Windows operating systems, including a bunch of other devices.

Cortana is currently available in 8 worldwide languages, and a few more are expected to be added soon. Apart from chatting with it, you can use this AI-based assistant to search the web, keep track of your orders, and more.

This personal assistant can work on Xbox as well, and, since January of 2018, it is available for iOS and Android.

Google Assistant

Keeping in line with its competition, Google Assistant has gained a lot of attention since it was released in 2016. It is ideal for connecting to your smart home, as it can trigger cleaning devices, thermostats, and more. Google Assistant can also help you with navigation while you’re in your car.

You can find this personal assistant on Google Play, but it’s also available for iOS devices via the iTunes store.

Bixby

The lesser-known of all of these devices, the Bixby personal assistant was developed by Samsung in 2017. It recognizes seven different languages and it has some really cool features for your Samsung phone.

By using your phone’s camera, Bixby Visual can tell you at which landmark you are at, what’s the price of the product you just took a picture of, translate text, and more. Of course, it also has a voice system, and it can be connected to your smart home gadgets.

Bixby is currently available only for Android devices.

Also, Read about the AI Revolution Infographic compiled by Nick Galov at TechJury

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Coming to data science we need to give a solution for complex business problems. Data Science is a multifaceted area of work that includes processes to collect, prepare, analyze, visualize and model data that generate useful knowledge to understand complex problems and help in decision making.

These data are often unstructured and miscellaneous. Many cases set data in large volumes. Depends on the complexity of the business problem it requires artificial intelligence techniques and innovative architecture to extract relevant knowledge.

The Hypothesis of AI – Data Science

 Knowledge >Planning>Operation>Outcome for perception

Data Science uses different concepts for defining the patterns to solve business problems. For instance, in the first step, i.e. Knowledge(perception), data scientist need to getting knowledge of given data to predict patterns with the help of the data. Similarly, in the next step, i.e. planning, there are two aspects:

a) Finding all possible solutions

the initial step is to determine all possible metrics which are suitable forgive the business problem. We need to solve the problem in all possible metrics. Finally, choose one of the best solutions among all solutions

b) Finding the best solution among all solutions

Generally finding a solution is very complicated without AI. AI has some predefined functions. We can call the functions when it is in need. And AI reduces time complexity to solve particular business problems.

I collected a few of the expert opinions on how AI & Data science is interlinked

Selva perumal Pragasam studied Computer Science at Ph.D. Degrees in Computer Science

The main aim of AI is to infuse intelligence to machines. This includes Computer Vision (to help agents to view the world around it), Language Processing (Speech and text processing) to help the agent to understand human text and speech and also to respond to them more naturally. Machine Learning helps the agents to learn and improve their performance just like how humans do.

All these fields together constitute Artificial Intelligence whose aim is to mimic human intelligence or in other words, to infuse intelligence to agents(robots) so that it will replace humans in every aspect.

Data Mining, Data Engineering, Data Science all use statistical learning algorithms (Machine learning) to extract intelligence from the data. For an agent’s perspective, this intelligence may help in improving its performance.

Paul King, computer science at Harvard, the tech industry Data science is the use of statistical methods to find patterns in data.

Statistical machine learning uses the same math as data science but integrates it into algorithms that get better on their own.

Artificial intelligence is the general field of “intelligent-seeming algorithms” of which machine learning is the leading frontier at the moment.

Rahul Baxi Human intelligence builds upon what we read, observe, learn, sense and experience. It’s our ability to store a large amount of data, accumulated over years and co-relating a few data points to answer a certain question, that makes us intelligent.  You, for instance, figured out quora would be a good place to get your answer.

For machines to replicate human intelligence, they’ll need to absorb a large amount of data and access certain co-relating data points at a given point of time to make an intelligent decision.

Data science helps with the “co-relation” of data, combining multiple data points to get meaningful information from a vast amount of data.  A machine, with such capabilities, will make for a good starting point for artificial intelligence.

Google’s self-driving cars gather a large amount of data by monitoring the surrounding environment through an array of sensors: infrared cameras, proximity sensors, to name a few.  The machine driving the car, co-relates information gathered from this large amount of data to make an intelligent decision.

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