The Role of Chatbots in Boosting Brand Loyalty and Experience

As social messaging apps are gaining popularity, AI-powered chatbots are one of the best ways to reach out to a broader audience. Soon, several businesses across various verticals will implement AI chatbots that will help them carry out multiple tasks, including customer service and marketing activities.

According to Gartner, by 2021, over 50% of companies will spend more on developing chatbots (intelligent conversational assistants) against traditional mobile app development.

A chatbot is an AI-based program that communicates with humans through text messages or chats. It is a virtual assistant integrated into mobile applications, websites, or instant messengers and enables better engagement with your customers.

Read more: 5 Leading NLP Platforms that Support Chatbot Development

Two common types of chatbot

There are two types of chatbots a business can use: Transactional Chatbot and Conversational chatbot.

• Transactional Chatbot is pre-designed to provide a customer with a fixed set of choices. The customer can select an appropriate option, and the chatbot will then assist them through the whole process by providing more choices till their problem is solved. A transactional chatbot is an excellent choice for banks, online food delivery, restaurants, or businesses that are able to understand and pre-define the solutions/ products that their customers generally seek.
• Conversational Chatbot is designed to understand and respond to a conversation in a more human-like manner. It is equipped with artificial intelligence and has access to knowledge databases and other contextual information.

A conversational chatbot is more suited for businesses with advanced SaaS tools and B2B companies providing enterprise solutions and online social platforms.

Read more: How to Choose the Right Chatbot for Your Business

Five leading chatbot use cases in vogue today

1. Healthcare chatbots

Hospitals, clinics, and patient treatment centers leverage chatbots for booking appointments, sending medical information to refill prescriptions, answering common questions raised by patients or their attenders, checking physicians’ availability, and so on. According to Crunchbase, VCs have invested over 800 million dollars in at least 14 known start-ups like Safedrugbot, Sensely, Cancer Chatbot, and others to own a version of a chatbot with health features. The most significant advantage is that chatbots function silently 24/7 without disrupting anyone and answering questions at any time of the day. 

Browsing through the website of a multispecialty hospital and sifting through the different specialties and doctors can be daunting. Chatbots reduce this strain and help patients book an appointment quickly based on their ailment or health condition. When a patient interacts with the chatbot, it will ask a series of questions to determine which doctor and department the patient should visit.  

Example of a healthcare chatbot

Healthily is an AI-driven chatbot that allows you to input your symptoms and get an appropriate diagnosis. Its machine learning model allows the app to give near accurate or even accurate diagnoses.

Chatbots like Healthily prevent patients from waiting in long queues or relying on phone calls to consult doctors. With the ongoing pandemic, chatbots are making patients feel less anxious about seeking medical care. Chatbots help them get assistance in real-time.

2. HR chatbots

As the traditional office spaces give way to modern workspaces that are mobile, digital, or home, HR (PeopleOps) professionals face the increasing pressure to streamline communications and send instant responses to employees. Chatbots powered by AI free people from low value, tedious, repetitive, and transactional tasks to more high-value, strategic, engaging, and creative work. The inherent value extends to providing consistency and coherence to employee communication and reducing confusion for the end-user while delivering an instant response. HRs get more time to rethink the way they engage with employees and manage their entire cycle of work, including recruitment, onboarding, managing payroll and benefits, training and skills development, performance management, and so on.

Example of an HR chatbot

AskHR is an AI-powered HR chatbot that enables employees to get answers to the most frequently asked questions. AskHR bot natively supports 50+ languages and is hence a globally popular virtual assistant. It offers voice, email, and text support to employees and helps reduce costs, enhances employee engagement, and offers analytics to derive valuable insights. 

Read more: Capitalizing on AI Chatbots Will Redefine Your Business: Here’s How

MUSA, Fingent’s AI virtual assistant

Multi Utility Assistant or MUSA is an AI-powered virtual assistant (a chatbot) integrated with Fingent Hub – Fingent’s internal employee management system. MUSA enables employees to get answers to common queries related to HR and IT DevOps processes at Fingent.

The virtual assistant helps our employees with questions related to their leaves, company policy, hardware or software issues, IT requests, and many more.   

3. E-commerce chatbots

E-commerce chatbots guide customers in their purchase decisions.

Often, customers can get confused while browsing several products online. An E-commerce chatbot helps customers obtain detailed information about the product they are looking for or even helps them land on the right product page. Chatbots also help reduce cart abandonment as they can remind customers about the items left in their cart and prompt them to update their cart or purchase the items. Timely reminders and notifications will nudge the customers to revisit their carts and make a purchase decision, thereby helping businesses generate revenue quickly. 

Example of an E-commerce chatbot

While launching its AirMax Day shoes, Nike increased its average CTR by 12.5 times and the conversions four times with the help of StyleBot- Nike’s chatbot.  

Chatbots play an essential role in providing more reliable and quicker customer support and keeping the customers up-to-date about the delivery status of their purchases.

Read more: 5 Ways Chatbots Can Transform Your Real Estate Business

4. Chatbots in banking and finance

Chatbots are a great addition to any bank or finance institute that prioritizes customer service inclined towards digital interactions.

Chatbots can help users check their account balance, transfer money to other accounts, view the history of transactions, or even locate the closest ATM. In addition, banks are currently using chatbots for marketing activities such as sending customized information about a customer’s savings, investments, etc., and notifying customers about their new products and services.

Example of a BFSI chatbot

In 2018, Bank of America introduced Erica, their AI Chatbot. It helps customers conduct simple actions such as paying bills, receiving credit report updates, view e-statements, and seek financial advice. Recently, Erica’s capabilities have been updated to enable clients to make smarter financial decisions by providing them with personalized insights. Thanks to its budgeting capabilities, Erica users grew to 12.2 million in Q1 2020 compared to 10.3 million in Q4 2019.

5. Hospitality chatbots

The hospitality industry is hugely dependent on customer service, goodwill, reviews, and references. As travelers use multiple channels (website, social media, mobile travel apps, travel aggregator portals, etc.) to look for information, travel and hospitality service providers face stiff competition in disseminating updated content across all media. They need to be available round-the-clock in answering customers or helping with bookings. Chatbots help customers make bookings, gain more information about hotel services, travel packages, and inquire about offers and deals. From check-in to several concierge services such as booking restaurants to activity reservations, chatbots can seamlessly assist customers.

Example of a hospitality chatbot

Quicktext is a popular AI-powered chatbot for hotels that automatically handles 85% of guests in 24 languages and delivers instant response to customer requests across six different channels. In addition, it serves as a messaging hub where hospitality businesses can centrally manage Live Chat, WhatsApp, Facebook Messenger, WeChat, SMS, and Booking.com communications.

Read more: Hospitality Technology Trends: Revive The Lost Glory in 2021

How can chatbots help your business?

With the rise of emerging technologies such as artificial intelligence and wearable technology, chatbots provide industries with new avenues for businesses to engage with their customers.

1. Leverage messaging platforms

While social media engages audiences, messaging platforms enable businesses to have a one-on-one conversation with their customers. So, by integrating chatbots with your messaging platform, you could eliminate the need to build a new app and save time and money.

2. Customer service

Chatbots are a great way to augment customer service. The bots are available 24x7x365, which allows them to initiate the conversation proactively and prevent customers from waiting for long.

3. Customer engagement

Intelligent chatbots allow you to have more in-depth conversations at an individual level with your audiences, freeing them of any irrelevant information. So, a well-designed chatbot can extend the conversation and make the visitor come back for a discussion or a purchase. This can go a long way in establishing your brand loyalty.

4. Cost optimization

Getting a chatbot for your business is far cheaper and faster than developing a cross-function app or hiring additional employees. Chatbots do not need breaks, get tired, or make errors. Also, they can perform repetitive tasks without complaining.

Read more: A Chatbot Story – How We Built a Comprehensive Onboarding Assistant for a Leading Research University

Develop a custom chatbot for your business application

Chatbots are being widely used across different business functions and are augmenting customer experience. With advances in technology, bots will only get more competent and open new avenues to streamline customer communications.

We software development experts build bespoke and personalized chatbots leveraging AI and machine learning that enable your business to generate leads, increase revenue, and enhance user experience. If you are planning to develop a customized chatbot that can compete with the predefined bots in the market, contact us. 

A Comparison of Top 5 NLP Platforms: Select Your Chatbot Development Platform Wisely

The surge of artificial intelligence-based applications and conversational AI have heightened the use of NLP (Natural Language Processing). Each day, businesses collect an enormous amount of structured and unstructured data from their customers and users. Such information is collected through chatbots, intelligent assistants, and so on. Analyzing these data offers businesses insights into crucial operations and enhances their decision-making. However, manually inferring insights from tons of data is a challenging endeavor. This is where NLP platforms plays a vital role. This article will help you evaluate five different NLP platforms you can consider while developing a chatbot for your business support functions. 

How Does NLP Platforms Help Businesses

Gartner and IDC report that more than 80% of the enterprise data generated today is unstructured. Natural Language Processing (NLP) technology helps us derive meaning from the vast labyrinth of online data. NLP refers to applying several computational techniques that enable us to do analysis and synthesis of natural language and speech. According to a report by Intrado GlobeNewswire, the NLP market is expected to be work USD 42.04 billion by 2026.

Chatbots are software applications that help us conduct conversations online via text or text-to-speech instead of directly communicating with a human agent.  

Chatbots provide a better service experience to customers by responding to customer queries promptly, accurately, and most importantly, like humans. AI-powered chatbots can be trained to learn from data to respond in different and diverse scenarios. 

However, picking the right chatbot platform can be a gigantic task. Here’s an analysis of five NLP platforms. Choose your chatbot development platform wisely. 

Read more: Capitalizing on AI Chatbots Will Redefine Your Business: Here’s How

1. IBM Watson Assistant

A pioneer in the chatbot market, IBM’s Watson Assistant has evolved into a holistic customer care product. As the pandemic weighed heavily on businesses, customers and employee services were challenged in unimaginable ways. IBM Watson Assistant is designed to solve customer and employee challenges, so its relevance became even more significant during this time. Using IBM Watson Assistant to create your chatbot helps in the following ways:

First contact resolution

Resolving questions quickly and successfully is one of the biggest priorities of Watson Assistant. One of the critical challenges of first contact is user engagement. If that is your concern, here is how Watson contact can help:

• Allows integration with the phone, SMS, and WhatsApp: As it can integrate with phone and SMS, you can deploy speech services with natural sounding voices created with the help of advanced AI.
• Best intent recognition accuracy: Watson Assistant helps you understand your customers’ questions without always requesting them to rephrase the question.

Open ecosystem

Watson Assistant is designed as an open ecosystem. It, therefore, allows you to connect with your existing tools, systems, and applications. Besides, it gives you the ability to orchestrate the end-to-end experience. 

Scale your assistant to increase complex use cases

Watson Assistant allows you to scale your virtual assistant beyond simplistic FAQ bots. It can be customized easily to fit the specific needs of your business. 

Use cases: Companies such as Botanalytics, Ebix, and SnapEngage use Watson Assistant.

Read more: A Chatbot Story – How We Built a Comprehensive Onboarding Assistant for a Leading Research University

2. Amazon Lex

Amazon Lex allows you to create and embed engaging chatbots into your applications, shielding you from the complexities of NLU (Natural Language Understanding) and speech recognition. Here are some of the top features of Amazon Lex. 

Quicker integration

The capabilities of Amazon Lex are simple and easy to use, allowing you to scale up from ground zero to a fully operational chatbot within a matter of minutes. Using a combination of aliases and versioning, you can roll out your conversational interfaces into multiple environments.

Cost-effective solution

Amazon Lex has no upfront costs. You can pay-as-you-go. 

Fully managed service

It provides all the necessary features to build, deploy, scale, and monitor your chatbot.

Business use cases

• Commerce chatbot: Allows you to order food
• Support chatbot: Provides automated customer support
• Enterprise chatbot: This allows you to connect to enterprise data resources
• Use cases of Companies: Lumeneo.com, Paralect, and CommonBond use Amazon Lex.

3. Rasa

Rasa platform is an open-source framework. It is leading in ML toolkits that help developers create better chatbots with minimal training data. The two major components of the Rasa stack are Rasa NLU and Rasa Core. Rasa core helps create intelligent, conversational chatbots. 

It is best for businesses that are looking to increase subscriber engagement and for those who are interested in marketing automation and personalization.

The benefits of Rasa are:

• You can deploy into your own server by keeping the components in-house
• Highly customizable
• Allows for multiple environments necessary for development, staging, and production
• It helps you send individualized newsletters to each of your subscribers
• It continues to self-learn when it interacts with people
• It allows you to understand your customers better
• It can be integrated with Facebook Messenger, Twilio, Telegram, and more

4. SpaCy

Spacy is an open-source NLP library that is designed for production usage. It helps you build real-world projects and handle large amounts of text data. It is a one-stop operation for most heavy-hitting functions for NLP. 

It is best for companies that are bootstrapping a vast production or for vendors in charge of enterprise solutions. It has proven highly useful for companies that require industry-level solutions and need enhanced language support.

Some of the benefits of SpaCy are:

• It easily allows deep data mining
• It does not weigh down the user with obscure formulas
• It can analyze text quicker than its competitors
• Enables businesses to implement strategies to interact with customers and leads
• It is capable of working with over twenty languages
• Allows you to handle NLP solutions across an international suite of languages. 
• It is a powerhouse for every deep learning algorithm with the tools you need to teach your programs human language.

5. Microsoft Azure

Microsoft Azure bot service helps you develop, deploy and host a chatbot in an uncomplicated manner. It is a managed bot-building platform with an integrated environment. It is purpose-built for bot development. 

Microsoft Azure bot services are best for enterprises and IT companies. 

• It helps run FQAs speedily and consistently reducing customer management issues
• Easy to integrate with various Azure services
• You can build, connect, test, deploy and manage intelligent bots from one place
• Easy to integrate with other chatbot software such as Jabber or Skype

Read more: How to Choose the Right Chatbot for Your Business

Why choose Fingent as your chatbot development partner?

Acquire estimates that nearly 1.4 billion people are willing to talk to chatbots. Did you know that chatbots can handle 80% of routine customer questions? These figures prove that the chatbot platform is the present and future of your business. 

However, it is important to choose a chatbot development partner carefully. We at Fingent software development experts ensure your chatbot platform is easy-to-use even for non-tech employees. A customized and fine-tuned chatbot frees people up from low value, boring, repetitive, and transactional tasks. It enables your employees to focus on high-value, strategic, engaging, and creative work. The business value also lies in that the chatbot provides consistency and coherence to your communication, reduces/ eliminates confusion for the end-user while giving an instant response. It helps your employees get things done fast as they need not worry about the low value, boring, repetitive, and transactional tasks. Thanks to chatbot development! 

We will work with you through all the stages, from requirements identification, technology selection to planning and implementation. We will continue to support you in managing the chatbot after deployment. While you zero down on the chatbot platform, choosing the right partner to help you develop the chatbot according to your specific requirements is essential. Give us a call, and let us get talking.

How is AI Facilitating Healthcare Innovation Over Years

Today Artificial Intelligence (AI) is being used to enhance and improve all spheres of our lives. Artificial Intelligence in medicine is truly life-altering. The technology is used to solve complex healthcare challenges today. AI in medicine helps interpret the data obtained by diagnosing several chronic diseases such as diabetes, cardiovascular diseases, Alzheimer’s, and cancer. Automated systems, tools, and algorithms allow healthcare professionals to minimize errors and control disease progression. 

Artificial Intelligence in medicine has considerably advanced two fields: diagnosis and clinical decision-making. The implementation of AI in medicine enables physicians to minimize intra-observer variability and inter-observer variability. It facilitates the interpretation of diagnostic results with high accuracy and speed. 

A real-life example: Chest X-rays are among the most common imaging modalities read and interpreted by radiologists in hospitals today. Despite their widespread use, the modalities are difficult to interpret due to their low resolution. The AI models developed by IBM Research Center in California can read X-rays, and their performance is at par with the resident radiologists. 

This blog explains how Artificial Intelligence in medicine has been transforming healthcare in the past and present and how it can benefit us in the future.

Read more: Innovative Ways To Leverage Patient-Generated Health Data 

How AI has transformed healthcare in the past

In the past, Artificial Intelligence focused primarily on the development of computerized machines that were capable of making inferences or decisions that only humans can make. In 1966, Shakey- “the first electronic person” was developed. It was a mobile robot capable of interpreting instructions. Unfortunately, reduced funding and lack of interest pushed Artificial Intelligence in healthcare industry into a phase popularly referred to as “AI winter.” Thankfully, that was not the end. 

How is AI transforming the present

Artificial Intelligence in medicine has crossed numerous milestones after the bleak “AI winter.” The present generation has witnessed a paradigm shift across patient treatment, clinical diagnosis, and decision-making. Armed with large amounts of data, doctors are now more capable of providing effective treatment to their patients. AI has revamped analytic methods and changed clinical decision-making techniques. 

Read more: 7 Major Impacts of Technology in Healthcare

Increased data volumes enable decision-makers to gain unparalleled insights in all stages of treatment, such as diagnosis, treatment variability, care process, and patient outcomes. According to an analysis by Accenture, Artificial Intelligence in medicine can save 150 billion dollars for the US economy by 2026! 

Here’s how AI is transforming medicine and improving patient outcomes.

1. Electronic health records

A CDC survey revealed that nearly 75% of healthcare providers trusted their EHR to improve patient care. AI can make the existing EHR system more intelligent and flexible. AI can improve data discovery and personalize treatment recommendations. 

When used with virtual medical assistants, a practitioner can retrieve information from EHRs without becoming a victim of clinical burnout. Thus, AI in EHR can improve both clinical outcomes and clinicians’ quality of life. 

2. Medical imaging diagnostics

Medical imaging developers have discovered numerous ways to use Artificial Intelligence in medicine to detect and diagnose a wide range of diseases. These developments range from automating workflows to improving processing speed. 

While AI imaging may seem expensive, it saves a vast amount of capital spent on invasive disease treatment and prolonged hospital stays by detecting the disease at an early stage. Ai also improves the accuracy of screenings for conditions by helping doctors in early diagnosis. The advent of molecular imaging allows doctors to diagnose an ailment at the cellular level leading to accurate treatment, better patient outcomes, and decreased mortality and morbidity. 

3. Virtual health assistance

Virtual health assistance enhances outcomes by cutting short hospital stays, reducing readmission rates, and improving the patient experience, especially among chronically ill patients. Combining AI with healthcare wearables helps streamline telemedicine and improve patient outcomes. 

A Virtual Health Assistant is developed to manage chronic diseases. It collects information about a patient every time he/she visits a doctor. It can be programmed to perform health screenings and send the results to the doctor. Each healthcare facility may choose to have a tailored virtual health strategy. This will help integrate appropriate healthcare platforms and technologies into the delivery model. 

4. Proactive medical care

Conventional medical treatment involved treating the patient after detecting the disease. This was called ‘reactive medical care.’ Thankfully, AI brought in a significant shift turning reactive medical care into ‘proactive medical care.’ AI-enabled proactive medical care includes studying patients’ medical history to locate high-risk markets for various diseases. Then they are monitored for any changes. Once an alarming change is detected, the application suggests medical intervention.

These apps encourage the patient to be an active participant in their personal healthcare. These can be extremely helpful or even life-saving in conditions such as palliative care, congenital heart disease, and diabetes management. Such proactive medical care enables the patient to take care of oneself’s daily routine, including emergencies.

Read more: The Application and Impact of Information Technology in Healthcare 

Future of AI in medical industry

The journey of Artificial Intelligence in the medical industry has just begun. It has significantly changed patient experience, clinicians’ practice, and pharmaceuticals. AI has found its way from our smartwatches to the supply chain. The future of AI in medicine includes everything from answering the phone to therapeutic drug and device design, making clinical diagnoses and treatment plans, and even conversing with patients. Here are two examples:

1. Drug discovery

AI solutions for the medical industry can identify new potential therapies from vast information available on existing medicines. These will help healthcare providers in redesigning treatments that target new threats such as the Coronavirus. AI can improve the efficiency and success rate of drug development. It can also accelerate the process in response to deadly disease threats. 

A report by PwC states that the healthcare and pharma sectors are experiencing 67% improvement in clinical trials and drug discovery with the help of AI. 

Listed below are a few examples of how AI handholds frontline workers in the battle against Coronavirus pandemic:

• AI-enabled contactless and wireless thermal scanning devices designed to collect and store precise temperature data of individuals. Care providers can integrate this data into healthcare platforms for further analysis. 
• AI-based computer vision programs that can be integrated into drones and CCTV cameras installed in public places to monitor the adherence of the public to COVID safety protocols. It can send real-time alerts to police and public health departments in case of safety violations.
• AI-based robots that help physicians and healthcare workers with patient screening to avoid the risk of virus exposure. It can also enable video conversations with medical experts to procure prescriptions.
• Customized AI applications that help predict COVID-19 symptoms through sample screening. AI also helps in predicting COVID risk scores.  

2. Primary care

Multiple organizations are working on ‘direct to patient solutions’ offering advice through voice or chat-based interaction. This can enable patients to receive quick, scalable access for their medical issues minimizing unnecessary trips to the healthcare facility. Healthcare providers should use AI-based direct-to-patient solutions to provide essential guidance for patients in remote geographies of the world. 

Artificial Intelligence is revolutionizing the medical industry

Artificial intelligence in medicine is changing the role of doctors and medical professionals. It is also helping patients improve personal health management. AI is here to grow and transform numerous aspects like diagnosis, decision-making, treatment plan, drug development, etc. AI can play a leading role in how the future medical industry operates, ensuring optimal patient outcomes. 

Fingent keeps a close eye on the happenings in the medical industry and has developed the necessary capabilities to enable a connected healthcare ecosystem for our clients by developing advanced, end-to-end digital solutions. 

Give us a call, and let’s discuss your digital needs.

How AI is Transforming Businesses Worldwide

Post the PC and the dot-com revolution, the world is witnessing another significant disruption- Artificial Intelligence.

Businesses that implement AI applications will have better access to data across multiple functionalities such as customer relationship management, enterprise resource management, fraud detection, finance, people operations, IT management, and other crucial segments. AI in business finds solutions to complex problems in a more human-like way and automates processes. Organizations can redirect their resources towards more creative aspects such as brainstorming, innovating, and researching.

The COVID-19 pandemic required solutions in days, not weeks or months, and business leaders needed to act quickly. AI-based techniques and advanced analytics are helping organizations augment decision making during crises like the coronavirus. While machine learning models were a great choice, developing machine learning models or advanced analytical models would take around four-eight weeks. So, the pandemic accelerated the demand for developing minimum viable AI models quickly.

Despite the many naysayers who believe robots will take over human jobs in the future, AI is already revealing itself as more of an enabler than a disruptor. Here are nine examples of artificial intelligence transforming business.

Read more: How Will Artificial Intelligence Transform The World By 2030 

1. Sales and Business Development

As lockdowns and stay at home orders continue, people are now moving from personal interactions to digital interactions such as online shopping and mobile banking. This shift has created many new and unstructured data that is hard to interpret. That’s where AI comes into the picture and helps understand what consumers feel and need.

AI-powered sales performance solutions can identify which customers are most likely to buy a company’s product or service. This model will help people in sales prioritize their customers and improve their productivity and effectiveness.

2. Demand and Supply

Most companies are interested in matching demand and supply. For instance, a steel company may have information about various factors that may influence steel demand. Typically, these demand measures depend on external data to match up with what the company’s supply chains can generate.

AI solutions help analyze these external data and ensure that the company is not producing more than you need to satisfy the demand and not leaving any request unfulfilled.

COVID-19 crisis is unprecedented, and companies have to make sure that they use data that is representative. Historical data allows you to gain insights into upcoming demand patterns and predict possible outcomes. 

3. Back-office Tasks

Companies can leverage AI-powered cognitive assistants to perform their back-office tasks such as ordering new credit cards, canceling orders, or issuing refunds. If these assistants cannot handle complex tasks, human assistants can perform those tasks. It will ensure that the team members spend their time solving challenging problems and focus on productive activities.

As long as there are structured tasks, Robotic Process Automation can take care of back-office service operations. RPA is particularly useful for automating the claims processes of banks or insurance companies. Enterprise platforms like SAP offer Intelligent RPA that combines automation and artificial intelligence to augment business process automation. 

4. Cash-flow Forecasting

As revenue systems dry up, cash flow is likely to be a severe concern for smaller businesses. However, several AI solutions can analyze data (only if representative) for cash-flow forecasting. 

Read more: 6 Ways Artificial Intelligence Is Driving Decision Making 

5. Document and Identity Verification

AI can identify and verify documents easily. For example, think of a bank that needs to verify customer data for onboarding and compliance. Human agents manually verify documents such as driving licenses or payslips and other relevant records. It is a costly and inefficient process.

AI is used to identify the type of ID document captured, perform face-matching, determine if the ID’s security features are present, and even determine if the person is physically present.

6. Travel and Transportation

The transportation industry forms an integral part of a country’s infrastructure. As many employees may have to self-isolate during the COVID-19 crisis, AI solutions can analyze the number of staff needed by a travel company to run its business in these unprecedented times. For example, a company can request AI to provide information on whether they have enough workers to staff a railroad. Here, AI can help identify demand and supply from the laborers’ standpoint. 

AI is already being used in the transportation industry to reduce traffic congestion, avoid accidents, improve passenger safety, lower carbon emissions, and reduce overall financial expenses. 

7. Healthcare

From robot-assisted surgeries to safeguarding personal records against cybercrime, Artificial Intelligence is transforming the healthcare industry like never before. The healthcare industry has suffered in terms of medical costs and inefficient processes. 

AI-enabled workflow assistants are helping doctors free up 17% of their schedule. Virtual assistants are reducing redundant hospital visits, thereby giving nurses almost 20% of their time back. Also, AI helps pharmaceutical companies research life-saving medicines in a shorter time frame and reduce costs. More importantly, AI is being used to help improve healthcare in underdeveloped nations.

Read more: 7 Major Impacts of Technology in Healthcare 

Examples of AI in healthcare:

• PathAI creates AI-powered technology for pathologists to help them analyze tissue samples and diagnose them more accurately.
• Atomwise uses AI and deep learning to improve drug discovery and to speed up the work of chemists.
• Pager is using artificial intelligence to help patients with minor pains, aches, and illnesses.

8. Finance

The financial sector relies on real-time reporting, accuracy, and processing of high volumes of quantitative data, where AI can enhance the processes. The finance industry is rapidly implementing chatbots, automation, algorithmic trading, adaptive intelligence, and machine learning into financial operations. For instance, Robo-advisor, an automated portfolio manager, was one of the biggest financial trends of 2018.

A few examples of how artificial intelligence transforms the financial industry:

• Betterment uses AI to learn about an investor and create a personalized investor profile based on their financial plans.
• Numerai is an AI-powered hedge fund that uses crowdsourced machine learning from many data scientists worldwide.

Read more: Artificial Intelligence and Machine Learning: The Cyber Security Heroes Of FinTech 

9. Social Media

With over 3.6 billion active profiles and about $45 billion in annual revenue, social media is invariably in the battle to personalize and provide a better experience for users. 

AI can organize massive amounts of data, recognize images, predict shifts in culture, and introduce chatbots. The technology has the potential to make or break the future of the social media industry.

Similarly, machine learning enables social media to identify fake news, hate speeches, and other anti-social activities in real-time.

Final thoughts

With the advancement in technologies, AI is improving possibilities taking businesses to the next level. These examples of artificial intelligence prove that artificial intelligence can transform business models if deployed correctly. 

Case Study: Development of AI-enabled chatbots and teaching assistants – How Fingent helped a leading university to create an Automated Intelligence-driven ecosystem

Fingent top custom software development company helps you leverage AI to drive the smart reinvention of your business workflows, processes, and technology. If you are looking to develop an intelligent infrastructure for your business or improve the security process or enhance the customer experience, contact us today! 

Manufacturing technologies set to hold the reins

From big data analytics to advanced robotics to computer vision in warehouses, manufacturing technologies bring unprecedented transformation. Many manufacturers are already leveraging sophisticated technologies for manufacturing such as the internet of things(IoT), 3D printing, Artificial Intelligence, etc., to improve operations’ speed, reduce human intervention, and minimize errors.

As 2024 rapidly approaches, manufacturers will have to move away from Industry 4.0 and embrace Industry 5.0. The latter is all about connecting humans and machines (smart systems). Interestingly, Industry 5.0 may already be here. The ongoing COVID-19 pandemic only accelerates its arrival.

Read more: Digital Transformation in Manufacturing

Here are the top 10 technologies that positively impact the manufacturing industry.

1. Robotics

With advances in robotics technology, robots are more likely to become cheaper, smarter, and more efficient. Robots can be used for numerous manufacturing roles and can help automate repetitive tasks, enhance accuracy, reduce errors, and help manufacturers focus on more productive areas.

Benefits of Using Robotics in Manufacturing:

• They improve efficiency right from handling raw material to finished product packing
• You can program robots to work 24/7, which is excellent for continuous production
• Robots and their equipment are highly flexible and can be customized to perform complex jobs
• They are highly cost-effective even for small manufacturing units

Collaborative assembly, painting, and sealing, inspection, welding, drilling, and fastening are a few examples of the jobs done by robots. Today, robots work in several industries, including rubber and plastic processing, semiconductor manufacturing, and research. While they are mainly used in high-volume production, robots make their presence felt in small to medium-sized organizations.

Read more: What Are Cobots and How Can They Benefit Industries? 

2. Nanotechnology

Nanotechnology has grown to a great extent in the last few years. It involves the manipulation of nanoscopic materials and technology. Though its widespread use is relatively new, it will be indispensable to every manufacturing industry soon. Further research and experimental designs suggest that nanotechnology can be highly effective in the manufacturing industry.

Applications of Nanotechnology in Manufacturing: 

• Create stable and effective lubricants that are useful in many industrial applications
• Car manufacturing
• Tire manufacturers are using polymer nanocomposites in high-end tires to improve their durability and make them wear resistance
• Nanomachines, though not used widely in manufacturing now, are, for the most part, future-tech

3. 3D Printing

Post its tremendous success in the product design field, 3D printing is set to take the manufacturing world by storm. The 3D printing industry was worth USD 13.7 billion in 2019 and is projected to reach USD 63.46 billion by 2025. Also known as additive manufacturing, 3D Printing is a production technology that is innovative, faster, and agile.

Benefits of Using 3D Printing in Manufacturing:

• Reduces design to production times significantly
• Offers greater flexibility in production
• Reduces manufacturing lead times drastically
• Simplifies production of individual and small-lot products from machine parts to prototypes
• Minimizes waste
• Highly cost-effective

Major car manufacturers use 3D printing to produce gear sticks and safety gloves.

Read more: 3D Printing: Fueling the Next Industrial Revolution 

4. The Internet of Things (IoT)

IoT in manufacturing employs a network of sensors to collect essential production data and turn it into valuable insights that throw light into manufacturing operational efficiency using cloud software. This connectivity had brought machines and humans closer together than ever before and led to better communication, faster response times, and greater efficiency.

Benefits of Using IoT in Manufacturing

• Internet of Things (IoT) reduces operational costs and creates new sources of revenue
• Faster and more efficient manufacturing and supply chain operations ensure a shorter time-to-market. For instance, Harley- Davidson leveraged IoT in its manufacturing facility and managed to reduce the time taken to produce a motorbike from 21 hours to six hours.
• IoT facilitates mass customization by providing real-time data essential for forecasting, shop floor scheduling, and routing.
• When paired with wearable devices, IoT allows monitoring workers’ health and risky activities and making workplaces safer.

The ongoing pandemic has expanded the focus on IoT due to its predictive maintenance and remote monitoring capabilities. Social distancing makes it difficult for field service technicians to show up on short notices. IoT-enabled devices allow manufacturers to monitor equipment’s performance from a distance and identify any potential risks even before a malfunction occurs. Additionally, IoT has enabled technicians to understand a problem at hand and come up with solutions even before arriving at the job site so that they can get in and get out faster.   

Read more: Upcoming IoT trends that can shape the business landscape

5. Cloud Computing

After making its presence felt in other industries, cloud computing is now causing ripples in manufacturing. From how a plant operates, integrating to supply chains, designing and making products to how your customers use the products, cloud computing is transforming virtually every facet of manufacturing. It is helping manufacturers reduce costs, innovate, and increase competitiveness.

IoT helps improve connectivity within a single plant, while cloud computing improves connectivity across various plants. It allows organizations across the globe to share data within seconds and reduce both costs and production times. The shared data also helps improve the product quality and reliability between plants.

Read more: Why It’s Time to Embrace Cloud and Mobility Trends To Recession-Proof Your Business? 

6. Big Data

The manufacturing industry is complicated in terms of the variety and depth of the product. As far as opening new factories in new locations and transferring production to other countries is concerned, companies can leverage big data to tackle it. 

As the process of capturing and storing data is changing, new standards in sharing, updating, transferring, searching, querying, visualizing, and information privacy are arising. Think of manufacturing software like MES, ERP, CMMS, manufacturing analytics, etc. When integrated with big data, these can help find patterns and solve any problems. 

Benefits of Using Big Data:

• Improve manufacturing
• Ensure better quality assurance
• Customize product design
• Manage supply chain
• Identify any potential risk

Explore our use case: Adding New Dimensions to Equipment Maintenance with IIoT, AR, and Big Data

7. Augmented Reality

In manufacturing, we can use AR to identify unsafe working conditions, measure various changes, and even envision a finished product. Augmented Reality can help a worker view a piece of equipment and see its running temperature, revealing that it is hot and unsafe to touch with bare hands. An employee can know what’s happening around them, like what machinery is breaking down, a co-worker’s location, or even a factory’s restricted sites. Simply put, AR applications can help inexperienced employees to be informed, trained, and protected at all times without wasting significant resources.

AR has made it possible for technicians to provide remote assistance by sending customers AR and VR enabled devices and helping them with basic troubleshooting and repairs during the COVID-19 crisis. Also, more and more customers are open to allowing manufacturers to implement AR with the long-term goal of creating permanent solutions. After all, it helps both the customers and field technicians by reducing the risk of exposure. 

Read more: How Augmented Reality Can Simplify Equipment Maintenance 

8. 5G 

5G will have a tremendous impact on the manufacturing industry. It will be more transformational for devices that drive automated industrial processes.

The amazing low-latency and connectivity of 5G will power sensors on industrial machines. It will help generate a lot of data that will open new avenues of cost savings and efficiency when combined with machine learning. Currently, China and South Korea are leveraging 5G this way. Soon the US and the UK are expected to compete with them.

Read more: From Remote Work to Virtual Work, 5G is Reinventing the Way We Work 

9. Artificial Intelligence(AI)

Manufacturers are already employing automation on the plant floor and in the front office. In the future, AI-powered demand planning and forecasting will continue to develop that will help manufacturers align their supply chain with demand projections to get data that were not possible previously.

A study from IFS shows that 40% of manufacturers plan to implement AI for inventory planning and logistics and 36% for production scheduling and customer relationship management. 60% of the respondents are said to focus on productivity improvements with these investments.

Read more: The Future of Artificial Intelligence – A Game Changer for Industries

10. Cybersecurity

Moving manufacturing operations to the cloud and building and integrating systems using IoT will equally create opportunities and challenges. In an increasingly insecure digital era, there is a pressing need for heightened security. 

Manufacturing experts are investing in secure cloud-based ERP like SAP and Odoo to resolve the security challenges. Enterprises-big or small- will soon increase their dependence on cloud-based ERP systems to address security glitches and save costs by paying for usage. 

Read more: Top 6 Reasons Why You Should Move to a Cloud-Hosted ERP 

White Paper: What difference does RPA bring to your business? How can you embrace this disruptive technology to remain competitive? Download to learn more! 

Conclusion

Technologies for manufacturing will decrease labor costs, improve efficiency, and reduce waste, making future factories cheaper and more environment-friendly. Additionally, improved quality control will ensure superior products that will benefit both the consumers and the manufacturers.

COVID-19 has changed the way the manufacturing industry operates. If your business wants to remain competitive, you will have to embrace manufacturing technologies to shape your company’s future. To know more about the forward-thinking strategies that integrate the latest trends and technologies, please connect with us today.

How is AI Transforming Various Industry Sectors?

From Siri to self-driving cars, Artificial Intelligence has been breaking into new realms, including industries that are late to adopt technology or that heavily rely on manual labor. Gartner predicts that by 2020, AI will produce more jobs than it displaces. By 2022, one in five workers engaged in mostly non-routine tasks will rely on AI to do a job.

The future of AI only looks bright! 

According to experts, AI and the future of work will amplify human efficiency and productivity. AI may match or even surpass human intelligence and capabilities on tasks related to pattern recognition, complex decision making, sophisticated analytics, language translation, reasoning and learning, and speech recognition. 

This article discusses how five major industries are benefiting from AI and its innovations.

1. Healthcare

No surprises here. AI in healthcare always comes first on the list. From doctors, surgeons, nurses to desk receptionists at clinics, Artificial Intelligence is enabling process automation across the healthcare community and ecosystem.

AI tools enable medical professionals to diagnose symptoms, identify trends, analyze data or information that would predispose a person to a particular disease.

AI-powered bots assist surgeons with heart, thoracic, and colorectal surgeries. Using bots for surgeries helps lower the risk of infection and blood loss, reduce pain, ensure higher accuracy, shorten hospital stays, and expedite recovery. Digitized health records (EHRs) help patients access their information on a shared online health portal.

Along with other technologies, Artificial Intelligence is being widely used in the ongoing fight against the COVID-19 pandemic. Remote patient monitoring using AI-powered medical equipment or devices help doctors maintain a safe distance from the patients, while offering treatment. The massive amounts of data generated every second in the field of medicine can be utilized effectively to continuously train AI systems through which these systems acquire the capabilities to generate insights that can aid medical researchers.   

Read more: How Emerging Technology is Transforming the Healthcare Industry? 

The future of AI in healthcare could include everything from answering the phone to interpreting radiology images, and designing therapeutic drugs.

2. Manufacturing

AI plays a key role in helping achieve better productivity, efficiency, and visibility across manufacturing operations. AI systems can transform the way organizations run their production lines, enhance human capabilities, garner real-time insights, and facilitate the design and product innovation.

Read more: Digital Transformation in Manufacturing 

Following are some of the ways by which AI impacts the manufacturing sector:

• AI systems help monitor every stage of the production cycle and machine learning algorithms can be used to predict the fill rate, thereby optimizing the manufacturing processes and production planning.
• Small, lightweight “cobots” help create safer working environments. Manufacturers can adopt robotics to perform dangerous jobs, thus sparing their employees for crucial tasks, thereby avoiding occupational health hazards. Cobots are considerably less expensive and easy to program than the usual industrial robots. Soon, machine learning algorithms can improve their capabilities and help the cobots take instructions from humans and interact with them in a better way.

Read more: What Are Cobots and How Can They Benefit Industries? 

• Predictive maintenance helps companies understand when machines need to be attended and serviced. Using machine learning, predictive maintenance can generate valuable data that helps prevent unplanned downtime. Sensors and advanced analytics in manufacturing equipment allow manufacturers to respond to alerts and resolve machine issues on time.
• Engineers or designers can input design goals and other parameters into generative design software (a program that generates several outputs to meet specific criteria) to explore better designs. Using machine learning, designers can learn from each iteration and understand what works and what does not.

3. Finance

According to a report by Business Insider Intelligence, about 75% of bank respondents with assets worth over $100 billion said that they are using AI technologies compared to the 46% of banks with assets less than $100 billion. 

As much as $199 bn is saved for the front office and $217 bn for the middle office. AI technologies in banking can help generate over $250 billion in value. Considering the significant savings opportunities, more and more companies are implementing AI. Simply put, AI helps financial services companies mitigate risk, reduce overheads, and generate more revenue.

4. Education

Thanks to the numerous AI applications, the academic world is becoming more personalized. Today, a student can access study materials easily through computers and smart devices. AI helps automate administrative chores and minimizes the time required to complete complex tasks thereby allowing teachers to spend more time with each student. 

Teachers can assess both multiple choice tests as well as written responses easily. Robots are helping create smart content such as video lectures and simulations as well as digitized textbooks that can be customized to the learning requirements. Along with the learning aids, these digitized interfaces help students of all academic ages and grades. 

There is also a rising interest towards smart campus initiatives. A smart campus is a physical or digital set-up in which humans and technology- based systems come together to create and deliver automated experiences across higher education institutions.

AI is eliminating the boundaries of learning regardless of the physical locations. Today, students can learn any course from anywhere across the globe, at any time. AI-powered education helps nurture the fundamental IT skills of students and soon, there will be a wide range of highly interactive and personalized courses available online.

5. Retail

The retail and e-commerce industry has huge volumes of customer information, sales forecasting, stock and inventory to be tracked. Artificial Intelligence helps simplify data management to a large extent. For example, while searching for a product on an e-commerce application, AI recommends similar items according to your budget, color preference, purchase history, browsing data, online behavior, etc. 

Cart abandonment is a common issue in the e-commerce industry which occurs when a customer adds items to their shopping cart but does not purchase them. With the help of chatbots and predictive analysts, the likelihood of cart abandonment can be reduced. Chatbots can remind your customer of the items left in their cart before they choose to navigate away.

Previously, people had to rely on the FAQ section of the website to get their questions answered. However, this included unchangeable questions and static answers and most customers were not satisfied with the answers. Today, however, it is changing. A chatbot agent can respond to questions using Natural Language Processing or NLP in a much better way and ensure that potential customers don’t abandon your website. Integrating voice search features into e-commerce applications helps offer a seamless digital customer experience. 

Read more: How Will Artificial Intelligence Transform The World By 2030 

Final Thoughts

Artificial Intelligence and machine learning together are promising to help transform every industry by guiding, organizing, and automating work. AI is definitely here to stay! At Fingent top custom software development company, we have the expertise to help businesses of all sizes including startups as well as established enterprises to gain an edge over competitors. 

From suggesting products or providing basic customer service or running software tests, developing apps, and completing extensive problem-solving procedures for industries, we use AI technologies such as machine learning, natural language processing, and business rules that will provide you with optimal results. Get in touch with us to learn more. 

The Growing Application of AI in Insurance Leads to a Radical Transformation

• Introduction
• Why does the insurance industry require AI now?
• What are the benefits of AI in the insurance industry?
• Top 3 primary use cases for AI in the insurance industry
• Must-have AI technologies for the insurance industry
• Are you ready to ride on the wave of AI?

How Time Series Analysis Enables Businesses to Improve Their Decision Making

• Introduction
• Definition of Time Series
• The 5 Most Effective Time Series Methods for Business Development
• Time Series Regression
• Time Series Analysis in Python
• Time Series in Relation to R
• Time Series Data Analysis
• Deep Learning for Time Series
• Benefits of Using Deep Learning to Analyze Your Time Series
• Time Series is Valuable for Business Development

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Understanding the concept and significance of Deep Reinforcement Learning

The field of reinforcement learning has exploded in recent years with the success of supervised deep learning continuing to pile up. People are now using deep neural nets to learn how to use intelligent behavior in complex dynamic environments. Deep reinforcement learning is one of the most exciting fields in artificial intelligence where we combine the power of deep neural networks to comprehend the world with the ability to act on that understanding.    

In deep learning, we take samples of data and supervise the way we compress and code the data representation in a manner that you can reason about. Deep reinforcement learning is when we take this power and apply it to a world where sequential decisions are to be made. 

We use deep reinforcement learning to solve tasks where an agent or an intelligent system has to make a sequence of decisions that directly affect the world around the agent. While trial-and-error is the fundamental process by which reinforcement learning agents learn, they do use neural networks to represent the world.  

Read more: Key Differences Between Machine Learning And Deep Learning Algorithms

Types of learning

All types of machine learning– supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning are supervised by a loss function. Even in unsupervised learning, there is some kind of human intervention required to determine and provide inputs on what is good or bad. Only the cost of human labor required to obtain this supervision is low. Thus, the challenges and the exciting opportunities of reinforcement learning lie in how we get that supervision in the most efficient way possible.  

In supervised learning, you take a bunch of data samples and use them to learn patterns to interpret similar samples in the future. However, in reinforcement learning, you teach an agent through experience. So the essential design step in reinforcement learning is to provide the environment in which the agent has to experience and gain rewards. In other words, a designer has to design not only the algorithm but also the environment where the agent is trying to solve a task. 

The most difficult element in reinforcement learning is the reward – good vs bad. For example, when a baby learns to walk, success is the ability to walk across the room and failure is the inability to do so. Simple! Well, this is reinforcement learning in humans. How we learn from so few examples through trial-and-error is a mystery. It could be the hardware – 230 million years of bipedal movement data that is genetically encoded in us or it could be the ability to learn quickly through the few minutes or hours or years of observing other humans walking. So the idea is if there was no one around to observe, we would never be able to walk. Another possible explanation is the algorithm that our brain uses to learn which has not yet been understood. 

The promise of deep learning is that it converts raw data into meaningful representations whereas the promise of deep reinforcement learning is that it builds an agent that uses this representation to achieve success in the environment.     

Deep Q learning

Q-learning is a simple and powerful algorithm that helps an agent to take action without the need for a policy. Depending on the current state, it finds the best action on a trial-and-error basis. While this works for practical purposes, once the problem size starts increasing, maintaining a Q-value table becomes infeasible considering the amount of memory and time that would be required. This is where neural networks come in. 

From a given input of action and state, a neural network approximates the Q-value function. Basically, you feed the initial state into the neural network to get the Q-value of all possible actions as the output. This neural network is called Deep Q-Network. However, DQN is not without challenges. The input and output undergo frequent changes in reinforcement learning with progress in exploration. The concepts of experience replay and target network help control these changes.

Read more: Top 10 Machine Learning Algorithms in 2020

Deep Reinforcement Learning Frameworks

Here are the three Deep Reinforcement Learning frameworks:

1. Tensorflow reinforcement learning

RL algorithms can be used to solve tasks where automation is required. However actual implementation is easier said than done. You can ease your pain by using TF-Agents, a flexible library for TensorFlow to build reinforcement learning models. TF-Agents makes it easy to use reinforced learning for TensorFlow. TF-Agents enables newbies to learn RL using Colabs, documentation, and examples as well as researchers who want to build new RL algorithms. TF-Agents is built on top of TensorFlow 2.0. It uses TF-Eagers to make development and debugging a lot easier, tf.keras to define networks and tf.function to make things faster. It is modular and extensible helping you to pick only those pieces that you need and extend them as required. It is also compatible with TensorFlow 1.14.

2. Keras reinforcement learning

Keras is a free, open-source, neural network Python library that implements modern deep reinforcement learning algorithms. Using Keras, you can easily assess and dabble with different algorithms as it works with OpenAI Gym out of the box. Keras offers APIs that are easy and consistent, thus reducing the cognitive load. These APIs can handle the building of models, defining of layers or implementation of multiple input and output models. Keras is fast to deploy, easy to learn, and supports multiple backends. 

3. PyTorch Reinforcement learning

PyTorch is an open-source machine learning library for Python based on Torch and is used for applications such as natural language processing. It consists of a low-level API that focuses on array expressions. This framework is mostly used for academic research and deep learning applications that require optimized custom expressions. The PyTorch framework has a high processing speed with complex architecture.  

All these frameworks have gained immense popularity and you can choose the one that suits your requirements.

While deep reinforcement learning holds immense potential for development in various fields, it is vital to focus on AI safety research as well. This is going to be fundamental in the coming years in order to tackle threats like autonomous weapons and mass surveillance. We should, therefore, ensure that there are no monopolies that can enforce their power with the malignant use of AI. So international laws need to keep up with the rapid progress in technology. 

We have tried to brush across the basics of deep reinforcement learning and the top 3 frameworks that are in use currently. Want to know more about this amazing technology? Reach out to us at Fingent!