Machine learning and artificial intelligence technology will drive the future in many ways. It is one of the most disruptive ways to automate business processes today. AI is finding new applications in day to day activities in business and it is accelerating productivity for organisations. With a wide range of AI libraries and tools available at their disposal, it will be intriguing to see how companies reinvent their existing processes in the AI driven future.

While businesses are looking at context driven automation, there are more avenues opening up where AI applications can improve efficiency, productivity and even accuracy. Here’s an intriguing list of applications for AI software: 

1. OCR Techniques 

The optical character recognition techniques can be used for reading bills and receipts. When paper bills, receipts are scanned into your system, the data can be recognised and extracted using OCR. With machine learning, it becomes easier to reconcile bills and receipts automatically into the accounting system.

The bills and receipts are automatically reconciled to the profit & loss reports, balance sheets and other accounting reports. The bills and receipts can be scanned, emailed, barcoded & entered directly into the accounting ledgers. The financial data can be captured for paper invoices, bills, and other account categories. The machine learning algorithm’s accuracy improves with increasing volumes of data. It saves time, improves accuracy and prepares accounting reports in real-time. 

2. Multilingual Voice Assistant

The virtual assistants are increasingly adopted for businesses owing to the improved speech recognition softwares. The speech recognition capabilities ensure that voice commands are converted to text easily and with very high accuracy leading to optimised assistants. Whether it is business reporting, customer support interactions or creating transcripts in local languages, multilingual voice assistants are extremely handy. 

The virtual assistants use machine learning techniques to analyse data and can be integrated into application softwares. These assistants can be used for a wide range of business activities depending on the needs of the organisation. They can be used for interactive conversational flow and real time support for interpreting and learning large datasets too. 

3. Image Recognition

The image recognition algorithms are used for security of apps. The image recognition algorithms are integrated into critical business apps for security purpose. The image recognition is also used for medical imagery, detecting diseases in patients etc.

The facial recognition algorithms are built into web applications for authorisation of users. The facial recognition techniques are used in mobile devices as well. The facial recognition is done using a deep learning algorithm that compares a live image to the stored image. This helps in verifying the identity of an individual. The image recognition algorithms are also used for classifying images and objects for ecommerce stores. The facial recognition systems can be set-up easily using React or Tensor JS libraries to implement the recognition capabilities in your web apps.

4. Sentiment Analysis for Marketing


The sales and marketing has been undergoing massive changes after the Corona pandemic. Data driven marketing has never assumed more significance than now. Sentiment analysis is emerging as one of the most potent tools for marketing. The brands are looking at large data sets from social media sites like facebook, instagram and twitter etc. These are then analysed to gauge the sentiments of their customers.

The brands use sentiment analysis to provide users with recommendations as per their moods. Even B2B sales requires data analysis for the key executives. The sentiment analysis tools can predict with high accuracy the prospects that will be a good prospect for your product or service. The sentiment analysis can help organisations improve their prospecting and is integrated with CRM systems to improve their marketing campaigns.

5. Sales Recommendations

The sentiment analysis is a useful tool for targeting the right prospects. However, every organisation needs to improve its sales to grow. How does AI assist in sales? The AI systems are now capable of analysing, profiling and predicting trends for sales with a high level of accuracy. The sales recommendations tools are developed using data models that understand the target customer profiles for companies.

The target customer profiles are analysed, all the data feeds on the web are searched for carefully understanding the needs of the customers and then buyer intent is calculated. For e.g. an executive who is looking for a custom CRM system for Retail industry would be a good prospect for a CRM company that specialises in Retail CRMs. The buyer intent, online conversations and data is analysed for sales teams, they are then given a list of high probability prospects for targeting where there is buying intent for the product or service your company sells.

Cons of Machine Learning 

Machine learning and AI are dependent on the quality of the data sets. There could be cases with high probability of errors, the AI system needs to be developed with good data. It is not the amount of data, but the quality of the data that can play a role in accuracy of the system. Good learning systems are developed with a strong feedback loop. The accuracy of the AI models is dependent on the data quality. Over time, algorithms learn to discard bad data to improve the accuracy of the system. 

The impact of AI systems will be profound. The leading companies are leveraging the data driven approach to understand the patterns of its customers and proactively serving them. Brands that are driven by AI-powered algorithms and systems are gaining a competitive edge. Take the e.g. of Netflix recommendations for its customers. The way organisations personalise, interact and innovate for their customers will create a big difference to their fortunes.

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Machine learning and AI is being implemented in nearly every industry and making huge changes to the way we bank, shop, and browse. It has created endless opportunities in countless industries, combining the processing power of millions of human minds to accelerate the rate of technological evolution and ensure the world becomes that little bit more advanced with each passing year.

One of the sectors that has felt the most benefits is health care. Not only is this a vast and highly lucrative sector, but it’s also a complex and ever evolving one, and machine learning has been used to improve everything from drug development to diagnostics.

20th century science fiction writers spent an entire century dreaming of creative ways that AI and technology would destroy the human race, but if recent health care advances are anything to go by, AI could actually be our savior.

Just take a look at these surprising ways that AI and machine learning is changing the health care sector for the better.

1. New Drugs are Being Discovered

The biggest controversies and conspiracies surrounding healthcare concern the pharmaceutical sector. The tin-foil-hat brigade insists that pharmaceutical companies have the cure for cancer, but are refusing to release it because they make more from treating than curing. That’s a somewhat extreme view, but it’s not that far from the truth.

There are millions of compounds waiting to be researched and studied and the cure for cancer could be amongst them. However, it takes a lot of work and money to go from research to development, and it’s a process that will see thousands of compounds being studied and checked, after which a short list is created and more tests are performed.

Finding the next big cure is not simply a case of discovering a new substance, giving it to animals or human test subjects, and then ticking off the illnesses it cures and the problems it causes. The goal often comes first, and in trying to reach this goal, researchers have to reject a lot of compounds without really seeing what they can do.

It’s a process that can cost upwards of $2.5 billion for each new drug that makes it to market. It’s expensive, it’s wasteful, and it’s outdated. Thankfully, however, it’s also changing with help from machine learning.

Machine learning algorithms can help to analyze all those compounds before they are rejected. And the more powerful these tools get, the more accurate they become. There’s a good chance that the cure for most known diseases already exists and is waiting to be studied, and if that’s true, machine learning could help to bring those compounds to the market.

2. It’s Turning Drug Combinations Into Cures

Many experts believe that most known diseases can be cured with just a handful of existing, heavily-researched drugs. According to Daniel Cohen, the CEO of Pharnext, “everything” can be treated with just 50 drugs.

Pharnext is using machine learning to analyze diagnostic data and user reports, calculating the potential synergistic effects of specific drugs to determine what kind of combination and dose is needed.

It seems a little far-fetched, but only because everyone thinks of the next big cure as being a single compound. The truth is, many of the world’s deadliest diseases are treated using a combination of drugs, ones that work perfectly when used together but simply wouldn’t be beneficial when used individually.

This is true for chemotherapy treatment, for instance, but it’s also true for HIV treatments and countless other diseases. When you have the flu or a virus, you probably take a single capsule, one branded as a “cold and flu” tablet. But what you’re actually taking is a combination of drugs designed to alleviate the symptoms of colds and flus, a combination that can include acetaminophen for fever, guaifenesin for decongestion, codeine for coughs, and more.

It’s an idea that already works really well and with help from machine learning, it’s one that companies like Pharnext could take to another level.

3. 24/7 Doctors

Doctors are stretched to their limits, working with more patients than they can handle. If you have to cater for the needs of dozens of patients every day, giving them all comprehensive care is impossible, but AI is lending a helping hand.

Medical Virtual Assistants are devices that patients can wear to track their vital signs and feed data directly onto the patient’s medical file, which can then be accessed by the doctor.

If the doctor is worried about a patient’s heartbeat, they can give them a device that will monitor their heart every minute of the day and feed this back in real time. If there is a serious problem, they can refer them to the hospital; if everything is okay, they can dismiss them without requiring a return visit.

These devices are especially useful for doctors in rural areas, where they are more likely to have older patients who can’t always make it to the clinic. These devices can bridge the gap, provide 24/7 monitoring, and allow the doctor to setup alerts for medications, appointments, and more.

Author Bio:

Nicky Sarandrea is working in the tech and healthcare sectors. His work has included guides on everything from the likelihood of robots destroying the human race, to the benefits of HSG testing. He lives and works in Arizona.

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