What is a real-time dashboard?

According to research conducted by DOMO, we create an estimated 1.7MB of data every second for every person on earth! But data is only useful if you can derive meaning from it. A real-time dashboard simplifies this task for you. It is a pictorial representation of data that is automatically updated with current information to derive meaningful insights.

It is an at-a-glance view of what is going right with your business and what can be the improvement areas. It focuses on your KPI (Key performance indicators) and helps you in better decision making.

What kind of information can a dashboard provide?

The main purpose of a dashboard is to tell you a story, the health of a business, or a specific process based on the Key Performance Indicators (KPI) of your business. Dashboards are versatile and can be used for different markets like Retail, E-tail, Travel, Telecom, Healthcare, Sales, and Marketing. Real-time dashboards help you to break down data like your past transaction data and point of sale data to better understand your customer base.

You can have KPIs like customer retention rate, attrition rate, Net promoter rate, and market growth rate. If you are a call center business you will need the response time of your employees, customer satisfaction, resolution rate, and live tracking of your ticket volume trends. Having a real-time sales dashboard can help you keep track of your sales by monitoring high sales regions, customer acquisition costs, and even forecast future sales. It can also keep track of your social reach and website traffic by monitoring your online presence.

The use of a dashboard will also vary from employee to employee. If you are a project management head you can keep a check of the cost of work done and estimate completion of projects. But as a CEO you will focus on ROI, Operating margins, and predicting the company revenues.

Dashboards are versatile and can simplify any information to benefit your business model.

6 Reasons your business needs a real-time dashboard

1.   Time is money

A  company can save over 40 hours per week by shifting to a dashboard reporting software. The time saved in getting the information translated into meaningful insights can be utilized to work on actions. Reports are usually generated at the end of the month because they are time-consuming. By using a real-time dashboard the only time consumed will be in setting up the dashboard, which is minimal to the overall time saved. With the rise in Conversational AI-based dashboards, the value for time and convenience has increased

 2.   All your data at your fingertips

Data never sleeps. Why should your dashboard? With the amount of data generated daily maintaining it becomes tedious to maintain data. Real-time dashboards ensure that all your data is accounted for. You can monitor KPIs and metrics from different platforms under one location. Your data is clean and the focus shifts to outcome-based decisions that can be monitored on the fly.

3.   Accessible to all

Gone are the times when the understanding of business was confined to the sharing of details at business meetings. According to research by BARC, the use of cloud planning has jumped from just 8% of survey respondents in 2016 to 36% in 2020. Dashboards use cloud computing to ensure that the data is accessible and transferable to everyone through your desktop or mobile. Dashboards are simple to access and can be understood easily by any employee. They improve internal communications and transparency in the business. They ensure that every member of the team is on the same page.

 4.   Reduced risk

With real-time reporting failures are highlighted at their initial stages. This reduces the time of diagnosis and helps divert attention to solutions rather than damage control. With an increase in uncertainty in business, it is important to be on your toes at all times and ensure your business grows.

5.   Leveraging artificial Intelligence

Dashboards need to adapt to your business requirements and prioritize your business targets. For readily available insights, dashboards do the task of report development, management,  testing/validation, and deployment in no time. Using advanced machine learning and artificial intelligence algorithms, smart dashboards help support dynamic business queries.

According to Gartner “Continuous intelligence is a design pattern in which real-time analytics are integrated within a business operation, processing current and historical data to prescribe actions in response to events. Continuous intelligence leverages multiple technologies such as augmented analytics, event stream processing, optimization, business rule management, and ML.”  You can use augmented BI based dashboards for decision automation and for providing decision support to improve your business.

 6.   Customizable

No one size fits all. Dashboards understand the need to design visual representation based on your needs. They understand the highly volatile nature of business and adapt to your requirements. They are highly customizable and user-centric.

Dashboards can help simplify the tedious task of reporting and help you focus on growing your business. Real-time monitoring can help your business to stay ahead in the race and transform your weaknesses into your strengths. 

Realizing the promise of an AI-driven world, geniSIGHTS has created ‘FLASH’. ‘FLASH’ is a first-of-its-kind artificial intelligence-powered dashboard with voice support to enable businesses in processing big data in real-time. It is highly customizable and scalable according to your business needs and can be easily integrated with existing systems. Dashboards need to cater to your changing business needs by continuous machine learning and by adding conversational AI to advanced systems FLASH has made reporting easy and convenient for everyone.

Rajesh Kumar, the Founder of geniSIGHTS who strongly believes in AI-driven technology says “ Today’s business insights provide good reports but do not go beyond the basics. Flash is an AI-driven product that empowers the business users to quickly experience advanced analytical insights thereby significantly bringing down the development time.

Check out the FLASH dashboard on https://flash.genisights.com/

Also check out Ordo Ab Chao, A sentiment analysis dashboard that uses Social media on https://genisights.com/ordoabchao/#!/home

Dashboards like FLASH are transforming advanced real-time reporting to a whole new level. According to a report by Deloitte “Early benefits from the adoption of Conversational AI mean the global AI-derived business value is expected to grow by an average of 30% annually”. Businesses are undergoing a digital transformation and the advancements in Self-Service BI have made employees and key decision-makers independent and confident even with a lack of knowledge in data management. The rise in Conversational AI-based dashboards has made reporting simpler and faster than ever before. It is time for you to shift your business to a smarter, cleaner, and faster data-driven real-time business analytics dashboard.

The use of non-branch banking solutions has increased dramatically and especially in COVID times.  Before COVID-19, digital transformation was but a plan on paper, with only 15% of the major banks digitally transforming while for most banks and financial institutions things changed after the pandemic.

According to a recent survey by J.D. Power, only 46% of consumers will go back to “banking as usual.” The biggest change will be in the increased use of mobile banking (20%), increased use of online banking (17%), and decreased uses of branches (10%).

35% of consumers stated that they had increased the use of online banking (laptop or PC) since the COVID-19 crisis, with 17% stating they have used this capability much more.

Banks, credit unions, and financial institutions must use this time of disruption to consider reinventing themselves during this time of the pandemic. This is the right time to use data, AI, and technology to impact innovation and the digital delivery of services and solutions. Improved use of data and advanced analytics can provide meaningful insights that can improve customer engagement and experiences, streamline financial operations, and be a driver for digital transformation.

AI has remodeled banking vastly and can still do provided we tend to harness it properly. AI is empowering Banking and Financial institutions in fraud detection, credit risk scoring, Anti-money laundering (AML), and churn prediction.  BMO is the first Canadian bank where customers use mobile devices to open bank accounts. The Royal Bank of Canada provides a dynamic experience to employees from their first day on the job.

Customer support and the front workplace of banks are already chatbot enabled. Further, with the support of supported past interactions, AI develops a much better understanding of consumers and their behavior. Every client would favor a special methodology for treating their finances. With  AI, banks will customize monetary products and services by adding customized options and intuitive interactions to deliver meaningful client engagement and build sturdy relationships with their customers.

With its power to predict future eventualities by analyzing past behaviors, AI helps banks predict future outcomes and trends. This helps banks to spot anti-money wash patterns and create client recommendations. cash launderers, through a series of actions, portray that the supply of their hot cash is legal. With its power of Machine Learning, AI identifies these hidden actions and helps save millions for banks.  AI is additionally in a position to find suspicious information patterns among whopping volumes of information to hold out fraud management. With increasing online banking facilities this can be of utmost required.  Further, with its key recommendation engines, AI studies the past to predict the future behavior of information points that help banks to successfully up-sell and cross-sell their products.

About 32% of financial service providers are already using AI technologies like Predictive Analytics, Voice Recognition, among others. (Source: Wipro).  AI is integral to the bank’s processes and operations and keeps evolving and innovating with time.  AI can alter banks to leverage human and machine capabilities optimally to drive operational and price efficiencies, and deliver customized services. AI won’t solely empower banks by automating its data personnel, it’ll conjointly create the full method of automation intelligent enough to try to do away with cyber risks, stay in the competition, predict patterns and provide recommendations. Amazon has generated 35 percent of its revenue from its recommendation engine.3 Netflix saved $1 billion in customer retention using its recommendation engine.3 C

Conversational AI (CAI) systems can be rapidly deployed to give a service boost and better decision making during and after challenging times. Many banks have adopted CAI based processes with the use of chatbots. HSBC introduced the digital financial assistant Amy while Bank of America’s AI-powered digital assistant, Erica, has more than ten million users and completed 100 million client requests in the first 18 months since its introduction.  Banks can engage customers with two-way communication with the use of a chatbot or an interactive assistant powered by AI to intelligently respond to their queries other than routine questions to gain a competitive edge.  By adopting AI, leaders within the banking sector are already taking actions with due diligence to reap these advantages.

On the occasion of geniSIGHTS stepping into the 5th year of operations, our HR Head, Mr.Sridharan J.S hosts an e-conversation session with Mr.KC Ayyagari, Engineer @ Google, Experienced Tech Sales Specialist and a Startup Mentor on ‘Steering ahead in the times of crisis’, how cloud infrastructure is at the forefront now for delivering customer satisfaction.

Hi Mr. KC; We are happy to host an e-conversation with you during these trying times.

Sridharan – The cloud ecosystem is a broad one, but there are some common trends that have emerged. Tell us some insights into how cloud infrastructure has changed in the last 3 years.

KC – There are many, but in my view, here are the 3 main trends I can think of:

1) Cloud-native – GenNext companies, startups, and SaaS companies, etc., are pioneering this.

2) Hybrid or Multi-cloud – This trend is picking because of the various factors but mainly because companies don’t want to miss out on innovation from any cloud provider. IT/ITES companies, Consulting companies, service industry companies, etc., are pioneering this.

3) Cloud Burst – Here is where companies are looking to use the cloud for expanding their on-premise infrastructure when needed. Legacy companies who already invested in their data centers, manufacturing, and retail verticals companies, etc., are pioneering this trend.

Sridharan – How is the sudden surge in cloud technology seen during the pandemic?

KC – Cloud computing is keeping us all connected to this pandemic. Many health care organizations started doing remote consulting, all the IT workforce started to work from home, individuals are using video calling services, retail organizations moved their entire orders to online, etc. All these are unplanned workloads for any organization and most of them have to depend on public clouds for the same. This brings to one of the main trends I mentioned above, i.e., Cloud burst. This is also the time where cloud providers also started re-evaluating their products and started releasing products to facilitate these changes all over the world. For instance, Google Cloud released products like BeyondCorp Remote Access; a service lets your employees and extended workforce access internal web apps from virtually any device, anywhere, without a traditional remote-access VPN.

So to say there is a surge in cloud usage and at the same time an increase in the number of different useful cloud services released by Cloud providers.

Sridharan – We know Cloud technology is the backbone for IoT and in order to evaluate data profitably, Analytics companies like ours are aware the hybrid cloud is increasingly becoming more important and Big data produced by the IoT devices are going to be provided by the cloud. What do you think will the demands be like in the upcoming 1 to 3 years?

KC- In general, as per McKinsey & Company, it is estimated that hybrid, multi-cloud is set to be a USD 1.2 trillion market opportunity by 2022. Usage of Cloud agnostic solutions like Kubernetes is on the rise. If you ask me why this is happening, there can be many factors like companies that want to use the best hardware available to the best of cloud services they can use.

Sridharan – Do you think a success factor for analyzing big data could be analytics itself that could provide a seamless link to the data hosted on the cloud?

KC- Today the Vs (Volume, velocity, and variety) of data even within a small startup is in terabytes. Data is as good as the time it is analytics. So yes, the success factor for analyzing big data could be analytics and a seamless link to cloud hosting makes this entire process faster irrespective of the size of Vs organizations are dealing with.

Sridharan – What are the do’s and dont’s for companies not in cloud wanting to move to the cloud, now, during the pandemic?

KC – These are some, I can think of:

• Act fast

• Be prepared for a Cloud burst if your apps are running on your private cloud.

• Start with or migrate to cloud-agnostic technologies like Kubernetes to build your apps.

• Use serverless services like Cloud Functions (in Google Cloud) where ever possible.

• Strengthen your DevOps practices. Make sure CI/CD is set up.

• Be prepared for anything and document everything in the company. Even though it’s hard to say this, it’s not good advice to just depend on your star employee for everything. Be prepared to give him time off or worst case sick leave.

• Listen to the customer. This is the time you need to understand customer trends more than ever.

• Plan your budget properly. Don’t move services to the cloud if you cannot pay for them. Move only the much-needed part of your product.

• Don’t just keep on releasing new features. Now it’s not the time. That comes later.

Sridharan – Are there any additional data security features introduced for the upcoming demand for the cloud?

Kc – Nothing I can think of, especially for cloud demand. Make sure your services are well protected, always irrespective of demand.

Sridharan- What are the new add-ons or features that we can see in cloud technology in the next 3 years?

KC – This is my view: I think we will see more services that support companies with remote connectivity, hybrid cloud, and multi-cloud.

Sridharan – Cloud computing has been a Top 3 trend in IT since it took root with the introduction of AWS’s S3 data storage in late 2006. Where is the cloud technology heading?

KC – In my view Cloud computing would be the defacto standard for running most of the workloads in any organization; big or small. Within the cloud, in my view, serverless computing and microservices are going to be the future.

Thank you for taking the time to have a conversation with us, it has given us a lot of insights.

Thanks again and stay safe!

COVID outbreak has affected many industries with some sectors being affected badly. The infection is hitting associations hard from everywhere throughout the world. Businesses across the globe are adapting to the ‘new normal’ of working remotely.

In this time of crisis, Artificial Intelligence can be a great aid for businesses to boost their operations and increase productivity. This article will discuss how AI can help revive industrial operations from COVID impact.

1. Demand & supply: Organizations always tend to maintain the balance between demand and supply to avoid excess inventory. In this time of crisis, maintaining this balance is critical to achieving as, even if the demand data is available in the open sources, production is either at a halt or is operating with minimum human support. For industries like manufacturing, steel, factory, etc production, their basic activity, operates with human resources. But organizations who have upgraded themselves to industry 4.0 have AI in place to help and enhance human taskforce. AI can perform the same tasks as that of a man but at a faster pace. AI needs data to learn and during this COVID time availability of current data might be a challenge for the organizations. Organizations are using data to train AI to be representative for it to learn the patterns and intents, for demand forecasting and optimal supply chain distribution.

2. Administrative jobs: Artificial intelligence can perform admin tasks like scheduling meetings, tech support, issuing refunds, order tracking, etc with accuracy. This way AI supports human resources by enabling them to focus on value-added activities. AI automates the tasks using machine learning. Advanced AI can perform structured and unstructured tasks from its learning algorithm. This capability will allow organizations with a fair amount of work in a short span of time.

3. Revenue forecasting: Generating revenue in the next few months will be a big challenge for organizations especially for small and medium businesses whose operations are at halt currently because of lockdowns in several parts of the world. Cash flow becomes critical during times like these pushing accounts to maintain data on the path of cash-flows. This data when feeding to AI, will analyze the purpose of cash-flow and forecast the paths where the cash should flow to gain revenue. The data fed or provided must be accurate for AI to predict actionable insights that would benefit the decision-makers. Many organizations are already moving towards revenue loss but with AI they can streamline the cash-flow with profits.

4. Staffing and infrastructure planning: Because of this pandemic and the worldwide lockdown to maintain social distancing employees are staying indoors. Work from home is followed by several organizations but this method is not applicable to all industries. Industries like manufacturing, automobiles require their staff to be physically present mostly in the production and technical field. AI can benefit these industries to identify the number of staff required to be present in the field so that the operations can continue with the minimum workforce. Following revenue losses, many organizations are laying off staff to balance their revenue profits. AI can be utilized to decide if the number of layoffs is correct, more-skilled employees should retain, and many other decisions.

The artificial intelligence network is working profoundly to deliver its applications in every sector to fight the COVID pandemic. But the AI framework is still at the nascent stage and will take time and data to train themselves to fight the next pandemic with substantially more effectiveness and efficiency.

Amidst these trying times, we got a chance to sit down for a chat with our Co-founder & Chief Analytics Officer Ms. Parvathy. She has taken the time to answer questions asked by Mr. Sridharan, our Talent management head and reflect on how her whole journey began, the inspiration behind creating FLASH, steps she and her team have taken to combat the current situation, the vision of geniSIGHTS, major challenges she faced while building FLASH among other things.

Sridharan –  We would like to hear about you from yourself.

Parvathy – I hail from Thrissur, Kerala, and moved to Chennai to pursue my masters in Econometrics. Madras University was one of the few universities in India that was offering a course in Econometrics during that time. I was intrigued by the subject and this served as my entry point into the field of analytics. I joined Aaum Research and Analytics, the parent company of geniSIGHTS, as a Data Analyst and was among the company’s early employees. It’s been an overwhelming journey of 10 years now. With the support of our Founder Rajesh Kumar and a wonderful team, we have delivered some very challenging analytics assignments, built interesting AI products, spun off the platform initiative into a separate product company, and also broadened our horizons to the North American and Canadian markets in these past years. 

Sridharan – So, let’s talk about the COVID situation, how have you and your team handled the situation? What are some of the major changes you made? Also, what are the major challenges you face every day?

Parvathy – We went into a remote working policy a week before the lockdown was announced in India. We have always believed in extending remote working and flexible working hours to our employees in the past and hence it was not a major transition for us. Having our servers running on the cloud and encouraging remote access for all our projects with customers from the beginning made the transition go smooth for us. Apart from those quick huddles to co-ordinate across teams or brainstorm over ideas and those interesting pieces of chai conversations that we miss terribly, work has taken its normal course otherwise. 

Sridharan – We see technology like AI playing a major role directly or indirectly in the fight against COVID, have you come up or do you plan on building anything that’s relevant to these times?

Parvathy – Over the past few weeks, we have seen innumerable instances of AI playing a major role in areas like virus diagnosis, patient care, drug discovery, etc. When the outbreak happened, we saw an influx of dashboards reporting numbers on COVID statuses and hotspots across the country/state/district which was very useful. We wanted to however focus our efforts on understanding how the public was reacting to the pandemic. With the implementation of lockdown and most of the companies going for a temporary shutdown or remote working, mobile screen time of users has increased considerably. Most of them have also become more vocal about their thoughts and activities on social media. We wanted to make sense of this disruption and thus launched ‘Ordo Ab Chao’, an intuitive dashboard providing insights on how the nation is reacting to this pandemic. It is powered through our flagship product FLASH making it interactive over voice. This novel dashboard gives users data-driven insights on the pulse of the nation during these upheaval times as vocalized through social media. 

Sridharan- When and how did the concept of FLASH come about? What was the inspiration? What’s the vision?

Parvathy – We have seen technology adapt over the three stages of click, touch, and voice over the past decade. With the integration of voice-based products and AI assistants like Google Assistant, Alexa, etc. into everyday technology, there has been a paradigm shift for individuals to control a large part of their day to day technology needs over voice. This was where FLASH drew its inspiration from. FLASH is your data assistant and marks your essential BI transition from touch to voice. 

Sridharan – What do you think makes FLASH unique? What’s different about it when compared to the other players in the market?

Parvathy – FLASH is a first of its kind AI-driven dashboard with voice support for decision-makers. It is meant for users who are hard-pressed for time and would want to get near-time updates on their business through a conversational tool. The fact that it marries AI and voice support to provide relevant data-driven insights through conversations driven by the speed of thought is what makes it unique over other BI tools available in the market. While there have been tools on Search and AI-driven technology, we have not come across anything like a true data assistant yet. This is FLASH’s USP and the product is also on a patent-pending state in this regard. 

Sridharan – Could FLASH help businesses especially in situations like these? How could it add more value?

Parvathy – FLASH comes with an admin and business user suite that makes configuration and engagement with users easy. The admin suite is powered through features that enable users to add data elements to dashboards dynamically and self-build their own dashboards through voice/text. The business user suite is constantly listening and learning from the user’s interests, past queries, past behavior patterns to provide users with personalized recommendations, and relevant insights. Such features help businesses to scale their deployments quickly and support users to make proactive decisions based on data-driven insights. 

Sridharan – What do you think the roadmap would be for AI-driven products like FLASH?

Parvathy – There is no doubt that AI is soon going to become the de facto standard for all organizations to thrive irrespective of the nature of their businesses. Amidst the ongoing crisis, organizations will need to adopt innovative means to keep their operations running. We, at geniSIGHTS, is committed to make AI an affordable platform for all businesses. While the first set of FLASH deployments was on-prem based and for large corporates, we are soon rolling out a cloud and subscription-based offering of FLASH targeting the small and mid-sized companies. With the technology available at a near and easy reach, the AI adoption drive amongst businesses will scale up and help them in their daily challenges. 

Sridharan – What are some of the other initiatives your team is currently engaged in? 

Parvathy – The team has also been focusing on Conversational AI oriented deployments outside the scope of FLASH otherwise. This is another emerging area of technology that will see widespread adoption. We have already seen a lot of traction for this in the North American markets. Big Data and IoT related offerings have always been a part of our kitty otherwise. The best thing about our standalone AI products is that they are exposed as Restful services making it easier for companies to integrate with their own platforms or products.

Pandemic like the ongoing coronavirus outbreak which has no previous recorded information about how it’s being created has ended up being a great challenge for the government and healthcare officials to collect information and find a way to eradicate it permanently. This hinders the efforts to prevent and rapidly respond to the virus attack and increases the term of the spread. This is where artificial intelligence (AI) plays a crucial role in foreseeing an outbreak, limiting it’s spread and aid researchers fight such pandemics in the future.

Data is pivotal for AI to develop potential medicines for diseases. Currently, the biggest challenge is the availability of past COVID data or any data that is similar. AI identifies possible drug compounds by using generative design algorithms which produce a wide range of prospective results and further culminate the ones worth looking for attentively. There are various AI projects where scientists are closely studying AI as a diagnostic tool. Not only the drug data but other data like news reports, internet, and social media contents are also important to forecast a virus outbreak. Researchers are being able to detect the initial COVID outbreak stages with the content available on the internet and social media channels.

COVID outbreak has accelerated various AI applications to search for a cure. It might be a bit late now for the current AI technology to completely eradicate the current COVID outbreak but with the huge advancements in AI, there’s hope any outbreaks in the future will be better handled. Tech companies have already started researching other potential AI applications to mine all the outbreak information and train their AI with accurate information in order to unfold any future pandemic beforehand. As artificial intelligence can find connections and patterns through big data, it makes it easier to determine what kind of treatments or experiments could yield results. When compared to the initial outbreak, data now are immediately available which is helping AI in a greater way as some researchers are able to produce the genomic sequence of the new virus. This will help future scientists to do such studies and trials with increasingly precise and fast outcomes. The role of artificial intelligence in treating pandemics and other healthcare challenges is just set to develop.

Significantly, the key AI elements that will majorly help in epidemics prevention is its speed and range. AI can instantly detect any anomalies which are incredible when attempting to prevent epidemics. Humans cannot efficiently detect conditions from this volume of data with much speed. AI can be trained with a wide range of available data as it can extract information from them simultaneously. Artificial intelligence is all set to turn into a firewall against deadly diseases and epidemics. This is possible because AI can not only screen and detect the conditions but also identify potential vaccines and treatments for next COVID or any similar diseases.

This treatment revelation will prove essentially significant later on. What’s more, in terms of screening, AI will get one of the essential ingredients in guaranteeing that another COVID wouldn’t affect the worldwide economy. COVID-19 coronavirus is likely to cut global GDP growth by $1.1 trillion this year, in addition to having already wiped around $5 trillion off the value of global stock markets (Source: Forbes).

Artificial intelligence algorithms are as of now developing drugs for the known diseases and this technique can be utilized to identify new anti-infection compounds that could execute any similar viruses or bacteria in the future. It will lessen the time taken by scientists to do research on new, unknown infections and their possible medications from scratch during the future outbreaks. To be successful, AI-based medication engineers would need to prepare for the time, choosing an infection genome prone to cause problems in the future and focusing on it when there are hardly any motivators to do as such.

It has been proven time and again that during any situation of crisis, technologies have been immensely supportive. Governments, academic institutions, and start-ups are all doing their part to deploy innovative solutions as quickly as possible when situations warrant.

Apps are being used as a contact tracing solution to help in breaking the chain of infection and connect the patient with the healthcare system and the government directly. Such an app also keeps the user alert about infection in their surroundings. Telemedicine/teleconsultation is a field that is seeing a wide acceptance where one can avail doctor consultations through an electronic portal.

Banking technology which is not so new is becoming of great use these days. People prefer going to shops that are UPI enabled instead of crowding at ATMs. The government is also able to transfer relief amounts and other benefits directly to the people. The constant live updates through the government dashboard itself have demonstrated the application of technology. We are able to receive live updates and all sorts of news at our fingertips.

Even the future of virtual education has become a matter of concern with the recent trend of online classes and webinars. Virtual communication has come such a long way that we are able to communicate with foreign investors and facilitate FDI & FII inflow for the benefit of our economy.

Most of the employees of the service sector are able to work and organize meetings from the comfort of their homes.

The coronavirus was first detected in Wuhan as unusual pneumonia by a Canada based AI firm. The predictive analysis could have been used globally to take further precautions. Similarly, rapid testing kits also could have been developed earlier with the help of AI.

There are pros and cons to the use of technology. However, in a time like this, the uses have clearly outdone the drawbacks.

AI applications in the automotive industry are huge, from self- driving cars to voice commands systems to build a car with more convenience and safety, Automated Guided Vehicles (AVGs) are being used to move materials in the plant units without human intervention.  AI has a huge potential to innovate the automotive industry to gain a strong foothold. Conversational assistants integrated into cars have NLP and ML algorithms enabled to provide the driver with ease of delivering commands to the system. AI also has other advantages like to imitate, develop, and assist human actions while leveraging its algorithms in precision with machine-based frameworks.

According to Allied Market Research, the global automotive artificial intelligence market is expected to reach $8,887.6 million by 2025, from $445.8 million in 2017, growing at a CAGR of 45.0% from 2018 to 2025. The growth of AI in the automotive market will increase if there’s a demand for autonomous automobiles and customer driving experience enhancements. Customer’s most preferred AI interface is voice control because it allows the drivers with hand-free control which reduces the probability of getting distracted from the road. Today voice assistants are ruling the tech industry with its advanced features like talkback, interactive experience, real-time insights, etc. The concept of self-driving cars has been there in the automotive industry for quite some time and to materialize this concept artificial intelligence plays a very crucial role. AI has its applications in many other industries too but the scope of innovation is more in the automotive industry. This cutting edge technology leads to many automobile manufacturers designing and developing the best AI-enabled automotive technology. Artificial intelligence’s subset ML enhances the AI automotive features by learning the driver’s movements and expressions from the videos/images captured from the cameras integrated into the car that helps the system to learn and perform accordingly. AI in the automotive industry is estimated to exceed $10,73 billion in the next five years (source: medium.com). The future of automotive vehicles relies on AI and its algorithms.

To get a deeper understanding of what AI/ML brings to the automotive industry, in this article we will discuss about its prominent applications in this industry.

1. Cloud computing: AI-powered vehicles require big data to efficiently perform their task and for training purposes. This application makes sure the data is available 24/7 to the system to execute AI technologies like predictive analytics, big data access, and centralized connectivity. The cloud service further helps in predicting the maintenance required by monitoring the sensors for identifying irregularities in operation. It helps the drivers to get their required items easily available by recommending them the nearest buying point and also let them pay for the fuel from inside the car in gas stations. Cloud computing allows automotive marketers to brand their product for a precise target market rather than targeting the audience as a whole.
2. Deep learning: Deep learning enhances AI activities in automobile functioning thereby avoiding the chances of having any problems. It automates the tasks which make the work of the driver easier to manage the vehicle function. It has brought a wave of innovations in the automotive industry by its applications in advanced driving assistance systems and self-driving. Deep learning has lately instigated a huge headway in this sector.
3. Infotainment systems: Features like eye tracking, speech and gesture recognition, monitored driving, etc. comprises the infotainment. These are some of the advanced features drivers’ expect from automotive vehicles. With artificial intelligence developing the infotainment systems have become better and bigger. It receives data from the cloud service neural networks for operational purposes.
4. Cognitive capabilities: Cognitive frameworks are expected to work like a human which would interpret a genuine circumstance, so as to do that, profound knowledge of unstructured data is required. Auto manufacturers have now begun consolidating this into their vehicles. With capabilities like identifying patterns, natural language interpretation, cognitive analytics gives us a path to mine and comprehend this data.

AI innovation is constantly developing and the reception of this utilizing innovation is welcoming; thanks to the engineers and analysts who have been able to structure the application to the optimized computational capacity to attain the degree of flawlessness.

Governance need not always be a time consuming, tedious, and inefficient process, at least not with the use of AI.

However, a government agency’s readiness for AI is not only about buying or and installing new technology. The dynamic nature of AI calls for preparation across multiple critical areas. To capture AI’s potential for improving governance, there has to be a solid plan to re-establish relevant existing processes, upskill, hire key staff, train personnel and refine approaches towards public-private partnerships and develop the necessary data and technical infrastructure to deploy AI.

With a growing population and a rapid economic engine, every government has a lot of data to be processed. For any new scheme to be applicable, a lot of time goes into processing data. AI can help shift through data and identify patterns that could improve decision making which is ultimately done by a person.

And Moreover, it is not just about policymaking but also for improving citizen services. For immigration services and other government portals, conversational AI can be utilized. For instance, when one has to file tax returns, the government website can provide virtual assistance. this way every layman would find it easy.

Security within a country can be enhanced with features such as facial recognition. This proves useful in airport terminals where mobility can be made faster and crowds could be managed better. In vast cities that function 24 hours, it becomes difficult to administer. Real-time data sensors can alert authorities of abnormal activity to aid in keeping a check on crime and any abnormal activity. This has been used and proved beneficial in Las Vegas. Real-time sensors can amplify public transportation convenience and safety with penetrative maintenance and risk assessment. With urban mobility being a crisis in recent times, such technology to ensure safety and traffic management is most needed.

Deloitte has estimated that automation could save US government employees between 96.7 million to 1.2 billion hours a year, resulting in potential savings of between $3.3 billion to $41.1 billion a year. (source: Deloitte insights).

Conversational AI is the quickest way to convert your leads into prospective customers. It has already built an enormous application space in the technology and marketing domains. Businesses are adopting voice-based assistants to enhance their sales efforts and increase the lead generation processes. According to McKinsey, AI has the potential to add between $1.4 to $2.6 trillion in sales for businesses worldwide.

Many well-known brands are implementing conversational AI in their business models to offer customers with personalized experiences. AI helps businesses to understand the intent of the customer while engaging in conversations with them. In the present time, customers have a variety of options to make purchase decisions. Businesses can further leverage the NLP algorithm to respond to customer intent in the form of text or voice. Conversational AI uses an ML algorithm to continuously learn customer behavior, their language based on the conversations and help generate recommendations to ease the customer’s product choices. This method can be achieved by using powerful intelligent chat platforms. The conversational AI must be well trained and computed before customer handling.  More customer-centric campaigns with marketing techniques coupled with product recommendations and predictive lead scoring can be used to drive sales and maximize revenue potential.

Customer-centric campaigns – Businesses now are more digitally-driven which is an added advantage in conversational AI implementation. Organizations’ websites have various customer-centric forms such as feedback forms, inquiry forms, subscription forms, etc which helps them to keep a record of all the data collected. And with the help of conversational AI, organizations can do predictive analytics and get insights in real-time to address the customers with more relevant products and services. Organizations can further segment the customers based on their intent by applying funnel analytics to get a more clear understanding of how their products and services are meeting their customer needs, a segment of customers’ desires, new innovations prospects are looking for and much more.

Market research – Many organizations do extensive market research before rolling out products and services in order to segment their target markets. Conversational AI eases their research analytics models by providing them with actionable insights and assistance on how to deal with various market segments. As conversational AI can automate repetitive tasks without human interventions the possibility of any error is minimal.

Sales enablement – The marketing and sales team collects hundreds of leads on a daily basis from several ongoing campaigns and sources. But only a fraction of them is capable of effectively utilizing the leads to final closure. Leads gathered need to be nurtured until the final sign-off. This process is wearisome but significant for the sales team in order to close a deal. It is widely observed that after an initial contact it takes 3-4 follow-ups by the sales team to get a response from the leads. One sales resource can reach out to one lead at a time which will be quite difficult for companies having big data. AI assistants can be integrated into any existing ERP and other databases to contact each leads in a personalized way and do multiple automated follow-ups using deep learning algorithms in order to qualify them as sales prospects within a short time span, unlike human representatives. These tasks can be easily automated using deep learning to close deals rapidly.

Customer relationship management – Sales CRM contains a wide range of leads information and quite often sales representatives are lost in those records while manually searching for lead details that lead to a loss of considerable time.  AI helps the sales team to dig up old leads collected way back and somehow fallen off track for not communicating proactively. Conversational AI when integrated with sales CRM provides deeper insights on leads’ intent, demography, and other information required by the sales rep to engage with them proficiently. One huge advantage voice-based assistants can provide here is that the sales personnel are not required to be wary of technical know-how to understand the insights provided.  They just need to speak in natural language with the tool and the NLP does the rest. The time and efforts saved by the sales representative can be productively utilized in communication with prospective clients to increase sales closures.

Predicting purchases – Predictive analytics and machine learning can predict customer purchases as the tool is consistently learning from human behavior and can alert the logistics departments about the deficiencies of required materials in their inventory well ahead of time in order to avoid any delay in sales. AI can track the movements of millions of customers on the website at once and decide the demand for definite products and services which encourages the sales team to come up with various promotions on those particular products and services.

Content selling – AI can analyze high volumes of data and track content engagement rates. This can help sales and marketing personnel to prioritize their follow-ups to provide personalized content to prospects at every stage of the customer journey and also share relevant content with the social networks thereby progressing towards a meaningful conversation and a fruitful conversion.  AI and ML can be a great support to the sales team in designing sales campaigns and promotions and allows them to offer feasible sales discounts. AI algorithms analyze past price data such as discounted figures, promotional parameters, and sales history and calculate the price elasticity for certain products and services so that the team can set prices optimally personalized to the customers.

Conversational AI not only reduces human efforts in tedious tasks but also helps the sales team to generate more revenue from the market.