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Tag: Artificial Intelligence

Using Artificial Intelligence in Indian Farming Sector: The Way Forward

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India’s roots and foundation have been agrarian, and they continue to remain so. The India Brand Equity Foundation (IBEF) reported that 58% of rural households in the country depended on agriculture for their livelihood, as of 2018. On the level of the national economy, agricultural services and machinery industries have led to a cash influx through foreign direct investments of about $2.45 billion. 

Looking at these metrics, it is clear that the agricultural sector has much to gain through the use of technology to advance its crop yield, in ways that grow the sector and benefit the farmers. The field of. Artificial Intelligence (AI) has shown scope for widespread application and impact. The most popular uses of AI in farming span the life-cycle of sowing, caring, harvest, and selling. Here, we detail some applications of AI usage in the farming sector across different periods of harvest. 

Predictive Technology
Predictive technology such as Microsoft India’s AI-based sowing app has addressed a critical issue of the right time in crop sowing. The right sowing time cuts losses through seed costs and fertilizer applications. To automate this through historic data, this predictive app uses data from over 3 decades to determine the optimal sowing period, which is then shared with the farmers via text messages. The findings from their pilot run indicated that crops sowed at the time predicted by the AI-based app lead to 30% higher yields in the targeted geographical location.

Microsoft India has extended this further and uses AI & machine learning to assess the risk of pest attacks on crops. This helps farmers take preventive action before it is too late. On the other end of the spectrum, makers of predictive AI tools have even reached out to governments and policymakers through their price forecasting feature for agricultural goods.

Automating Tasks
The World Urbanization Prospects report a massive movement of population from rural to urban areas, thus leaving fewer hands-on-deck in the rural areas where agriculture thrives. This creates a need for automation of tasks that were previously manual.

Using automation AI-based tools that help operation through remote locations, agricultural operations can rely lesser on manual efforts in their processes such as driver-less tractors and automated irrigation systems that account for weather conditions.

Image Recognition Tools
Through image recognition, certain AI-apps have been developed that identify potential defects and certain deficiencies in the soil that’s easily captured by any smartphone. This app is called Plantix and has been developed by a Berlin-based start-up called PEAT. Once these deficiencies are found out, the farmers are then equipped with solutions such as soil restoration techniques and more so as to address the issue found. 

What’s Next?
Thanks to the success of AI-driven modifications in the farming sector in India, the path has been forged and is followed by plenty of upcoming technologies. The challenge is to reduce costs so as to make it marketable in a mass way. It is predicted by technology experts that crop and soil monitoring techniques will remain important tools even as climate change is being increasingly studied and documented.  

The tools currently seek to address the core issues in the agricultural sector such as crop yield increase, soil health, and pest prevention. It is even anticipated that AI robots might soon start making an impact in this sector.
While there is significant progress, the ground is fertile for newer technologies to take root in the Indian home soil of agriculture.

AI its usage to manage regulatory compliance in banking Fin Services!

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A recent study revealed that the estimated annual cost of regulatory compliance and governance software spend by our banks is around $70 billion. Bank attorneys, loan officers, and paralegals spend thousands of hours into loan agreements, regulatory compliance filings, and other contracts to see if they comply with the law. Artificial Intelligence or AI has found its latest application in this significant issue among bankers. We are looking at a whopping 30% reduction in the total cost with this AI application.

How AI Helps…

The AI-based systems are now being implemented to document verification. They have been successfully tested to interpret regulatory meaning, comprehend the required action, and codify the compliance rules. The Contract Intelligence (COIN) programme developed by JPMorgan Chase is an example of the early adoption of AI technology. Through this system, the company can process loan documents that would cost around 360,000 working hours.

Some other areas of compliance services where AI can offer more safety are the following.

  • Know Your Customer – It’s already clear that AI systems can analyze a vast amount of data and scrape the web to find patterns. This ability can be used to strengthen the KYC processes. Pattern recognition techniques paired with unstructured text analysis, these systems can identify risk-prone customers.
  • Money Laundering Detection – Using AI, monitoring reports and regulatory alerts can be evaluated as risk indicators. The accounts with more significant exposure to these indicators can be analyzed further. It reduces the complexity of the existing Anti Money Laundering systems.
  • Detection of Rogue Employees – It is possible to find the employees who generate a fake account using AI. Reports using the same e-mail or IP address can easily be tracked down by AI.
  • Trade Monitoring – AI can learn about the trader’s behavior and personality. This will lead to more precise predictions about suspicious trading. The time lost due to false alarms can be saved.

More and more application of AI in financial regulatory compliance can be expected with the progress in the algorithmic machine learning models.

Challenges In Implementing AI

Regardless of the immense pros, few issues are slowing down the AI implementation. The problem of rectifying information processed by AI is considered one of the biggest. We know that often our internet can be riddled with fake news and misinformation. If such information gets to the AI and influences its decision, the result would be terrible.

The efficiency offered by AI in decision making is tremendous but, it also takes the professionals away from this decision making process. It may create an opacity for the regulators to see if the proper procedure is being followed for business practices such as suspicious activity reporting.

Despite these challenges, the potential offered by the AI is attracting the financial Institutes to try it. With researches showing large signs of progress day by day, we can expect AI to decode these challenges sooner. Shortly, the AI will undoubtedly replace the legacy system and reduce massive costs incorporated with the current system.

Is AI the Answer to our Transportation Woes in India?

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Until the recent past, Artificial Intelligence or AI was familiar to us only through science fiction movies. Then AI came to real life in the form of digital personal assistants like Cortana and Siri. Even though we were not fully aware, the AI researches were making rapid progress under the radar. From a pure personal assistant in our smartphones, the AI applications have grown into a giant business tool used to analyze and understand valuable data. In this article, we will discuss how India is trying to make use of AI to solve transportation woes in the country and how useful it could be…

Transportation Woes in India

It was found during the Harappa and Mohenjo-Daro excavation that roads existed in India as early as BC 2500. The importance of roads was escalated only after the Second World War. The number of motorized vehicles increased and hence the use of roads. Since then many attempts have been initiated by the government to improve the transportation facilities in the country. However, new issues came along with the time and few of them remain to be solved yet. The following are the significant issues recognized in Indian transport.

  • Road Accidents and Congestion
  • High Number of Traffic Death
  • Insufficient Public Transportation Infrastructure
  • Lack of Assisted Vehicle Technology
  • Need for sustainable transportation

The AI way

NITI Aayog (National Institution for Transforming India), a policy think tank of India has identified the following applications of AI to improve traffic in the country.

  • Autonomous Trucking – Through creative platooning and other techniques offered by independent trucking, a significant increase in efficiency and safety can be achieved. Optimal road- space utilization is also plausible through this system.
  • Intelligent Transportation System – It includes sensors, automatic number plate recognition camera, speed detection camera, CCTV camera, stop line violation detection systems, and signalized pedestrian crossing. Using AI, a real-time dynamic controlling of traffic flow can be made.
  • Travel Flow/Route Optimization – Given the access to traffic data, AI can make predictions about the traffic conditions and make human-like decisions on route selection. AI can also predict the flow of traffic at the network level and recommend flow strategies to contain congestion.
  • AI for Railways – Through real-time operational data analysis, train operators can be provided with a safer work environment. The derailment accidents can be predicted with remote condition monitoring using non-intrusive sensors for track circuits, monitoring signals, axle counters power supply systems, etc.
  • Community-Based Parking – In this system, AI is used to help drivers find vacant parking spaces. After collecting data about the parking spaces, the AI allocates cars to areas, in a way that the demand is always met.

Is That Enough?

Without a doubt, we can say that AI is going to improve our traffic conditions. Problems related to congestion and strategy planning will most certainly be improved. Better traffic control and road utilization can be expected. However, the Indian transportation woes extend beyond that. The Issues regarding lack of infrastructure are not going to be changed by AI.

The introduction of AI may not solve all the traffic woes, but a considerable increase in the efficiency of current facilities can be achieved. Better utilization will undoubtedly improve the transport facilities in the country.

The Economics Behind Artificial Intelligence!

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The introduction of Artificial Intelligence in various applications is set to overhaul the economics of multiple industries. Due to rapidly advancing technology within Artificial Intelligence, the cost of prediction is decreasing at a fast pace. This decrease in prediction costs results in projection being used to solve many new problems, even ones that we generally don’t use prediction to solve.

For example, let us consider the case of autonomous driving. Before AI, autonomous driving, as we see it today, did not exist. It merely consisted of engineers programming a vehicle to move around in a controlled environment with instructions to run in case of obstacles and following directions to reach a destination. But with the introduction of modern AI, autonomous vehicles have gotten the capability to be smarter.

AI in Autonomous Cars
Today, when an autonomous vehicle is being “taught”, a human driver is put behind the wheel and drives as they normally do. The AI then uses various sensors onboard the vehicle to observe how the human drives and comes up with its protocols for use in particular situations.

The predictions that the AI makes, in the beginning, will undoubtedly be flawed sometimes, but the AI can learn from its mistakes and update its protocols accordingly. The more “practice” that the AI gets in this way, the more accurate its predictions keep getting and can ultimately replace the human at one point. This method of “learning” by the AI works the same way wherever it is applied.

Errors in Prediction
As the cost of prediction drops, the demand for human-based prediction will decrease. Human prediction is prone to failure due to a lot of factors like human error, clouded judgment, or even negative emotions. Using AI for forecasts removes all of these problems. Hence if adequately applied, AI can make much better predictions when compared to humans. Since AI is more efficient and costs less, eventually the value of the organization or company using it goes up.

The only area where AI falls short is human judgment. An AI can make predictions and give them to a human, but it is ultimately up to the human to decide what to do with it. Some companies like Amazon are working to remove these limitations, and their work has shown that ultimately AI can be used to make judgments based on their customers’ preferences and spending habits. For example, if a customer regularly orders a product, then the AI can decide to place the order for the customer when the time comes, thereby increasing the chances of selling the product.

Organizational Benefits
AI will be the most beneficial to organizations that can define their objectives and goals clearly. As we have seen above, the method of “training” AI makes it essential to have clear-cut objectives to reap the benefits. We have already seen AI making substantial disruptions in industries where it has been applied.

A 2013 study conducted by Oxford University estimated that AI could replace 47% of jobs in the coming years. A similar survey conducted by OECD estimated that AI could return 9% of jobs just within the next two years. Another study conducted by Accenture concluded that over 84% of all managers advocate the implementation of AI to make things more efficient.

Hence, to conclude, AI will have drastic implications for every industry, with it replacing humans in several roles. However, the savings to be gained from AI will make business practices more efficient and increase profitability.

Impact in Machine Learning and Artificial Intelligence on Real Estate and Trends!

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Impact in Machine Learning and Artificial Intelligence on Real Estate and Trends!

Have you ever been on the lookout to buy a house or an apartment and found yourself buried in heaps of redundant information? Your dreams of owning property were either forced out the window or grew even more complicated thanks to inadequate information. Well, thankfully this is effectively becoming a thing of the past with the entrance of AI into the world of real estate.

Artificial Intelligence is finally taking over every arena and domain in the world and comes as a welcome relief rather than a cause of alarm amongst us. Real estate is relatively late to the game, consequently, only a few elements of the sector are currently benefiting from machine learning.

What Is Machine Learning?

Machine learning is mostly a computer algorithm that assimilates every ounce of data it is fed, analyses it, adapts to and evolves with this information. Essentially a program uses all of this to create a better version of itself. If you were to look for homes on a website or continuously search for a set of parameters, machine learning will pick up on this and tailor searches to make them more precise and even send recommendations on related listings your way. You are likely to not only have a large number of choices you will be pleased with and a quick search that makes you a homeowner in no time.

Applications in Real Estate

A real estate company can watch their sales numbers explode thanks to AI-driven programs that bring the right customers to them. If you employ this software, you could soon be up for the title of ‘sales executive of the month’ thanks to the number of customers coming your way. Algorithms also improve upon sales campaigns and perfect the entire marketing sales process to bring tenant and landlord together.

With insights, you learn how to ensure listings can become more attractive to search engines. A seller could help a potential customer clarify all their queries through a bot that learns from each question from various customers. However, you run the risk of a bot being so robotic that it comes across as rude or frustrates a customer by repeating the answer a script provides it. While AI is enhancing enough to do away with this drawback slowly.

Once you own your dream home, you will realize machine learning comes into play, mainly with property management. Automation plays a big part in ensuring the lighting, temperature and in general, the HVAC systems of a building are keeping everyone in it satisfied, while enabling the owner to manage bills more effectively.
The most exciting realm of AI for real estate lies in virtual or augmented reality.

They currently exist in simpler forms such as 360° views of a home, so you get a better feel for your home before you even buy it. However, this function is available in a limited capacity with researchers still sorting the multiple bugs that accompany it.

Irrespective of its pros and cons, machine learning, is here to stay inside and outside the world of real estate. Whether you actively use it to better your real estate experience or not, it has a part to play in improving every element of the process.

Things You Need to Know About AI and Supply Chain Management!

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Things You Need to Know About AI and Supply Chain Management!

You have just walked out of your house and forgotten to turn off the lights. You take out your smartphone and ask your voice-activated assistant to do it for you without having to step back into the house. How did it happen? The world as we know it today has entered a realm of endless possibilities thanks to Artificial Intelligence. A machine that is capable of replicating human intelligence to perform everything from the most mundane tasks to running businesses successfully, AI has seamlessly integrated into our lives.

All this began, 60 years ago when computers started learning the ‘checkers’ strategy in 1956 at Dartmouth College. Fast forward to the late 90s and we started using machines or AI for logistics, data mining, medical diagnostics, and other areas. Now you may be wondering, how does a device do it? Well a typical AI takes stock of its environment and starts evaluating a course of action in a human-like fashion to achieve its goals.

Now, how do we use this super-intelligent machine in businesses and supply chain management? Believe it or not, AI has a significant impact on digitization and business due to its ability to make decisions and risk assessments. For example, with the use of AI, a warehouse manager will be able to successfully forecast and order the next inventory that needs to come in without any hassles.

Here are some more benefits of AI in the Supply Chain –

Improved Customer Service

Organizations are increasing the use of AI to talk to their customers and resolve issues in real-time. AI platforms are intuitive and speak in different languages and solve complex customer queries thereby leading to an improved customer service experience.

Artificial Intelligence uses in procurement

What if, you as the procurement manager for an organization could automate the whole process of onboarding vendors using AI? Businesses today are already using AI to reduce costs, mitigate fraudulent activities, and save time during procurement by enabling machines to make decisions through data analytics.

Physical Prototyping is Outdated

Gone are the days when innovators would physically build a prototype and add on its functions. Today, we have machines that recognize the gestures and movements of hands and then render it to create 3D models of products. This implies that they directly talk to product developers in the digital space and make models in real-time. Another example of its implementation would be switching on a button of a prototype which can be done with a simple gesture by the product designer.

Conclusion

One of the greatest modern inventions, AI will continue to grow by leaps and bounds in the coming years. While we continue to expand its capabilities, we must be cognizant of its challenges and risks.

Reference:
https://www.financialexpress.com/industry/artificial-intelligence-the-next-big-thing-in-supply-chain-management/329033/

https://www.scmr.com/article/8_fundamentals_for_achieving_ai_success_in_the_supply_chain

https://medium.com/@KodiakRating/6-applications-of-artificial-intelligence-for-your-supply-chain-b82e1e7400c8

10 Machine Learning Use Cases You Should Know About

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Today, Artificial Intelligence is being applied in more and more applications across industries. However, unlike what the human-like robots in science fiction fantasies would have led us to believe, modern AIs are mostly used to automate various tasks which can include moving machinery or even finding hidden patterns within data.
Machine Learning is a branch of artificial intelligence (AI) which provides computer systems with the ability to autonomously learn and improve themselves using observation without having been programmed to do so. It has become one of the most significant technological developments in recent history.
Here, every time a customer interacts with the AI, it analyses the person’s actions and behavioral pattern and remembers it. The AI will then use that information to make it easier for the customer the next time he/she uses it. This, in turn, helps companies in identifying patterns across extensive amounts of customer and user data and target audiences which are most likely to buy their products or services.
machine-learning
Machine Learning allows computers to learn automatically without the need for human intervention or assistance, and react to situations accordingly. This increases efficiency and ensures an improved user experience.
Here are ten organizations that are using the power of machine learning effectively in their workflow:

Kaspersky

They use Machine Learning-based technologies in their Endpoint Security for Business. This software can detect previously unknown malware threats by ‘learning’ from relevant big data threat information and by building effective detection models. Machine Learning algorithms help predict security breaches.

Medecision

They developed an algorithm that could identify up to eight variables that helped predict avoidable hospitalizations among diabetes patients. This algorithm was effectively able to process more information for more accurate diagnosis than it’s human counterparts.

PayPal

PayPal has developed an artificial intelligence engine built using open-source tools to detect suspicious activity. This engine has the capability to separate false alarms and true fraud.

Google

Google uses Machine Learning to gather information from its users and improve their search engine results.
machine-learning

IBM

They have patented a machine learning technology that decides when to transfer control of a self -driving a vehicle between a human driver and a vehicle control processor in case of a potential emergency. This means that the algorithm can figure if it’s best to allow the human to continue driving in case of an accident, or if it’s best to allow the computer to drive the car.

Ecree

Ecree uses Machine Learning to power its automated writing assessment software. Whenever a student wants to submit an essay, an algorithm identifies whether the student has written a thesis or a statement of purpose, and then the statement is evaluated.

Walmart

Walmart uses Machine Learning to maximize its efficiency. Its Retail Link 2.0 system feeds on information that is gathered from the supply chain to notice deviations from any process so changes can be made instantly.

Honda

Honda uses a machine-learning algorithm to detect issues in their vehicles beyond the assembly line by identifying patterns in the free-text fields of the respective warranty return notes and from reports from mechanics.

Facebook

Facebook is using AI applications to filter out spam and poor-quality content, and they are also researching computer vision algorithms that can describe images to visually impaired people.

Amazon

Amazon has implemented personalized product recommendations based on shoppers’ browsing and purchasing history. Machine Learning also powers the natural language processing done by their digital assistant, Alexa.

The Major Industries that Artificial Intelligence will Transform by the Year 2020!

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In simple terms, Artificial Intelligence (or AI) is defined by the capability of a computer or a computer-controlled robot to perform tasks, which normally require human intelligence and skills such as visual perception and decision making.

Conceptualized initially as the means to impart intelligence to inanimate objects, AI is impacting multiple industries today including manufacturing, healthcare, and education. According to a Gartner report, AI will create 2.3 million new jobs by the year 2020, while eliminating 1.8 million at the same time. By the year 2022, 1 in every five workers will rely on AI to perform their non-routine task.

 

AI in 2020

How AI is transforming every industry that it touches

The use of AI-based technologies is transforming every industry that it touches by generating a business value projected to value $1.2 trillion globally in the year 2018, marking a 70% increase from 2017. Furthermore, this figure is predicted to reach $3.9 billion globally by the year 2022. According to Gartner, AI is generating business value through the following three sources:

  • Enhanced customer experience
  • New revenue generation through the increase in sales of existing products (or services) or through new products (or services)
  • Reduction in the cost of production and delivery of existing products (or services)

According to Svetlana Sicular of the Gartner research team, “AI will improve the productivity of many job roles while creating millions of skilled management positions including entry-level jobs.” AI will positively impact the technology job market by creating 2 million new jobs globally by the year 2025.
AI in 2020
Svetlana Sicular also adds that most predictions about job losses due to AI technologies is associated with job automation, but ignores the benefits of AI augmentation that complements both human and machine intelligence. For instance, AI and robotics can be leveraged to identify and automate labor-intensive tasks performed by retail workers, thus reducing labor and distribution costs.

Despite the immense potential of AI, the mass-scale adoption of this technology still faces numerous hurdles (including the following), which needs to be immediately tackled:

  • The supervised or structured form of learning by AI systems, which does not imitate the way humans learn naturally from our environment.
  • Lack of creativity and abstract level thinking on the part of AI machines that can process raw data and convert them into intuitive and easy-to-grasp concepts.
  • Being a relatively new concept, AI does not enjoy full public support and trust, thus halting its increased adoption.

AI-based virtual agents (including chatbots) are taking over the handling of simple customer requests from a call center or customer support executives, thus improving business revenue and freeing up employee time for more complex activities and decision making. While virtual agents are accounting for 46% of the AI-based business value in 2018, it will account for only 26% by the year 2022.

Right from our smartphones to self-driving vehicles, Artificial Intelligence is enabling the faster execution of tasks with more accuracy and increased knowledge. This article summarises how AI technology will transform many industries along with the many hurdles that it needs to overcome.

Artificial Intelligence – The Big Game Changer For Business!

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Artificial Intelligence as a concept seemed very distant and tucked away till firms like Google, Amazon, and Facebook brought it into our daily lives and we started seeing its impact in our day to day activities and interactions. Today realizingly or unknowingly we are surrounded by AI in almost every aspect of life from Health to fitness, Finances, Entertainment, education, business and selling, marketing and market research media, and lots more.

Let us try and understand the impact of AI on the key aspects of economic inequality & business cycles and business productivity.

Economic inequality is one of the biggest changes that artificial intelligence is bringing to our doorstep. Artificial intelligence poses the greatest threat to people employed in low skilled or unskilled repetitive jobs, and as more and more companies incorporate AI into their business model, the disparity between low skilled and highly skilled workers is set to increase.

If we consider three major sectors – agriculture, industrial and service, we will observe that the manpower employed in agriculture has reduced the most over the years (although output hasn’t reduced that sharply), and the services sector has seen the steepest rise in a number of people employed.

Every threat and weakness also brings in an opportunity wrapped in strength, and AI is no different. Although a lot of jobs are at risk due to artificial intelligence, AI is laying the field open for several other alternate professions. Programming and testing professionals, coding heroes, data scientists, they all are going to be in great demand in the coming years. Upskilling and learning a new skill is something the workforce would need to embrace if they are to keep their careers on the burn instead of fizzling out.

Artificial Intelligence has impacted almost every facet of business already and looks set to forcefully influence many other areas too. One of the ways in which artificial intelligence is in the way business cycles occur and repeat. Business cycles are the periodic changes from great prosperity and increasing revenue to economic downfall and losses. The business cycles are impacted by AI because AI pushes these cycles closer together and makes them shorter.

We have the growth phase and the consolidation phase during the upswing, but these are now happening more swiftly because AI helps to improve by course correction in a much shorter time. Thousands of data points are analyzed with the help of AI, which then trains itself to come closer to the required output. Compared to that, humans would go through the cycle at a much slower rate.

Let us take an easy example to understand how business cycles are affected by artificial intelligence. In the banking sector, the first quarter of the financial year is usually the most popular for customers to open new accounts to align with the start of a new financial year. From the point of view of the banks themselves, the last quarter is a big quarter to push for new accounts and deposits, primarily because they are rushing to fulfill their annual targets. The remaining two quarters are comparatively less rushed.

What happens to this scenario when the bank applies artificial intelligence to its systems? For the first and last quarters, the system could throw up the details of existing customers who would be likely to need new accounts, so that the bank officials could target them. This data-crunching could begin in the last quarter (for first-quarter acquisitions) and in the third quarter (for last quarter acquisitions).

For the second and third quarters too, which are usually leaner, the prospective clients for new acquisitions could be highlighted. What this whole setup would do after repeating for a few cycles is that the usual cycle of quarters would be disrupted, and acquisitions of new clients would be more uniform throughout the year.

The future looks very exciting for industries who are using artificial intelligence, and the possibilities seem immense.

Customer Service Trends – 2018 Is Making Operations Become Faster?

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A recent publication shows that a smart AI strategy ensures transformative customer service in times where the customer is spoilt for choices in every area, and how firms can use AI as a weapon to offer uniquely differentiated products on the back of its usage. Unlike common perception, the authors have demonstrated how usage of AI will make operations faster, more effective, and cheaper yet more human. It has various updates on chatbots and how they enhance the customer self-service experience at L1.

Usage of prescriptive AI for quelling headcount growth by taking over routine tasks and allowing agents to focus on deeper customer experiences.

There has been a  steady rise of virtual assistants like Siri & Alexa and they will become independent local hubs of customer experience. Visual engagement avenues such as co-browsing & screen sharing and the expected uptick in usage across age groups.

IOT will transform business models as It will allow production companies to provide proactive services for their high-end products, through preemptive on-site /user monitoring and reporting to a centralized center versus reactive service upon a product breakdown.

Usage of Robotic Process automation for improved delivery of repetitive tasks and end to end automation of basic processes allowing humans to take escalations. Machine learning along with this allows them to learn through interactions that they go through to become more cognitive and intelligent.

Enhanced field service by equipping agents with enough information and parts to ensure that they get the customer’s job done in a single visit. This also covers the usage of augmented reality along with digital interactions for deeper interactions in the physical world without physical presence.

The emergence of “superagents- equipped with AI tools” where companies will relook & redefine their workforce basis their skills and charge a premium for the usage of these services. There will also be seen a rise in customer service ecosystems where firms will use a combination of AI, their resources, and partners to see the customer through their entire journey and not just a portion of it.

With the above, we are looking at a new world order where Artificial intelligence will impact every aspect of our lives knowingly or unknowingly and transform the way we lead our lives including individual privacy and experiences.