How is AI poised to transform our future?
“Artificial Intelligence is the new electricity. It has the potential to transform every industry and create huge economic value”, says Chinese-English scientist and entrepreneur, Andrew Ng. The impact of artificial intelligence on our daily lives cannot be overlooked. From smartphones to ride-sharing apps, smart home devices, Google search, and Social media- there is hardly any industry or sector that is left untouched by AI.
There has been a huge surge in patenting of artificial intelligence in the last few years. PwC estimates that by 2030, AI would contribute a whopping $15.7 trillion to the global GDP. Analysis by the World Intellectual Property Organization (WIPO) states that the number of AI-related patent applications rose from 18,995 in 2013 to 55,660 in 2017. WIPO Director-General, Francis Gurry says that “We can expect a very significant number of new AI-based products, applications, and techniques that will alter our daily lives and also shape future human interaction with the machines we created”.
Industries such as healthcare, automotive, and financial services were the fastest to adopt AI.
Following are a few key domains that would be impacted most by AI in the coming years:
Related Reading: How AI Integration Helps Maximize Your Business ROI
AI will transform these areas in the coming years:
The general public would widely adopt self-driving vehicles. Apart from cars, self-driving vehicles would also include delivery trucks, autonomous delivery drones, and personal robots. Commutes may shift towards an on-demand approach like the Uber-style “cars as a service approach”. Commute-time would be viewed as a time to relax or just another way to work productively. People would live further away from their homes, reducing the need for parking space. This would change the face of modern cities.
However, enhanced connectivity, real-time tracking, traffic gauging, route calculations, peer-to-peer ride-sharing, and self-driving cars would be impossible without personal user data. This calls for the need to implement more stringent measures to secure the data and privacy of citizens.
2. Home/ service robots
Robots have already entered our homes in the past fifteen years. Recent advances in mechanical and AI technologies substantiate the increasing safety and reliability of using home robots. In the foreseeable future, we can expect special-purpose robots to deliver packages to our doors, clean offices and enhance security.
We are already familiar with the vacuum cleaning robot – Roomba, which has gained its place in millions of homes across the world. The AI capabilities of these kinds of robots are being increased rapidly with drastic improvements in the processing power and RAM capacity of low cost embedded processors. Low cost and safe robot arms are being used in research labs all over the world. Further advances enabled by deep learning will enable us to better interact with robots.
Healthcare is a promising domain for the use of AI technologies. AI-based applications have started gaining the trust of doctors, nurses, and patients. By revising the policies and other commercial regulations regarding the development and usage of such applications, AI can be used to improve health outcomes and quality of life for millions of people in the coming years. Patient monitoring, clinical decision support, remote patient monitoring, automated assists to perform surgeries, and healthcare management systems are some of the potential applications of AI in healthcare.
AI has the potential to enhance education at all levels, by providing personalization at scale. While computer learning will not replace human teachers, Massive open online courses (MOOCs) will help students learn at their own pace with techniques that work for them. AI technologies such as Natural language processing, machine learning, and crowdsourcing are giving an impetus to online learning. If these technologies can be meaningfully integrated with face-to-face learning, AI will find more applications in our classrooms.
AI has already transformed this domain to a considerable extent. AI-driven entertainment is gaining huge traction and response from the masses with overwhelming enthusiasm. AI-enabled entertainment will become more interactive, personalized and engaging by 2030. However, the extent to which technology replaces or enhances sociability is debatable. More research is required to understand how to leverage these attributes of AI for the benefit of society.
Related Reading: Building Incredible Mobile Experiences by Combining AR and AI
Concerns about AI
Advances in AI have already impacted our lives. However, you may also have heard of the dire predictions regarding AI made by some of the brightest minds such as the late scientist Stephen Hawking and Elon Musk (Tesla and SpaceX chief). Pew Research Centre surveyed some 979 technology experts to find out whether advancing AI and related technology would help or harm humanity. 63% of the respondents were hopeful of a better future in 2030. Many of them said that all would go well only if the concerned authorities paid close attention to how these tools, platforms, and networks are engineered, distributed and updated.
Following were the concerns that were mentioned most often:
- Individuals would lose control over their lives due to the use of AI
- Surveillance and data systems that favor efficiency over human betterment would be dangerous.
- AI would cause millions of people to lose their jobs leading to economic and social upheaval.
- As people continue to depend on AI, their cognitive, social and survival skills would be diminished.
- Cybercrime, cyberwarfare and the possibility of essential organizations being endangered by weaponized information would open new facets of vulnerabilities.
Overcoming the concerns
Following are a few solutions to take positive advantages of AI:
- The global population should join hands and create cohesive approaches in tackling AI’s challenges.
- The development, policies, regulation, and certification of autonomous systems should undergo essential transformations to ensure that any kind of AI development would be directed towards the common good.
- Corporate and government organizations should shift their priorities towards the global advancement of humanity rather than profits and nationalism. AI advances should be aimed at human augmentation, regardless of economic class.
Nicholas Beale rightly said, “AI done right will empower.” As artificial intelligence continues to be embedded in most human endeavors, let us make broad changes for the better. Let us be more thoughtful about how these technologies are implemented constructively.
If you would like to know more about Fingent’s development and implementation approach on AI, give us a call.
How is RPA turning into a highly sought-after technology
Robotic Process Automation or RPA is one of the fastest-growing segments in the global enterprise software category. Research analyst Gartner says that the market growth rate of RPA was a whopping 63% in 2019. With more enterprises using this innovative technology, RPA’s market value is set to reach 3 billion USD by 2022, shows a prediction by Statista. Early adopters of the RPA software are already raking in benefits as RPA streamlines workflows, automates tasks and allows human workers to focus on high-value work. RPA software appeals to organizations across the world due to its quick deployment cycle time.
How RPA helps businesses: A quick recap
Robotic Process Automation or RPA refers to software programs or ‘bots’ that are programmed to mimic human actions. An average back-office employee has to carry out lots of repetitive, time-consuming and dreary tasks such as producing reports, filling out forms, updating records and other high-volume transactions that do not require judgment or reasoning. RPA simply offers an easy way to perform these tasks more accurately and quickly.
Since RPA does not require any specialized coding knowledge, businesses have welcomed RPA into their processes with open arms. Let’s now have a look at some jaw-dropping statistics and facts about RPA.
Related Reading: How Robotic Process Automation Is Revolutionizing Industries?
Jaw-dropping statistics and facts about RPA
The statistics behind the widespread use of this technology can provide us valuable insights into how RPA is impacting the world.
- According to the National Association of Software and Services Companies (NASSCOM), organizations that implement RPA can reduce costs from 35-65% for onshore process operations and 10-30% in offshore delivery.
- McKinsey and Co. suggest that around 45% of the tasks in a business can be automated.
- In their Annual Global RPA survey, Deloitte found that 53% of the survey respondents had already started their RPA journey. Deloitte predicts that we would witness the worldwide adoption of RPA within the next two years.
- Among those surveyed, the ROI was reported at less than 12 months with an average of 20% full-time equivalent capacity provided by robots.
- The Deloitte RPA survey respondents also reported an improvement in compliance (92%), quality/accuracy(90%), productivity(86%) and a reduction in costs(59%).
- The Institute for Robotic Process Automation claims that RPA software robots cost about one-third of the price of an off-shore employee and one-fifth of the price of an onshore worker.
These compelling figures help us to see how RPA is adding value to organizations looking to operate with maximum efficiency.
- RPA cannot replace humans: One of the biggest misconceptions about RPA is that it will eat up human jobs. RPA works alongside humans to make their lives easier. RPA software carries out jobs that are repetitive and mundane. This can enable us to focus on fruitful endeavors thus improving efficiency.
- RPA will change the nature of outsourcing: RPA has disrupted the outsourcing industry. The increased efficiency and usability that comes with RPA implementation, has threatened traditional BPO relationships. Since RPA can handle more transactions without making mistakes or taking breaks, traditional outsourcing relationships have declined over the last few years. However, if BPOs embrace the benefits of RPA or any other transformative technology they’ll continue to work.
- RPA software implementation is complex: It’s true that RPA has delivered huge benefits to its users. However, many users have also found that the implementation of RPA was quite challenging. Selecting the wrong RPA is one reason that can cause the RPA project to become more complicated than it actually should. If your company doesn’t have an interconnected system that updates cloud or on-premise infrastructure, then RPA implementation can be a big challenge.
- RPA cannot improve a flawed business process: RPA automates processes but does not improve any defects in the existing processes. Due to the hype surrounding RPA, organizations view it as a solution to all their woes. While RPA does help to streamline and modernize processes that are well established, it does nothing to improve a flawed process. So before automating, it’s better to have a clearly defined business process.
- RPA cannot be used to automate all kinds of processes: RPA can be used where high volumes of repetitive transactions based on business rules are carried out. For eg: banking and financial services, insurance, healthcare, pharmaceuticals, manufacturing, travel, logistics, etc. However, if the processes involve reasoning, making decisions, taking different actions according to scenarios, then those processes will not be able to enjoy the full benefits of business automation.
- Future of RPA: RPA has advanced considerably and is the future of IT automation. RPA will be increasingly adopted in various industries such as manufacturing, oil, and gas, retail, etc. Humans will no longer perform data entry and data rekeying jobs. All such jobs would be automated. RPA would evolve to SPA (Smart Process Automation) making business processes smarter. By integrating emerging technologies such as machine learning, AI, big data, with RPA enterprises can promote new levels of productivity and efficiency.
Organizations need not scrap their legacy systems while implementing RPA. The ability of RPA software to integrate legacy systems has helped organizations to accelerate their digital transformation initiatives. They have also unlocked the value associated with past technological investments. As businesses look for new solutions to increase gains, RPA will continue to develop and gain relevance.
Related Reading: How Can Businesses Overcome The Barriers To RPA Adoption?
Have you implemented RPA in your organization? Do you have any insights to share? Do let us know!
Knowledge Representation Models in Artificial Intelligence
Knowledge representation plays a crucial role in artificial intelligence. It has to do with the ‘thinking’ of AI systems and contributes to its intelligent behavior. Knowledge Representation is a radical and new approach in AI that is changing the world. Let’s look into what it is and its applications.
Understanding Knowledge Representation and its Use
Knowledge Representation is a field of artificial intelligence that is concerned with presenting real-world information in a form that the computer can ‘understand’ and use to ‘solve’ real-life problems or ‘handle’ real-life tasks.
The ability of machines to think and act like humans such as understanding, interpreting and reasoning constitute knowledge representation. It is related to designing agents that can think and ensure that such thinking can constructively contribute to the agent’s behavior.
In simple words, knowledge representation allows machines to behave like humans by empowering an AI machine to learn from available information, experience or experts. However, it is important to choose the right type of knowledge representation if you want to ensure business success with AI.
Four Fundamental Types of Knowledge Representation
In artificial intelligence, knowledge can be represented in various ways depending on the structure of the knowledge or the perspective of the designer or even the type of internal structure used. An effective knowledge representation should be rich enough to include the knowledge required to solve the problem. It should be natural, compact and maintainable.
Related Reading: 6 Ways Artificial Intelligence Is Driving Decision Making
Here are the four fundamental types of knowledge representation techniques:
1. Logical Representation
Knowledge and logical reasoning play a huge role in artificial intelligence. However, you often require more than just general and powerful methods to ensure intelligent behavior. Formal logic is the most helpful tool in this area. It is a language with unambiguous representation guided by certain concrete rules. Knowledge representation relies heavily not so much on what logic is used but the method of logic used to understand or decode knowledge.
It allows designers to lay down certain vital communication rules to give and acquire information from agents with minimum errors in communication. Different rules of logic allow you to represent different things resulting in an efficient inference. Hence, the knowledge acquired by logical agents will be definite which means it will either be true or false.
Although working with logical representation is challenging, it forms the basis for programming languages and enables you to construct logical reasoning.
2. Semantic Network
A semantic network allows you to store knowledge in the form of a graphic network with nodes and arcs representing objects and their relationships. It could represent physical objects or concepts or even situations. A semantic network is generally used to represent data or reveal structure. It is also used to support conceptual editing and navigation.
A semantic network is simple and easy to implement and understand. It is more natural than logical representation. It allows you to categorize objects in various forms and then link those objects. It also has greater expressiveness than logic representation.
Related Reading: Understanding The Different Types Of Artificial Intelligence
3. Frame Representation
A frame is a collection of attributes and its associated values, which describes an entity in the real world. It is a record like structure consisting of slots and its values. Slots could be of varying sizes and types. These slots have names and values. Or they could have subfields named as facets. They allow you to put constraints on the frames.
There is no restraint or limit on the value of facets a slot could have, or the number of facets a slot could have or the number of slots a frame could have. Since a single frame is not very useful, building a frame system by collecting frames that are connected to each other will be more beneficial. It is flexible and can be used by various AI applications.
4. Production Rules
Production rule-based representation has many properties essential for knowledge representation. It consists of production rules, working memory, and recognize-act-cycle. It is also called condition-action rules. According to the current database, if the condition of a rule is true, the action associated with the rule is performed.
Although production rules lack precise semantics for the rules and are not always efficient, the rules lead to a higher degree of modularity. And it is the most expressive knowledge representation system.
Gain the Benefits of Knowledge Representation
Used properly, knowledge representation enables artificial intelligence systems to function with near-human intelligence, even handling tasks that require a huge amount of knowledge. The increasing use of natural language also makes it human-like in its responses. Making the right choice in the type of knowledge representation you must incorporate is crucial and will ensure that you get the best out of your artificial intelligence system. If you need help with this, we’re here. Please reach out to us.
Address Your Parking Woes With Intelligent Parking Management
Many of you might have experienced the frustration of finding a spot to park your vehicle while going for shopping in the crowded shopping plazas. In today’s rapidly growing urban centers, the first touchpoint for a business to focus on is to eliminate the frustration that accompanies the search for a parking space. The objective of this blog is to help businesses enhance their customer experience with an intelligent parking system.
How Crucial is Parking Space in Enhancing Customer Experience?
Traffic congestion has been a major problem in many cities around the world. As a result, hours are lost in search of a parking space. A commuter in Sydney spends around 156 hours annually trying to find a parking space. This definitely adds to their frustration and creates a bad experience even before customers have stepped into your store.
One in three customers will walk away from their favorite brand after just one bad experience. Hence, it’s important to ensure that your customers have convenient parking spaces while they visit your store. This can go a long way in mitigating the risk of losing your customers and saving your reputation on social media.
The role of parking facilities in a customer’s decision to visit your store is emphasized in the Google Reviews feature as well, where visitors are prompted to mention if the store has parking facilities or not. Parking is, therefore, an important aspect of your customer experience.
What is an Intelligent Parking System?
An intelligent parking system uses technology to help drivers find and navigate their way to a parking space quickly and easily. It also helps them find alternative routes when there are traffic congestions. This way, intelligent parking systems offer your customers greater convenience.
An intelligent parking system draws customers to your business by using automated parking systems, mobile apps, and street-side sensors. The advent of smart technology has given rise to complex systems that can integrate with other connected systems. This means that businesses can now have a platform that uses relevant information to improve parking management.
What adds to the appeal of these sophisticated parking management systems is that they are extremely easy to use. They just have to download the application from app store to their smartphones. The intelligent parking management system enables parking operators to maintain real-time information on parking availability in street-parking locations, vertical parking structures, underground parking areas, and more. Your customers can access all of this information through the application on their phones.
How Does the Parking Management System Work?
A parking management system depends either on vehicle movement detection or vehicle video tracking. This uses technologies based either on sensors or on cameras. Each parking area can be installed with sensors or cameras at the access and exit points. This helps in tracking incoming and outgoing vehicles.
Sensors are being successfully used at a single level, multi-level and even on-street parking spaces. When these sensors are installed at each parking spot, accurate data about specific vacant parking spots can be obtained by the customer that delivers a hassle-free parking experience.
Five Benefits of Intelligent Parking Management
- Reduced stress leads to happy customers: Driving through the same street several times trying to find a parking spot increases stress levels. But with the intelligent parking system, a customer can navigate straight to a vacant parking spot and then into your store.
- Saves customer’s time: Your customer will have more time to explore your business offerings instead of driving around your premises looking for a parking space.
- Reduced fuel expenses and increased sales: Since your customer does not have to go in circles searching for a parking space, they tend to save more on fuel expenses. This can even prompt the customer to purchase more from you.
- Lowers pollution: Reduced pollution will contribute to better health for you and your customers.
- Reduces traffic snarls: When there are fewer vehicles on the streets, moving painfully slow searching for a parking spot, congestion on the roads is reduced. This, in turn, attracts potential customers.
The benefits of an intelligent parking management system are manifold. The most crucial factor is that it contributes to enriched customer experience and consequently to an improvement in sales and customer loyalty. At Fingent, we help clients develop applications with advanced technologies such as IoT, AI, cloud, AR, and VR. Reach out to us to discuss more.
How Your Business Can Reap Profits Through AI Integration
“Is it the right time for companies to capitalize on AI?” – This question is lingering around the tech air for almost a decade now.
Fear of losing jobs to automation, soaring IT budgets, lack of adequate skills and infrastructure, inability to perch on the ideal technology partner, and many other reasons are refraining businesses from venturing boldly into artificial intelligence initiatives.
Engaging with clients across the globe, Fingent has realized that many of them believe AI to be the next thing in their business. Research suggests that next-generation enterprise IT systems that include AI components — called “future systems” — will grow revenues among leading companies by as much as 33% over the next five years. Many have adopted machine learning and other forms of AI into their core business processes.
Moving from data-driven to AI-driven digital environments is the next evolutionary phase in business. By embracing AI into their workflows in strategic ways, business leaders will transform how data adds value to the business. This will introduce new ways for humans to contribute as well.
As business leaders consider AI for their organizations, the top question is no longer “What is AI?” or “How does it work?” but “What can AI do for us?” In our effort to help our customers take advantage of AI, we have curated the pain points faced by businesses and how they can identify business capabilities and opportunities with AI in our latest white paper.
The insights you will gain by downloading our white paper:
- Understand more about AI and its broad categories
- Identify business capabilities and opportunities with AI
- Key business areas where AI brings the most value
- Steps to build a successful AI strategy for your business
- The simplest way to integrate AI into your business
- How to involve all in the AI wave to gain positive outcomes
Link to Download: How Can Your Business Use AI to Achieve Higher Profits Now?
Fingent’s technology solutions ensure that technical implementation and strategic adoption of AI into your business happen in accordance with your long-term goals. Connect with our AI experts today to start a conversation about your first AI initiative.
5 Tips To Select The Ideal Chatbot For Your Business
Chatbots have opened up a whole new realm of communication between humans and machines. They enhance a company’s customer service and improve operational efficiency, driving better engagement, reduced churn rates, and overall sales growth. They have become immensely popular and their popularity only continues to increase. It is clear that the use of chatbots is imperative for your business success.
In this blog, we will take a look at the types of chatbots available and how to wisely select the chatbot that suits your business.
The Wide Array of Bots
Studies predict that by 2021, more than half of the enterprises will increase their investments in chatbots, creation than traditional mobile app development. Customers would prefer to get real-time answers from bots on a company website.
Chatbots can do just about anything. They can help you deliver a surprise gift to someone you love. They can also help you break up with your lover and much more!
Broadly, chatbots can be classified as follows:
- Action Chatbot: In order to follow through with a specific action, this type of chatbot requests relevant data from the customer.
- Social Messaging Chatbot: It utilizes social messaging platforms and allows customers to interact with the chatbot directly on social media.
- Scripted Chatbots: It uses a predefined questionnaire to interact with the customer.
- Natural Language Processing (NLP) Chatbots: Being an application of AI, NLP enables chatbots to understand the written or spoken language and come up with the best response.
- Contextual Chatbots: It is the most brilliant of all chatbot types. Since it is based on artificial intelligence and machine learning, it can self-improve over time.
Tips To Choose A Perfect Chatbot For Your Business
As a communication agent, chatbots play a vital role in automating mundane tasks in an “always-on” work environment. Chatbots can handle day to day queries until an emotive or complex issue arises, which might require the intervention of a trained human agent to address it.
Related Reading: Capitalizing on AI Chatbots Will Redefine Your Business: Here’s How
Here are a few pointers to select the perfect chatbot for your business needs:
1. Think about your target audience
Like every business that has a target customer, the chatbot too must have a target audience. It is important to remember that the chatbot should serve as the bridge between you and your customers. The bot should be able to understand the preferences of your customers and cater to their convenience.
2. Define objectives
Identifying and narrowing the specific tasks or areas you want to automate would yield maximum benefits. There are a few points that could help your business define those objectives. Carefully consider factors such as the platform where the chatbot would be used, the queries it would answer, the queries it would direct to a human customer care executive, and how it would manage the hand-over process smoothly.
3. Define your value proposition
The value proposition involves ensuring that the most vital factor of your business, is given prime consideration. It determines whether your customers will come to you or go to your competitors. A higher value proposition might require AI or ML capabilities; so gauge and determine your value proposition to select the right chatbot that fits your budget and your business needs.
4. What is your response speed?
According to the 2018 State of Chatbots Report, customers want quick and easy answers. Customers might get frustrated if the answers are delayed. The appropriate selection of chatbots can help you avoid such kind of delays effectively. When dealing with a complex issue, ensure that your chatbot is capable of collating information quickly without delay. If there is a need to hand over the query to a human customer care agent, it should be done seamlessly and fast.
5. Evaluate features and functionalities
Evaluation aids your business in identifying the essential features and functionalities required from a chatbot to run your business successfully. To begin with, you could create a set of standards that would analyze all solutions. Decide on which features are required, such as NLP, integrations, contextual awareness, analytics, and so on. Proper documentation is required while evaluating the features. Such a candid evaluation helps a business choose the right chatbot that could be fine-tuned later or could self-learn.
Download our case study: Using chatbots to create an enhanced and engaging learning experience
Make Your Business Chatbot Ready
In the 24/7 era where customers want instant services, chatbots help businesses to keep pace with such expectations. By evaluating your own objectives and keeping in mind your customers’ expectations, your business can maximize the benefits of chatbot technology. However, choosing the right chatbot that fits your organizational needs and implementing it without any flaws require a good deal of expertise.
Our team at Fingent has been doing amazing things with Chatbots for our clients. Recently, we provided a matured chatbot assistant technology to a client, which provides comprehensive user intent identification and processing as well as satisfactory response according to the user query. Chat with us to identify the best chatbot solution for your business, and learn how we can implement it for you quickly.
What Is Robotic Process Automation?
Robotic Process Automation is the process of applying automation to perform tedious business tasks of the workforce, such as data manipulation, response triggering, transaction processing, and other redundant tasks. According to a recent study by Snaplogic, 90% of the workforce are burdened with redundant tasks. This not only reduces their productivity but also consumes significant amounts of time with which they could perform higher-value tasks.
The Role Of RPA: Features That Enhance Business Process
Once your enterprise has decided to implement RPA, it is time for you to choose the right robotic process automation solution.
Traditional RPA software bots are known to handle only a specific task at a given time. When it comes to addressing high volumes, there is a necessity to clone these bots and run them simultaneously. RPA providers usually charge users for each concurrent process. This can become a costly affair for enterprises, especially during volume spikes. Thus, undue extra costs are a key factor to consider while choosing an RPA solution for your business.
RPA works as a virtual assistant and can handle complex processes starting from performing complicated calculations, data capturing to maintaining records.
In addition to prioritized work queues, user-friendly features, data analytics, and non-disruptive nature, the following are crucial features that enhance business processes:
- Non-disruptive nature: An enterprise can easily implement RPA into their workflows without having to disrupt or change the existing structure or risks.
- Data analytics: Gathering critical data from multiple sources, analyzing and storing the data, and creating reports have brought digital transformation to businesses with RPA. This enables accurate forecasts of sales data along with other Key Performance Indicators (KPIs).
- Prioritization of Internal Work Queues: Every RPA software consists of internal work queues. These work queues are used to extract data derived from various transactions for analysis. The extracted data is then stored on a cloud server and made available for access by the bots.
- User-friendliness: Employees can operate on the robots without any extra RPA knowledge. They only need to learn how the systems work.
- Scalability: With RPA, it is possible to upscale and downscale various robotic operations.
Types Of Robotic Process Automation Tools
RPA enhances robotic performance in different ways. The three major categories include Working Robots that are commonly used for Data Collection and Project Planning. Monitoring Robots detect faults and breakdowns, whereas Screen Scraping Robots provide data migration tasks for enterprises.
Robotic Process Automation tools come in varying sizes and shapes. Analyzing your business objectives is the most critical factor before deciding to choose a specific RPA tool for your business. A few of the major RPA tools are as follows:
- Attended Or Robotic Desktop Automation Tools
This type of automation always starts with the user via the user’s desktop. The user first launches the RPA code to perform required operations rather than waiting for the workforce to perform.
- Unattended Automation Tools
This type of automation completes business processes in the background and is used mainly to perform back-end tasks.
- Hybrid Automation Tools
This type of automation combines both attended and unattended automation tools to perform start to end operations.
How To Choose The Right RPA For Your Business
A clear set of objectives form the primary goal before opting a specific RPA tool for your business. The following are the key factors you need to consider before selecting an RPA tool for your business:
1. Easy-to-use Interface
Simple user experience is a major criterion for choosing the right RPA tool for your business processes. A simple user interface will ensure all employees work efficiently.
2. Proper Deployment
An RPA tool that can be quickly deployed with the existing technology stack is what is required.
Replacing tedious tasks performed by the human workforce is largely replaced by the bots. This process of automation saves costs. Employees can focus on their core tasks and spend time and effort on their skills rather than performing redundant and tedious tasks with the help of RPA tools. Purchasing an RPA software tool involves associated costs, such as cost of individual licenses, cost of the software, and other overheads.
Implementing an effective RPA tool enhances the business processes and leads to the growth of the enterprise. This growth is accompanied by hiring more resources. Thus an RPA tool can enhance the scalability of a business in the long run.
Data analytics, compliance, and financial transactions require a highly secure environment. A great RPA software tool ensures a secure solution for all business processes and updates as well.
The architecture of the RPA depends on where you plan on employing your RPA tool. The deployment and maintenance of an RPA tool depend on factors such as layered design, component reusability, robust delivery, popular language support system, easy accessibility, and so on.
Choosing an RPA suite that consists of solid inbuilt features is critical. Flexibility, scope, availability of wizards and GUIs, other extendable commands and supports are some of the features to consider.
8. Exception Handling Support
A robust RPA solution can detect errors during automation and automatically resolve without human assistance. In other cases where human intervention is required, an effective RPA tool must be able to send error messages.
9. Extended Support
Different vendors offer different support. A dedicated support team is necessary to ensure strong maintenance and support.
To make the best decision on choosing the right RPA solution for your business and access the full potential of RPA tools, get in touch with our experts today!
Understanding The Types Of AI Systems To Better Transform Your Business
In this digital era, industries are witnessing the ability of multifaceted artificially intelligent systems performing tasks that mimic intelligent human behavior or even beyond. Artificial Intelligence today, manage large chunks of data and perform redundant tasks, allowing the human workforce to focus on core tasks. This saves cost and time and improves productivity significantly.
According to Gartner, the number of industries adopting AI has grown over 270% in the last 4 years. Technology giant, Google pledges $25 million USD in a new AI challenge named ‘AI For Social Good’. Understanding Artificial Intelligence Types, is important to get a clear picture of its potential.
Types Of Artificial Intelligence Calculation: Two Main Kinds Of AI Categorization
AI makes systems imitate human capabilities. Though AI can be classified into different types, the 2 main categories are defined as Type-1 and Type-2 and are based on AI capabilities and functionalities. Let us walk through the major classifications of AI types.
Type 1: AI-Based On Capabilities
1. Weak or Artificial Narrow Intelligence (ANI)
Weak or Narrow AI is a type of AI which performs assigned tasks using intelligence. This is the most common form of AI available in today’s industries. The Narrow AI cannot function beyond what is assigned to the system. This is because it is trained to perform only a single specific task.
ANI represents all AI machines, created and deployed till date. All artificially intelligent systems that can perform a dedicated task autonomously by making use of human-like abilities, fall under this category. As the name suggests, these machines have a narrow range of responsibilities.
Apple’s Siri, for instance, is an example for Narrow AI. Siri is trained to perform a limited pre-defined set of functions. Some other examples include self-driving cars, image and speech recognition systems.
The category of complex artificially intelligent systems that make use of deep learning and machine learning, fall under the category of Artificial Narrow Intelligence systems. These machines are categorized under the ‘Reactive’ and ‘Limited Memory’ machines, which is discussed in detail going forward in this article.
Know more about the key difference between deep learning and machine learning.
This video is made using InVideo.io
2. Artificial General Intelligence (AGI)
General Artificial Intelligence is a type of AI which can perform any intellectual tasks as humans. AGI machines are intended to perceive, learn and function entirely like humans. Additionally, the objective of devising AGI systems is to build multiple competencies which can significantly bring down the time needed to train these machines.
In a nutshell, AGI systems are machines that can replicate human multi-function capabilities. Currently, researchers around the globe are trying to design and develop such AI. Since there is no example as of now, it is termed, General AI.
3. Artificial Super Intelligence (ASI)
Artificial Super Intelligent systems can be best described as the zenith of AI research. ASI is intended not only to replicate multi-faceted human intelligence, but also possess faster memory, data processing, and analytical abilities.
This is a hypothetical concept of AI where researchers are trying to develop machines that can surpass humans. This is an outcome of General AI.
Type 2: AI-Based On Functionalities
1. Reactive Machines
The reactive machines perceive the real world directly and react according to the environment. The intelligence of Reactive Machines focuses on perceiving the real-world directly and reacting to it. An example of reactive machines is Google’s AlphaGo. AlphaGo is also a computer program that plays the board game. It involves a more sophisticated analysis method than that of DeepBlue. AlphaGo uses neural networks for evaluating game strategies.
2. Limited Memory
Limited memory machines are those that can retain memory for a short span of time. These machines have the capabilities as that of purely reactive machines. Additionally, limited memory machines can learn from previous experiences to make decisions. For instance, self-driving cars are limited memory machines that can store data such as the distance of the car with nearby cars, their recent speed, speed limit, lane markings, traffic signals, etc.
The observations from previous experiences are preprogrammed to the self-driving car’s system. This data, but is transient. That is, it is stored only for a limited period of time. This is because it is not programmed to be a part of the self-driving car’s library of experience, compared to the experience of human drivers.
Nearly every artificially intelligent system today uses limited memory technology. For instance, machines that make use of deep learning is a prime application of limited memory. These machines are trained with huge volumes of data sets which are stored in their memory as a reference model. An example of this is the AI that recognizes images. Image recognition AI is trained using a multitude of pictures along with their labels, as data sets.
Artificial intelligent systems such as chatbots and virtual assistants are also examples of limited memory machines.
3. Theory Of Mind Machines
Theory of Mind can be defined as a simulation. To be crisp, when a person considers himself in another person’s shoes, his brain tends to run simulations of the other person’s mind. Theory of mind is critical for human cognition. Additionally, it is crucial for social interaction as well. A breakdown of the theory of mind concept, for instance, can be illustrated as a case of autism.
Instead of a pre-programmed engine, AI scientists are looking forward to developing a series of neural networks. This series will be used to develop the ‘Theory Of Mind’.
‘Theory Of Mind’ machines are aimed at figuring out someone else’s intentions or goals.
4. Self-Awareness Machines
Self-Awareness machines exist hypothetically today. As the name suggests, these machines are supposed to be self-aware, like of the human brain. The machines can be described as the ultimate objective of AI scientists.
The goal of developing self-awareness machines is to make these capable of having emotions and needs as of humans.
To learn more about AI capabilities and how it can benefit your organization, drop a call to us right away and gather strategies to implement AI for positive business outcomes!
Key Features And Benefits Of An Artificially Intelligent Ecosystem For App Development
Artificial Intelligence is omnipresent in this digital era. Artificial Intelligence and human intelligence work together to deliver numerous solutions at the technological forefront. The global artificial intelligence market size is expected to reach $169,411.8 million in 2025. The leading application development trendsetters such as IBM, Microsoft, and many more global leaders have adopted Artificial Intelligence as an inevitable part of their advancements in bringing up the intelligent app ecosystem.
Artificial Intelligence has its key segments in the market based on Technology and they are Machine Learning, Speech Recognition, Image Processing, and Natural Language Processing along with various other industry verticals.
What is an AI application?
Putting it in simple words, AI is the reason why your smartphone when connected to your car’s Bluetooth gives a beep with a message about the traffic conditions and in what time you are likely to reach your destination you often travel to. Artificial Intelligence makes this possible via its pattern recognition skills which it formulates over a period of time, known as machine learning!
For instance, Microsoft has developed a productivity AI application known as ‘Office Graph and Delve’ of Microsoft Office 365. It collects data and shows only relevant content to users according to their priority. Other examples include:
- Voice recognition apps with GPS.
- Search engine applications.
- Chatbots or AI-enabled assistants.
- Personalized shopping applications that make use of Google Analytics.
- Financial applications that are both accurate and efficient in calculations and providing results, such as automated advisors, powered by AI.
- Autonomous vehicles, drones.
- Transport apps powered by AI, Google Maps, etc.
- Social Media applications.
- Smart home devices that make use of data science like smart voice assistants.
- Creative arts such as Watson Beat, yet another powerful AI implemented the app.
- Security apps that implement Facial Recognition and image processing technologies.
Making Mobile Apps Intelligent With AI
The first quarter of 2019 witnessed over 2.1 million apps available in Google’s Play store for Android users. Apple’s Appstore was the second largest to have over 1.8 million apps ready for download.
Related Reading: Read on to learn how voice app can enhance your business.
With such intense competition, app developers are looking for ways to create personalized applications. The solution to this is AI implementation. The role of AI in an intelligent app ecosystem is to make the applications learn the intention of users. Machine learning and Data analysis features form the Intelligent Ecosystem for these applications.
The AI ecosystem increases the efficiency of programs and machines and also allows users to predict user intentions online like their purchasing trends and other social behaviors when they are online. The following are some of the roles played by Artificial Intelligence in an intelligent app ecosystem:
- Multi-faceted technology that incorporates machine learning algorithms and deep learning.
- Implements Big Data, Neural Networks in its programming.
- Predicts user communication and intention online in real-time.
- Automates functions.
- Can make informed decisions for specific users.
- Can create analytics from a history of searches.
- Interprets tasks using preassigned commands.
- Provides personalization.
- Accurate Insights
- Perfection In App Development Features
- Efficient Ways to Complete Redundant Tasks
- Enhanced User Satisfaction
- Notifies Important Details making operations easier
AI integrated applications help in improving success rates and also increase app performance. Along with helping machines to mimic humans and its problem-solving capacity, AI has become an inevitable part of app development with its ability to learn, perceive and manipulate data.
AI protects an application user’s data. It performs an analysis of the data for identifying user patterns and behavior online in real-time. This data can be used to provide better customer experience and for user engagement.
Role of Artificial Intelligence is also in aiming at generating revenue through efficient user interfaces.
The Intelligent App Ecosystem: How It Functions
Applications are becoming more intelligent each day. Staying ahead of the game is crucial for app developers with users wanting a personalized experience and much-improved experiences. The following are the latest trends in AI intelligent app ecosystem:
- Wider use of machine learning techniques.
- Wider adoption of microservices for app development.
- A rapid increase of numerous platforms to develop applications.
- A rise in the adoption of various machine learning models.
Various layers of technology are involved in the development of artificially intelligent applications. Artificial Intelligence plays a major role in modeling the market dynamics of businesses to a large extent. In addition to creating new opportunities, be it for startups or even for large industrial giants, artificial intelligence provides numerous implications on the development side including the following key features:
1. Customer Experience with machine learning defines an intelligent app ecosystem
Every business needs high-quality data that will suit their specific application needs. So the machine learning models are required by user data specific companies. For instance, for Google, it was the ‘search’ function, ‘entertainment’ was defined by Netflix, Facebook for the ‘social’ amenities and many more such specific intelligent applications.
Many more categories include healthcare, personal assistants, retail, agriculture, sales, security, automation and many more industry verticals. Machine learning along with user critical data have formed the basis of these applications on the artificial intelligence vertical.
Related Reading: Here’s how you can redefine your business with AI chatbots.
2. Cross-platform services With The Intervention of New Platforms
There are many cross-platform mobile applications developed on cross-platforms such as hybrid and native platforms. Consider Facebook Messenger and Amazon’s Alexa. The added features make it more user-friendly and even beneficial on the sales front. This also becomes simpler for industries to perform when they can deliver their products/ services across such platforms as it is all about adding a new layer of API that connects with the entire existing microservices for authentication and other critical functions. These interfaces, thus revamp the existing applications into macro-services.
3. Machine Learning Models And Intermediate Services For Business-Specific Platforms
Pre-trained machine learning models can be used as plug-and-play for performing functions such as natural language processing, image processing, etc. These are the intermediate services. On the other hand, there are raw intelligence providers that form the building blocks. These two are the major 2 types of industries that provide value to the intelligent app ecosystem.
4. The Big Data Technology Analysis And The Intelligent App Ecosystem
Do you know what Hadoop has contributed to enabling an artificially intelligent ecosystem of app development? The hardware companies that store large chunks of IoT data and other transactional data perform? Well! Big Data Analysis is how businesses keep up with the intelligent app ecosystem.
According to the latest IDC forecast, 75% of the commercial enterprise applications will make use of AI by 2021. This shows that deploying AI for app development will lead your business to have an intense advantage over your competitors in the near future. To learn more about how you can capitalize AI for your business benefits, get in touch with our experts today!
How relevant are chatbots in today’s business landscape?
Apart from merely acting as conversational interfaces to deal with customer queries, do they help your business flourish with improved sales and new revenue streams? Yes, without doubt, and specifically if it comes powered by state of the art AI.
In this digital age, your valued customers are constantly on the lookout for personalized services. Structuring the experiences, chiefly customer service in a rather consistent and proactive way backed by data analytics will enable personalization on a much deeper level.
AI-powered chatbots will do that just one step smarter. Whether it’s round the clock customer support or driving engagement with advanced analytics, chatbots will minimize the role of human agents and do away with contact centers for delivering an uninterrupted customer service experience.
The outcome: better engagement, reduced churn rates, and overall sales growth.
Thereby, capitalizing on chatbots has turned out the need of the hour, especially if you wish to deliver augmented customer experiences and automate several of your critical business functions.
As a solutions provider, we did experiment with chatbots upon the request of our client . After extensive consultations, Fingent developed an AI-enabled chatbot to foster an engaging learning experience for the students.
Related Reading: 5 Ways Chatbots Can Transform Your Real Estate Business
AI Chatbots Fulfill a Broader Role in Organizations
Besides customer support and engagement, chatbots can undertake several tasks that need consideration. Its key role, however, is to serve as a replacement for tasks deemed repetitive and time-consuming. These include:
Automating Repetitive Tasks
Repetitive tasks take up time and redirect your efforts from areas that demand more attention. Deploying chatbots is one way to automate a multitude of recurring business functions within your company. In customer service, chatbots can readily take over the process of engaging basic customer inquiries round the clock and automatically redirect more complex interactions to human agents. AI bots also enable automation into your organization’s IT helpdesk to instantly provide answers based on your employee queries or FAQs. Besides, it can even offer IT support like a password reset without the need to consult with the IT team.
Our work with the client yielded a similar AI chatbot dubbed as a “virtual teaching assistant”, which allowed the students to interact, ask queries and get the relevant teaching materials, syllabus and schedules related to their curriculum 24/7. Thereby it serves as a replacement for the primitive methods utilized like forums where the instructor would need to answer a particular question several times repetitively.
Streamlining Lead Generation
As a salesperson, you may find it difficult to move customers through the sales funnel. With incoming cold leads, the process requires a coordinated approach to obtain successful conversions. You can assign chatbots to take over this process since they are more capable when it comes to creating engaging customer experiences. By analyzing the communication flow, chatbots deliver an interactive experience for active engagement, have quicker response rates, facilitate a friction-free experience and are available throughout the day.
Besides, AI chatbots rely on data leftover from previous customer interactions, which helps you better understand customer preferences and tastes, thereby helping out target future communications and recommendations that are highly effective in transforming and converting the potential cold leads into actual customers.
Gathering Details for Lead Targeting
Incoming leads never wait for your time and may bounce back if they find no signs of engagement. If you are away or the helpdesk personnel fails to respond, an alternate strategy is to collect basic details from the incoming leads to follow back at another convenient time. Gathering basic details from initial conversations like the client company size, budget or whatever the salespeople collect with their usual template from incoming leads is a routine task that might be tiring for your sales personnel.
Assigning a chatbot to do this task is a good way to make sure all the key details from incoming leads are collected and stored for later reference. Chatbots can engage with multiple leads at any time of the day and give them a consistent experience throughout, which sets the initial stage for successful conversions.
However, it does in no way imply that human intervention is to be completely left out. There is always the risk of possible mistakes made by the chatbot that will result in losing your potential leads. Certain measures like handing over the process to a human agent at the right time for taking charge over the lead conversion process should strictly be incorporated into the bot’s layout.
Substituting Contact Us Forms
Whether it be a standard contact form for business inquiries or feedback form or even surveys regarding a particular service, chatbots can take over the process and seamlessly guide the user through the process. Most websites still use forms containing multiple fields that must be manually filled with information, which is annoying to most users and thus indirectly contribute to rising bounce rates. The data obtained from contact forms are crucial for companies to create individual customer profiles for reorienting their services for the better.
AI bots, on the other hand, eliminate the need to manually enter information in the required fields by replacing it with engaging conversations. By gathering all the details concerning each customer that it engages with, an AI bot can gather data needed for the forms through initiating natural conversations. Besides, an intelligent chatbot can prolong the conversation and obtain more data from a customer as well as even try guiding them to other services/product landing pages, which is not possible through a generic form. This ultimately saves your customer’s valuable time and boosts engagement, which reflects back through increased conversions.
Chatbot Building and Integration Platforms Keeps Multiplying
It is now far easier to build and deploy chatbots thanks to the wider availability of chatbot development and publishing platforms. Both serve as tools and applications designed to create, test, train and deploy chatbots for use across varied business front-end operations. Understanding the slated differences between the two is vital.
A chatbot development platform is a tool or application used to build a chatbot from scratch. Using a bot development platform gives developers more room to add extra functionality into the chatbot like machine learning, API integration, and conversational flows.
Some popular enterprise chatbot development platforms include:
- IBM Watson Assistant
- Microsoft Azure Bot Service
- Google Dialogflow
- Facebook AI
Chatbot development platforms come in two types:
Non-coding Chatbot Platforms
Platforms that do not require any first-hand coding knowledge. It’s pretty basic and even novice programmers can start building their own chatbots using various features like drag-and-drop interfaces and other built-in resources.
E.g. – Chatfuel, Botsify, Flow XO, KITT.AI
Coding Chatbot Platforms
Platforms that require little or intermediate coding knowledge and skills. Comes integrated with a wider array of technological frameworks giving developers an edge when it comes to creating remarkable conversational interfaces inbuilt with AI. Coding development platforms chiefly find use in creating intelligent bots with extended capabilities and integration into CRM or other systems in an organization.
E.g. – IBM Watson, amazon lex, Wit.ai, Dialogflow, Microsoft Bot Framework
Building Chatbots from Scratch
You can also build a chatbot without depending on any platform. This would require specialized coding knowledge and skills to use development methods that include deep learning, Python libraries like NLTK, sci-kit learn, etc. Here, the development team will have total control over the bot’s learning curve.
Related Reading: Chatbot Security Measures You Need To Consider
Chatbot Publishing/Integration Platform
A chatbot publishing platform, on the other hand, is the medium through which the chatbot is made accessible to the users.
A few examples are:
- Your business website/ web application
- Facebook Messenger
- Google Assistant
We chose IBM Watson to create and deploy the bot for our client owing to the robustness, scalability, multi-language support and natural language processing capabilities of the platform.
After settling on a development or publishing platform, obviously, the next thing when it comes to creating a chatbot is to ensure that it contains all the elements that make it intelligent and conversational. The key aspects that round off a chatbot should be well defined beforehand so that it remains beneficial to you and your customers.
The Prerequisites of Building an Enterprise-grade Chatbot
Getting started with building a chatbot require a coordinated approach. First and foremost, you should remain clear of the intention behind deploying a chatbot. Will your business really be better off by deploying a chatbot? What function do you attribute the chatbot to take up in your organization? Is it a replacement for customer service across various user touchpoints or in a broader sense, to automate several key functions of your business like HR or sales.
Whatever the use case may be, there exist several key attributes to adhere closely when building a chatbot. As the technology partner for our client, our work with AI chatbots did present us with an entirely different note on the how-tos of building and deploying chatbots having remarkable conversational interfaces. Out of these takeaways, here are some noteworthy points to take care while building a chatbot for your business.
Design a Fluid Conversational UI
A well structured conversational UI stays at the core of any chatbot program. A sound conversational interface delivers a truly frictionless experience for the user across different touchpoints where it is deployed. By using an interface centered around language, conversational UI brings more ease and proximity in the interactions between the chatbot and its users than what is obtained through syntax-based commands or icons.
Pinpoint the Target Audience
Defining the target audience, which the bot is intended to serve stays in line with the actual purpose that the bot will serve. To do that, you should begin with clustering the targeted group and then bring into the picture their needs and expectations. The bot’s persona and abilities should remain in sync with the needs of the target audience, whether it is answering basic queries or guiding the user access services, making purchases, provide information, etc.
More importantly, the age group which the user category belongs also have a determining role since it helps structure the interactions of the bot in such a way so that it remains consistent with the age of the user. For a target audience well beyond 60+ years of age, the bot should engage with them differently than they would do to a teenager.
Apply Domain Knowledge or Specialization
Domain knowledge roughly translates to the knowledge and deep understanding of a specialized field or discipline. When applied to chatbots, domain knowledge brings a sense of purpose regarding the function that it is geared at whether it be assisting your customers to get answers to their inquiries or with lead scoring for guiding customers through each step of the sales funnel. It is actually learning, training and understanding through every interaction or task and then iterating for rapid improvisations in engagement and functionality.
Provide a Distinct Personality
Building chatbots devoid of any personality traits make it dull and robotic, which never interest the user to take any action, least create trustworthiness. Giving a distinct personality into the chatbot that you build remain pivotal as it represents your company’s unique branding to the customer first hand and creates engagement on a personal level. Weaving the personality based on the target customer that it serves, alongside giving it personality traits like a sense of humor, wit or even sweet sounding contributes significantly in providing a delightful customer experience.
Enable Quick Transfer to Human Agents
Bots, however, advanced they may seem with AI and NLP can at times remain incapable or even falter when engaging with customers. Switching to a human agent at the exact time is one way to prevent your customers from getting frustrated and leave while still providing the assistance that they seek. When designing a bot for customer service, it is mandatory to put into place the procedures that ensure a seamless transition to a live human agent whenever the customer becomes irate or dissatisfied with the bot’s interactions.
Acknowledge the User at Every Input
While designing the bot, it is downright important to add a built-in response mechanism that automatically acknowledges every single user input. When a user types in a query or for other information, the bot should first recognize the input and give immediate feedback. Failing at this can leave the user uncertain whether the bot has actually understood the given input. By creating a means to recognize the user input through a return message or greeting, the bot will appear more engaging and human-like to the users.
What to Avoid when Designing a Chatbot
Building a chatbot step by step can turn futile if one fails to understand the specific things or mistakes to avoid. Being a challenging process, bot development comes with its own pitfalls extending into every stage of the building process right from conception to maintenance, which is something developers need to be aware of.
Here are some of the common mistakes to avoid while designing a chatbot:
Avoid Conversational Roadblocks and Looping
In certain situations, a chatbot can fail to provide answers to the user and would end up repeating the same responses over and over thus creating a loop. If not dealt with immediately, such conversational loops can frustrate the user causing them to exit. Besides, such roadblocks leave the user unable to continue with the chat thereby prompting them to refresh the conversation and start everything from the beginning.
When designing a chatbot, it is downright important to eliminate any instances of looping with the exception of feedback loops by rigorous testing before it is made available to the users.
Beyond that, a chatbot should never force the user to repeat or structure the questions differently in order to understand the intent. Rather, the chatbot should be able to understand all the different types of questions and not just the samples it was trained on to provide the right answer.
However, confirming if the provided response was helpful to the user is a good way in which the chatbot would be able to learn and evolve over time. Rigorous testing to cover all possible chat flows of the user will help you avoid such instances of roadblocks and looping.
Missing Out on Testing Prior to Release
Failing to subject the bot to several phases of testing is one thing to avoid when building chatbots. Testing a bot for any discrepancies can help identify potential bugs or errors that affect the quality of its interactions. The testing phases go through the developer, functional and user aspects to assess the speed, accuracy, intent, and conversational flows of the bot for making it flawless prior to release.
Opting the Wrong API
Drag and drop bot development frameworks may work easy but comes with severe limitations. That does not mean settling on a coding platform to start building your bot from scratch, which has its own drawback of higher turnaround times. The right approach is to opt for a framework that balances all these features with the ability to customize them depending on the task. Bot building frameworks like IBM Watson contains a mix of the two and is preferred over basic builder platforms.
Poor Design and Build
Poor design accounts for several issues that may crop up once the chatbot is deployed and made live. It may lead to inconsistencies in the interactions, forcing users to exit causing an increase in bounce rates. Bad design contributes to a cluttered user experience that interferes with seamless navigation through the predefined conversational flows. Understanding the scope of the bot and then working on building a hassle-free interface can resolve the issue.
Elements that Define the Brain of an AI Chatbot
In a chatbot, certain elements present commonly dictate the functioning and help the bot understand the user input to provide the appropriate answer. The concept guiding all chatbot building platforms is to train the bot using Q/A pairs as represented by the following elements:
Intent – Identifying the intention of a user query
Entity – Identifying the entity in the user’s intent to understand the intent further
Dialog – Response provided to the user for the identified intent and entity
To better understand the intent, entity, and dialog, consider a chatbot integrated with Alexa at your home.
Turn off the Lights, Turn off the Music, Turn off the TV are all user commands with the same intent – “Turning Off”
Lights, Music, TV are the entities based on which Alexa decides what needs to be turned off.
The response Dialog from Alexa would be entirely dependent on the intent and entity, which produces the following output – “Hi, turning off music for you.”
Deviating from the conventions governing the functioning of rule-based bots, AI chatbots goes an extra mile when it comes to the quality of interactions. Put side by side, it is easy to distinguish between an ordinary bot and an AI customer assistant.
The former is severely limited in functionality and lags behind when it comes to creating engaging customer experiences. The latter is more adept at interacting with multiple customers and delivers human-like conversations by wrapping a layer of personalization.
Besides, AI bots contain a few extra elements, which gives them their natural conversing abilities and customer service automation capabilities such as:
Natural Language Processing (NLP)
Natural language processing gives chatbots the ability to carry on natural conversations with humans. It enables the bot to interpret the user input and provide responses accordingly. An NLP enabled bot can seamlessly understand the intent, entity, and context of the user commands to render accurate matching responses that carry on the conversations in a smooth flow free from any interruptions.
An intelligent bot derives its ability to instantaneously supply information to every user request while moving through the sense-think-act cycle using a knowledge base. Simply put, a knowledge base acts as a repository for the trained data sets and the data gathered by the bot through every user interaction. This accumulated data acts as a source for the bot to pick the relevant information upon request as well as to multiply its learning capability.
Quick and After-hours Support
Anytime engagement is possible with an AI-based customer assistant owing to its round the clock availability. Engagement must happen at any time of the day whether human agents are behind the desk or not. AI customer assistants can reach out and retain customers at any given time thus minimizing or eliminating the chances for any miss outs. Even after elapsing conventional working hours, an intelligent bot can provide constant after-hours support to field multiple customer inquiries for acknowledging their requests/queries.
Services tailored to individual needs have ratcheted up in demand. AI bots can do well to provide the user exactly what he/she needs by creating highly personalized experiences. Getting acquainted with a user and gradually moving up the level of interactions into a more personal level makes the customer feel valued. One way chatbots do this is by recalling the specific context derived from previous conversations.
Ideally, a well-designed chatbot should able to look back on the data trails leftover from previous user interactions to minimize the need for the user to repeat the things that they have already mentioned before. By training the bot with relevant questions and leveraging user data from past interactions will spruce up the interaction level and usher in truly personalized experiences that trigger successful conversions.
Chatbots Finds Application in Diverse Areas
Chatbots make it easier to get a specific task done. There is no need to have a separate app since it is possible to ask the bot directly via any messaging platforms that it is compatible with. Besides automating customer service, chatbots have acquired a renewed role and have forayed into several areas linked to our lifestyles. Its applications have broadened from pushing content to making payments, getting recommendations, etc. Some of these include:
Ordering food from your favorite restaurants is easier with chatbots. Major fast food chains have already deployed chatbots that allows its customers to place orders by initiating conversations. Its relatively easier and do away with the need to have a separate app. A user can order food directly by asking the bot, which will then easily guide through the rest of the process like showing up the current menu, finishing with the payment and tracking the delivery status in real-time.
Whether getting reservations for dinner at your preferred restaurant, to booking flight or movie tickets, chatbots can turn it simpler and interesting. Rather than having to go through an app and tediously follow around, a chatbot delivers a hands-free experience (if the bot supports voice commands) or a minimal interface to quickly search, compare and book tickets all via natural conversations.
Curated content is on the rise and chatbots can help with the filtered dissemination of information based on user interests. Top media and news organizations are now integrating chatbots across popular messaging platforms like Facebook Messenger to deliver content like news, insights, blogs and how-to guides by understanding user preferences. Users can get the latest content right away by conversing with the bot without installing separate apps or searching the web.
Chatbots integrated into e-commerce platforms can guide customers to make informed decisions related to their purchase faster. It enables a more convenient shopping experience as customers can search and shop by conversing with the bot via text or voice. The use of conversational assistants in such shopping portals will give rise to improved personalization of their services, which ultimately adds to customer satisfaction and help them get the right products meeting their wants and needs via recommendations.
As organizations are increasingly lean towards a data-driven approach, chatbots have found immense scope and application in departments like HR. Rather than putting people behind painstaking hiring processes like candidate screening, a chatbot can very well take over this task to reduce the burden and thereby free the personnel for attending other crucial functions. Moreover, getting a chatbot to partly handle HR will also assist with fielding basic inquiries made by the employees thus giving way to quicker response rates and more convenience in the process.
We did work on an internal chatbot for automating a few of the functions related to our HR department. Already in use, this chatbot is successful in providing assistance to the HR team by giving first level support to the employees around the clock in areas like onboarding, circulating training documents, and employee assessments.
Will Chatbots Ever Replace Humans?
With Gartner estimating that 25 percent of all customer service operations will take place through virtual customer assistants (VCA) by 2020, it is without a doubt that chatbots and AI will positively disrupt how organizations deal with their processes and operations. Research into its potential value reveals that chatbots alone can help businesses save about 30 percent on customer service costs. Besides, the advancements in the field have elevated human-computer interactions to new heights, where the lines have started to blur.
So, will chatbots eventually do away with the need for human skills and labor?
The answer: No.
Chatbots are never intended as a replacement for human skills. Its fundamental role, however, is to serve as a means to augment the human skills in whatever function that it takes up, whether it is in personalizing customer service or automating certain areas of your business like HR, sales, marketing, etc.
Call centers or other organizational front-ends cannot function without adequate backends in the form of human agents. Besides, achieving true human-like conversation is still miles away and may very well turn out an unrealizable prospect. Considering the high efficiency of chatbots in handling certain tasks, it can function better on a limited scale rather than taking over entirely. Meanwhile, giving a bot full authority over specific tasks does possess security risks that may negatively affect your organization.
Moreover, human skill is still and will be in the future an indispensable irreplaceable element. This also means that none of us need to worry about AI bots being an existential threat to our jobs. Rest assured, AI will never completely replace humans in the foreseeable future.