Artificial Intelligence in Investment Banking

Despite deep roots of origin, Capital Markets have evolved with the help of technology and still holds an appetite for innovation and improvement. Capital market firms such as investment bankers have been testing AI for implementation since the precursor technologies.

Before AI got some mainstream attention in the form of self-driving cars and robots, capital market firms have been leveraging machines for their daily operations such as algorithmic trading, quantitative analysis, and market predictions. These facts show how forward the capital market is in the tech race by capitalizing on emerging ideas and leveraging them for value generation for clients.

Though most firms use AI for becoming cost efficient, the potential of AI in the capital markets is beyond imagination and can create value across the organization.

What makes AI stand out from other technologies

Below are the features that make artificial intelligence a desired technology for businesses today.

1) Sense: AI can collect, recognize, sort, and analyze structured and unstructured data in the form of text, audio, and images.

2) Comprehend: Artificial intelligence can then derive meaning, knowledge, or insights by using that data.

3) Act: The comprehension gained is later used to perform a defined process, function or activity.

4) Learn: AI takes real-world experiences into account and evolves over time, thus enabling it to resemble a human brain and handles multiple tasks at once.

High-degree of customized AI interactions.

With hyper-personalization, curation of real-time information, and conversational interfaces, AI delivers enhanced interaction in the form of superior experience to clients. The advent of AI made it possible to cater to a high degree of customization in a cost-effective manner along with flexibility. AI analyzes the clients’ behavior and provides information instantly.

Currently, capital market firms are after storing, categorizing, and analyzing sales and trading conversations to better forecast client needs and enhance the effectiveness of interactions. This is achieved by the introduction of digital assistants handling the sales and services interactions.

Digital assistants are a cost-effective way to deliver a sophisticated and improved experience to the users. It eliminates the need to fill forms, navigate online portals, and the need for additional human resources. The involvement of digital assistants can also greatly improve the client’s acquisition and retention rate.

Intelligent products

AI can help you move up the value chain, access new ecosystems, and introduce innovations in the market faster than ever. AI enables the monetization of new services and products and also makes existing service offerings profitable in new geographical markets.

Enhanced trust

With AI at disposal, trust is enhanced in terms of governance. Compliance, risk, finance, legal and audit are necessary for vigilant oversight. AI provides a cost-effective approach to governance with important insights.

Transparency and traceability should be on top priority for capital market firms who are thinking of building and using AI solutions. Also, most capital market firms that are currently using AI are focusing on automating things that they currently do. But the real value lies when the scope of AI is used to enhance human judgment, to expand products and services, to improve client interactions, and to build trust and confidence among the stakeholders.

Potential of AI in capital markets

The massive potential that AI holds can be encashed in risk management, stress testing, conversational user interface, and algorithmic trading. Also, in recent times, the attention has shifted to client service in the form of next-best-offer and next-best-action decision making.

1) Intelligent automation:

The advancement in technology has made the layering of cognitive capabilities on automation technologies possible, thus enabling self-learning and increasing autonomy.

2) Enhanced interaction:

Curation of real-time information, hyper-personalization, and conversational interfaces have enabled the delivery of superior client experiences.

3) Intelligent products:

New products and services can be launched with the aid of AI along with tapping into new business markets and business models.

4) Enhanced judgment:

Human intelligence can be augmented with AI capabilities and decision making can also be improved.

5) Enhanced trust:

With AI, the whole organization can be kept intact and trust can be flourished outside the organization in how AI is used.

The future belongs to AI

Now is the time when the value of AI should be understood by capital market firms in order to build a base camp for it to flourish now and in the times to come. AI has more potential than just achieving efficiency in the daily tasks and cost-cutting.

The real question is, how you choose to deploy it? 

Fuel your AI engine by redefining your ecosystem and distinctly identifying your source data, internal as well as external. Datasets have a major role to play in the AI world, so be vigilant about which data can be shared and can be monetized.

Set guidelines for your AI

Capital markets out of the many industry lines are the most regulated. As the application of AI will grow in this industry, a new set of regulations will be imposed. With all these constraints, how you choose to use technology and adhere to the regulations will always be a point of discussion. Strong guidelines will help you in the long run. Guidelines that define your data use ethics, information sharing policies, maintain transparency, and privacy.

Final Words

AI is all set to help you improve your business practices and augment your forthcoming ventures. Capital market firms are still in the pipeline to understand the complete worth of AI and all that is at stake. With well-defined guidelines and an appropriate dataset, AI can yield constant and better results on an auto mode. The future is full possibilities and the present is in your hands. Now, it is your call how you choose to direct your future!

Let’s connect to discuss further on how AI can add great value to your business process. Drop us a quick message with your requirements and we will be happy to get on a quick AI consultation call.

How to use Deep Learning for Global Warming

Over the past years, our planet has experienced drastic climatic changes. Global warming is now inevitable as observed by scientists with the help of Earth-orbiting satellites and other technological advances. Since the late 19th century, the planet’s average surface temperature has risen about 1.62 degrees Fahrenheit (0.9 degrees Celsius), a change that has been driven largely by increased carbon dioxide and other human-made emissions into the atmosphere. Most of the warming has occurred in the past 35-40 years, and it is all a consequence of human activity.

Climate change has not only affected the global temperature, but it is also the reason behind warming oceans, shrinking ice sheets, glacial retreats, decreased snow covers, rise in the sea level, declination of the Arctic sea ice and also the acidification of oceans. These issues together cause a global challenge.

The world’s current population of 7 billion will grow to around 9.8 billion by 2050 and this augmentation will lead to an increase in the demand for food, materials, transport and energy and further increasing the risk of environmental degradation.

The important question to be asked now is can humanity preserve our planet for our future generations?

The answer is YES. A new study published in the journal Proceedings of the National Academy of Sciences has found that Artificial Intelligence (AI) can enhance our ability to control climate change.

Artificial Intelligence is defined as the simulation of human intelligence processes by machines, especially computer systems. These processes include the learning process (the acquisition of information and rules for using the information), the reasoning of information (using rules to reach approximate or definite conclusions) and self-correction. AI, in particular, has immense potential to help unlock solutions for a lot of problems.

Artificial Intelligence is a broad term under which come two applications – Machine Learning and Deep Learning.

Machine Learning provides systems the ability to automatically learn by developing computer programs that can access data and use it to learn from them and then apply what they’ve learned to make informed decisions. 

On the other hand, Deep learning creates an “artificial neural network” by structuring algorithms in layers. This network can learn and make intelligent decisions on its own. Deep learning is a subfield of Machine Learning. The “deep” in “deep learning” refers to multiple layers of connections or neurons, similar to the human brain.

How can deep learning help the challenge?

Artificial Intelligence can prove to be a game changer if used effectively. The advancement of technology achieved by AI has the potential to deliver transformative solutions. Some possible ways in which deep learning can be useful for the Earth are:-

1. Weather forecasting and climate modeling

To improve the understanding of the effects of climate change and also to transform weather forecasting, a new field of “Climate Forecasting” is already emerging with the help of Artificial Intelligence. This way of saving the planet sounds very promising since the weather and climate-science community have years of data, in turn, providing a fine testbed for machine learning and deep learning applications.

These datasets demand substantial high-performance computing power, hence limiting the accessibility and usability for scientific communities. Artificial Intelligence can prove useful in solving these challenges and make data more accessible and usable for decision-making.

Public agencies like NASA are using this to enhance the performance and efficiency of weather and climate models. These models process complicated data (physical equations that include fluid dynamics for the atmosphere and oceans, and heuristics as well). The complexity of the equations requires expensive and energy-intensive computing.

Deep learning networks can approximately match some aspects of these climate simulations, allowing computers to run much faster and incorporate more complexity of the ‘real-world’ system into the calculations. AI techniques can also help correct biases in these weather and climate models.

2. Smart Agriculture

Precision agriculture is a technique used for farm management that uses information technology to ensure that the crops and soil receive exactly what is needed for optimum health and productivity. The goal of Precision Agriculture is to preserve the environment, improve sustainability, and to ensure profitability.

This approach uses real-time data about the condition of the crops, soil, and air along with other relevant information like equipment availability, weather predictions etc.

Precision Agriculture is expected to involve automated data collection as well as decision making at the farm level. It will allow farmers to detect crop diseases and issues early, to provide proper and timely nutrition to the livestock. In turn, this technique promises the increase of resource efficiency, lowering the use of water, fertilizers, and pesticides which currently flow down towards rivers and pollute them.

Machine and deep learning help in creating sensors that are able to measure conditions such as crop moisture, temperature and also soil composition that will automatically give out data that helps in optimizing production and triggering important actions.

Smart Agriculture has the capability to change agriculture by changing farming methods and proving beneficial for the environment.

3. Distributed Energy Grids

The use of the application of deep learning in the energy grid is spreading increasingly. Artificial Intelligence can help in enhancing the predictability of the demand and supply for renewable resources, in improving energy storage as well as load management, in assisting the integration and reliability of renewable energy and in enabling dynamic pricing and trading.

AI-capable “virtual power plants” can easily aggregate, integrate and also optimize the use of solar panels, energy storage installations and other facilities. Artificial intelligence will enable us to decarbonize the power grid, expand the use and the market of renewables, thus increasing energy efficiency. The decentralized nature of distributed energy grids makes it more possible for them to be used globally.

Final thoughts

In conclusion, Artificial Intelligence techniques like deep learning can prove to be very useful for the environment in the future if used effectively. After years of damaging our planet, it is our time now to save it for the coming generations.

AI in Diabetes – A breakthrough in Healthcare

The National Diabetes Statistics Report, 2017, U.S. states that an estimated 30.3 million people of all ages or 9.4% of the U.S. population suffered from diabetes in 2015 and this count is growing every year.

These figures are alarming to healthcare authorities as well as to the controlling government. With an immediate attention required in this area, brilliant technologies like AI, machine learning and big data can be used to overcome the gap between those suffering and cured.

The economic cost to the US for diabetes care in 2017 alone amounted $327 Billion. This economic burden is getting out of the control with the number of diabetic patients adding every year.

For contributing to the cause, few digital health companies are taking the initiative to lessen this burden with the help of technology. They are leveraging technological advancements to innovate diabetes care solutions like non-invasive insulin delivery systems, continuous glucose monitoring devices, and digital diabetes management platforms.

These devices are the source of behavioral, physiological, and contextual data which can be analyzed and used to come up with more efficient diabetes care.

Today we are presenting some revolutionary startups who are making a contribution to help diabetes care evolve. Their contributions are remarkable with out of the box solutions for the problem at hand. Let’s take a look:

1) Livongo Health leveraging Big Data-Based Approach for Diabetes Care

Livongo Health is leveraging big data to help people manage their health conditions more efficiently and improve patient outcomes.

Hundreds of thousands of people are using their products such as blood glucose meters, blood pressure cuffs, and scales. The added advantage is that these devices collect data and send it to a larger database which is then used by the company for generating insights to benefit their members.

Also, this pattern has encouraged the startup to come up with a reinforcement learning platform where they observe the data and generate a variety of personalized messages to send to their members.

They learn about members’ behavior with the responses received and eventually know what works best for them. We would call this a good start!

2) Bigfoot Biomedical is working on AI-driven automated insulin delivery with an artificial pancreas

Bigfoot Biomedical, a California-based diabetes management company, is working on a mission to develop an automated insulin delivery system with an artificial pancreas. This system sounds promising and can make the lives of diabetic patients easy. The future of diabetes care will change with this product launched in the market.

The startup made it possible by leveraging the potential of AI to devise a closed-loop system that would observe and learn from the user’s response to food, exercise, insulin, and then adjust the dose.

A good head start is that the company has received a substantial financial support and thus the process of product development has fast-forwarded to the clinical trial phase.

It is just a matter of time that an AI-driven automated insulin supply system will become the life-changing diabetes care solution.

3) Glooko is providing mobile and web apps for diabetes care

Glooko is a global diabetes data management company which provides HIPAA-compliant and widely compatible mobile and web apps. These apps synchronize with diabetes care devices and activity trackers to collect data like insulin, blood pressure, blood glucose, diet, and weight.

The company is harnessing the power of Big data and predictive algorithms to empower diabetic care professional with tools to analyze trends and provide necessary recommendations.

Glooko collects data from over 180 exercise and diabetes care devices and then correlates it with exercise, food, medication, and other relevant data to deliver insights with clinical care and self-management.

These apps will contribute a lot to self-management and also sizeable improvement can be made in patient outcomes.

4) Virta Health is using AI to reverse Diabetes

Virta Health is a silicon valley startup which has embarked upon the mission to cater alternative treatment for type two diabetes without any surgery or medication. The company has already received 50% positive results in its clinical trial for reversing the chronic diabetic condition.

Virta has taken the nutrition centric approach which is based on ketogenic diet. With this diet, the body burns fat for fuel and not carbs. Virta has a user-friendly app which allows the user to enter ketones, blood sugar, and other relevant information.

Once the details are entered, the app uses AI to device a customized treatment plan for the individual.

Additionally, this app helps patients find specially assigned clinicians and health coaches for immediate assistance and consultation. Another great step in improving self-management!

5) Digital Diabetes Clinic by GlucoMe

GlucoMe is an Israeli startup which has invented a digital diabetic clinic which uses a cloud-based solution for remote diabetic care. With this facility, the healthcare professionals can remotely monitor the patient’s insulin and blood glucose and adjust the dose accordingly as and when necessary.

The data is transferred from smart glucose monitors and insulin pens to a mobile app which helps in monitoring and making decisions that support the platform to function.

AI and machine learning are used to generate meaningful insights and actionable treatment plans. The healthcare will be simplified to a great extent with the use of digital diabetic clinic.

Final words

Personalized treatment plans based on real-time data along with intelligent insulin delivery algorithms are the need of the hour. Technical advancement in the field of healthcare has a promising future and startup initiatives like these can open up a gamout of opportunities for healthcare professionals and patients.

The Artificial Intelligence in Music Debate

Music has come a long way from the early days where the only instrument was the human vocal cord. The use of computer-based technology in music started in the 60s when the iconic Moog synthesizers starting taking over the British music scene and paved way to the irreplaceable artform that is progressive rock. If it was not for the Minimoog, Keith Emerson would have just stuck to literally rocking the Hammond L100, riding it like a bucking bronco, brutalizing it with knives and occasionally when he got the time even playing music on it. Yes indeed, the 60s and 70s were crazy times. But the introduction of integrated circuits and eventually processors paved way for a lot of innovation in music and the discovery of many new genres of music.

However, technology in music didn’t stop with the synthesizers. Over the past few decades, many technologies have been incorporated into music including the controversial auto-tune. In recent times though the most technology to make its way into the musician’s arsenal is Artificial Intelligence. While the overuse of technology in music is heavily debated and often shunned by the musicians of yore, there is still a lot of support as well. So, let us have a look at how AI is being used in the music industry today.

Algorithmic Composition

Surprisingly the roots of Artificial Intelligence in music date back to as early as 1965 when synth pioneer and inventor Ray Kurzweil showcased a piano piece create by a computer. Over the years, the use of computer programs in the composition, production, performance, and mass communication of music has allowed researchers and musicians to experiment and come up with newer technologies to suit their needs.

Algorithmic composition is one of the more complex applications of AI in music. Kurzweil’s early efforts towards producing music using computer algorithms relied on pattern recognition and recreation of the same in different combinations to create a new piece. The process of creating music cognitively used today have spawned from this idea. The algorithms used therein are created by analyzing certain human parameters that influence how music is perceived. The first step here is for the cognitive system to understand the music as a listener and then draw vital information from it as a musician would. To achieve this the AI system must first be able to compute the notational data in relation to its audio output. Aspects such as pitch, tone, intervals, rhythm, and timing are all taken into consideration here.

Based on the approach and the result intended there are several computational models for algorithmic composition such as mathematical models, knowledge-based systems, evolutionary methods etc. Each model has its own way of uploading a piece of music into the system, how the system would process it, what information it can derive from it and how it would do it. Composition is not always the purpose of an Algorithmic system. Musicians can use it for comprehension or even draw inspiration.

Non-Compositional Applications

While AI-based music composition is a work in progress that has been going on for decades and could take quite a few more to perfect, AI serves the music industry in many ways outside the studio.

Engagement Tracking

Today almost every form of art and entertainment profits most from the digital medium. This is also the case with music if it wasn’t obvious enough. Artists create music for a target audience which is spread around the many platforms like Youtube and Spotify. So, the engagement data recovered from these platforms have a huge impact on the songs that are created as well as how they are promoted. Artificial Intelligence plays a central role in this process. AI systems often act as the buffer between the distributors, advertisers, marketers and the audience for all their processes including monetization and the transactions involved therein.

Analytics

Analytics data is an essential aspect of almost every online venture today. And music is no exception. Music creators, independent and signed alike rely on analytics data to manage their online presence. AI-based analytics tools help bring speed and accuracy to the analytics processes, so as to help musicians keep up with the fast-paced internet competition.

It’s not Rock and Roll, Man

Music, being an art form that demands a great deal of creativity also demands a human mind behind it that is creative rather than analytical. This is the debate that has been surrounding the AI-music alliance for a long time. Technology, in general, has had the music world divided for eons. In a decade where bands like Pink Floyd thrived exclusively on ‘space age’ technology like the EMS VCS3 and a whole bunch of other stuff, acts like Rush, Motorhead and Aerosmith roughed it out with just the rudimentary instruments. Although most musicians are always open to new technologies or eventually warm up to them, that has not been the case with AI. The prospect of having to put in very less creative effort to compose may entice some, (especially given the volume of music that is being put out everyday) it also opens up the debate of industry giants such as Sony, Universal, Warner and Tips misusing or overusing it which could lead to an eventual vacuum in creativity.

I’m Perfect! Are You?

AI has always been a source of contradiction among all communities so it is no surprise that the music community is both supportive as well as skeptical of it. So far efforts towards harnessing Artificial Intelligence-based composition have been of infinitesimal proportions. So, there is not much out there for us to judge and take sides. Ventures like Artificial Intelligence Virtual Artist (AIVA) are working exclusively towards bringing out the full potential of AI as a means of completely automating music production. While such ideas are a grey area today, how these will come to influence music as we know it, only time will tell.

What you Should know about Google AutoML

One of the highlights of last year’s Google I/O was the announcement of Google’s Automated Machine Learning or AutoML which according to founder Sundar Pichai was their ‘AI Inception’. The Machine learning Cloud software suite is finally about to hit the alpha stage and as of now, it seems like there is a lot that developer and designers alike can leverage from this tool. So let us explore this new addition to the Google family.

What is AutoML?

AutoML is Google’s Cloud software suite for Machine Learning tools based on Google’s Neural Architecture Search (NAS). Using AutoML a user can train deep networks without having any expertise in deep learning or Artificial Intelligence. This is achieved by leveraging NAS to derive the most suitable data network for the task at hand. This is a huge step for businesses as they will be able to harness the power of machine learning without using experts with years of experience in any field of automation. Customization of various AI product models meant for mundane as well as complex tasks.

How Will Businesses be able to Leverage AutoML?

Today, with deep learning still being in its infancy, most business use, AI for simplistic tasks. And considering the fact that AI and Machine learning experts are hard to come by and are quite expensive to hire, most companies try to keep their deep learning efforts to a minimum. This is where AutoML makes a world of difference. One of the most common applications of deep learning for businesses today is Image processing. Today companies rely on Image classification networks that run on neural matrices. This although running on machine learning tools, requires quite a bit of manual effort to isolate the proper network for the image datasets and then train the models to adapt to the task they intend to accomplish. With AutoML companies can skip the tedious processes of research and get directly into the transfer process. As a matter of fact, Google’s first AutoML tool is intended for exactly this aspect for visual companies.

AutoML Vision

This is AutoML’s first tool which will help developers create custom image recognition models. According to Google this tool will allow developers to upload their labeled data and the system will automatically create a custom machine learning model for them. This, in turn, will allow them to focus more on product development, design and other important aspects.  Although still in its alpha stage, this tool is being used by many big-time players such as the animation giant Disney.

Conclusion

As the concept of ‘Software 2.0’ catches on, developers around the world are getting used to the new tools largely built upon their predecessors, to write software without actually having to code each and every part of it. Tools like AutoML are a good example of how this is being applied to every aspect of software irrespective of the purpose. With AI the scope is further enhanced. Yet, for such tools to achieve any degree of comprehensiveness, they themselves need to be further developed to accommodate the needs of the upcoming generations of developers who will be relying upon them to do most of the work. So the road ahead will be the chicken and egg paradox where the efforts of both the man and the machine will craft the future. Today we have that sense of direction as to where we need to focus our efforts in terms of research, which is great news for AI tools such as AutoML.

A Brief Look at the Role of Artificial Intelligence in Logistics

Modern businesses rely a lot upon a good logistics system. Keeping up with the sheer volume of business that companies deal with today doesn’t make things any easier. This is where all the automated goodness of AI plays a major role as a disruptor. Given the expanse of functions within the logistics industry, it is no doubt that there is a lot that AI could do and to a great extent is already doing.

For the logistics industry, the journey has been long and eventful, starting in the early days of pioneers looking for trade routes to procure tea and spices. At this juncture of the 21st century, however, logistics has evolved to its fullest as far as infrastructure is concerned. Furthermore, there are very few industries today that does not apply logistics in one form or the other. There are quite a few aspects of logistics that AI could be applied to. Here are a few areas where artificial intelligence is applied in logistics.

Automating Transport Management

The heart and soul of the logistics industry is transport. Without transport, every other process in logistics is just redundant. While the 20th century could make do with paper and ledger based management systems, transport in the 21st century requires a giant leap. This is where AI’s role is pretty cut and dry. Before getting into AI’s role in transport management, we need to address the current scenario surrounding this technology. Transport being a rather physical function, most logistics companies address it as such and consider it quite a priority and with good reason. There are several hassles and risks involved with transportation which go beyond the pen and paper strategies devised in offices. This is the reason why most companies leave the data based technological bits to third-party logistics companies or 3PLs. This has slowed down the widespread adoption of technology in general.

This trend of outsourcing technological details to 3PLs has also limited the influence of mobile technology which plays a huge role in AI-based applications. So, coming back to automation, the advancements made in optimizing transport management has been monumental. As far as AI in transport management is concerned, the idea is to automate as much of the entire process as possible. And to this end, there are quite a few remarkable devices and applications out there.

1. Live Tracking

The fast pace at which the world moves today demands fast systems for anything and everything to cope with our lifestyle. In logistics live tracking has been quite a disruptor. There are several automated tracking applications for all platform that collect and relay a lot of vital data. From scheduling trips to providing live updates of the cargo’s position, AI-based applications ensure an open and intuitive line of communication between the bases and mobile units. Even information such as traffic, potential delays and so on are relayed using predictive analysis. Furthermore, advancements in the mobile industry and the level of simplicity they have facilitated therein have inspired various businesses to onshore many of their processes pertaining to this. Online retail companies such as eBay, Amazon and so on have already adopted this technology on a rather comprehensive level with their management capabilities going down to the door level.

2. Advanced Black Boxes

Black boxes in trucks have received an overhaul as well. Advanced back boxes provide a great deal of support for the operating companies as well as drivers. These devices are capable of collecting information such as engine temperature, the status of the load, tire pressure and so on. Despite their fundamental purpose as a disaster recording tool, these black boxes make this data analyzable in real time and allow operators to monitor and manage their transport systems well. Similar advancements have been made in the systems of cargo ships and airplanes along with advanced automated navigational tools.

WorkFlow and Process Automation

While transport is a major aspect of logistics, it is still just a part of a larger process comprised of several smaller processes. These are the processes where AI could manifest into more cognitive and at times even physical forms. There are various manual processes which take thousands of man-hours to get through, and after all that could still yield various levels of errors. Here are a few of those processes that have been optimized by AI.

1. Warehouse Management

Ask anyone who has worked in a warehouse and they will tell you how hectic the schedule there is. The volume of loading and unloading that take place alone is enough to drive Rain Man and John Nash crazy. Loading and unloading then again are just the tip of the iceberg. AI in warehouse management has simplified many of these processes. The use of AI-based sorting and labeling systems have allowed logistics companies to speed up these processes and minimized the margin of error.

2. Scheduling

The AI systems in logistics today are also capable of scheduling various tasks within the entire process. Automation of planning and scheduling is something that has revolutionized logistics. AI systems, based on various warehouse itinerary data schedule the transportation, organize pipelines for cargos, assign and manage various employees to particular stations and so on. Apart from the obvious con of potential layoffs, this particular application of AI in warehouses has facilitated a faster network for logistics processes.

3. Robots

While AI’s application in logistics has been in a minimalistic way to various mundane tasks, one of the most pronounced applications has been that of robots. Despite the constant opposition from various scholars and trade unions, robotics has been applied to a great extent in all fields and logistics has not been spared in the least. From working in warehouses to transferring freight at cargo airports, robots have their work cut out for them and they are doing it at speeds their flesh and blood counterparts could never achieve.

Conclusion

Despite the slow rate of progress in adopting AI as a logistics tool, the technology is slowly but surely seeping into the system. Machine learning has further allowed investors with a broader outlook on how AI could revolutionize logistics just as it has done in many other verticals. The issue that surrounds AI now is not lack of optimization but the dilemma of having to divert time towards setting it up. This in today’s context makes sense as most companies shy away from investing in newer technologies unless a dire need for it is presented.

How AI is Used in Business Development and Sales

We’ve been hearing the words artificial intelligence for a long time. For millennials the earliest memory of AI would be an Austrian accent uttering the words ‘Come with me if you want to live,’ and ‘I’ll be back’. Over the past decade, AI has been around everywhere and has contributed to a great number of advancements in many verticals. Through that period one vertical remained virtually unaffected by the AI scourge – Business Development and Sales. All that however changed in early 2016 when the industry adopted AI as a mainstream disruptor. In business and sales, AI serves a singular purpose, sorting, analyzing and processing data. Automation of data related processes in various aspects of sales has been enabled a huge increase in productivity as well as the introduction of several sub-verticals to effectively conduct business.

Virtual Sales Assistants 

One of the advantages of using AI in sales is the prospect of increasing productivity without increasing or investing in additional workforce. One of the ways this is achieved is through the use of virtual sales assistants. These programs can make use of sales data to generate leads and then contact the leads through emails. Technologies such as natural language processing and natural language generation have played a huge role in defining virtual sales assistants.

Furthermore, predictive analytics have enabled businesses to effectively automate processes such as lead generation. Efforts by some of the leading sales data companies have yielded great results which have fetched data to such comprehensive depths that was never previously achievable even with traditional methods of automation. The amount of accuracy, as well as the details offered by the use of AI, have enabled sales representatives to improve their efficiency by large margins and also make the most of the data provided to them. It is as though every single lead given to them is a Glengarry lead.

AI in Customer Relationship Management

CRM is an intricate part of sales that uses a great deal of information on customers. This process governs both the possibility of new customers as well the prospect of retaining current ones. Which of course means a thorough understanding of individuals in either demographic. While in the past a lot of effort has been put into gathering information on clients, it has not been quite enough and relied purely upon the wits and talent of the sales representative. So, for quite a long time it has been a touch and go process. Today however with AI in the picture, CRM has been kicked into overdrive. With each interaction, AI systems are able to gather vital data that can be used to enhance the entire process and so far it has been working out quite well.

Enhanced Customer Service

Customer Service is a territory where AI has been almost creating miracles for companies large and small alike. There is a lot that artificial intelligence has to offer to various customer service processes. Both on the business-to-customer as well as the business-to-business fronts AI has been able to engage with the customer on a much deeper level than ever before. Using the cognitive technologies available today, devices such as Amazon’s Echo and Google Home have been able to reach out to customers on a personal level, understanding their preferences and programming themselves to them. Through machine learning, these devices are capable of answer more complex queries as well as predict other contingencies. The use of chatbots is also something that has enhanced the customer service experience. The speed at which customers’ queries and needs are solved have made it quite desirable for companies to rely upon AI. A good customer service experience also reflects on the other parts of the sales funnel including the ones implied above.

The Downside?

As with every AI discussion we have to always address the potential of complete automation and the resulting loss of jobs that would bring about. So will AI based automation in sales cause sales representatives to lose their jobs? At this point, the answer is ‘no’. The use of automation in sales could actually decrease the workload for sales reps. The many hours they spend on market research and data collection once automated could allow them to focus more on lead conversion. Even with automation of leads conversion processes, the salesperson will have fill in managerial and supervisory roles. While minor layoffs owing to the redundancy of certain human roles are inevitable, there is no end in sight as of now for sales reps. Besides most customers still, prefer the human touch with such processes and may continue to do as we have experienced after dealing numerous times with automated query systems.

Is Artificial Intelligence in Government a Good Idea?

It may be hard for most of us to believe but there used to be a time when people were actually scared of machines with cognitive abilities, and that magical period was called the 60s. If you were living in the 60s this subject would have sparked so much debate and controversy. Well at that time so did talk of aliens, women’s rights, racism and going to school. Hey, wait a minute that stuff happens today as well, so I must tread carefully. Yes indeed the 60s were fun times, but what’s not strange is the reality that the robots are here and by the looks of things it seems they might be starting to govern us already. Things are escalating quite fast indeed, the good news here is that we will all be still alive to see the rise of the machines, and that’s the bad news as well.

What Process in Government Could AI Take Over?

Beyond the obvious stereotypes, there are a lot of processes within the government that happen on an unfathomable scale. The fact is unless you are going through that process you wouldn’t realize the sheer volume of documents government agencies process every day. We often forget the fact that the government deals with documents of millions of people and most of the time these are in hard copy format. It is no wonder that government employees are grumpy all the time. If we were to break down the government’s functioning, it would take us days to cover all of them. So, for the sake of brevity, we’ll narrow it down to three major functions.

Automated Document Based Processes

At the heart of any public sector are the processes revolving around documents. And as mentioned earlier there are a lot of them. So, most of the jobs in government are based on document processing. This is a repetitive process with a certain mechanical edge to it. The rather mechanical nature of the process makes it optimal for automation. Machine learning is a technology that could be applied quite effectively here to prepare the systems for any contingency. Again, as with most machine-based processes, the margin of error is largely reduced. However, there are concerns over how well an automated system could be used to process handwritten documents. Not everything today is in digital format and the use of good old pen and paper are still quite popular and in a government agency, an AI system will have to face such tasks often. Natural Language Processing has its own limitation and the range of variety in handwritten documents could prove quite challenging for even the most advanced AI systems.

Predictive Analysis

This is a technology that is in particular useful in fields such as Income Tax where detection on various levels is mandatory. While filing taxes, it is quite common for people and companies to cheat, which cannot be determined using traditional methods. Machine Learning again plays a major role in coming up the right algorithms to process all such data, particularly ones that are based on Natural Language. Natural Language Processing is a vital part of such government processes. However, the threat here again is the AI’s limitations. Here if the machine misreads information it could cost dearly as people could be convicted of an error the machine made. Ironically this is not unprecedented. Weak AI in the 90s have been applied to a great extent and has lead to plenty of dispute and criticism. With access to IoT today we could run documents through several stages of cross-checking to ensure that nothing is missed out or misinterpreted.

Sensor-Based Technology

This is where AI stops being a helpful tool and starts bordering on the level of menacing. It is often debated whether or not the use of AI could replace humans. When AI really takes over on a mainstream level the first jobs to go will probably be on the maintenance and security levels. As far as maintenance goes we already have several automated devices that work 24/7 with various tasks both on the domestic as well as enterprise levels. However, the use of AI in security has largely been on a monitoring level with visual, touch as well as audio cues being constantly tracked and later processed. That is not saying that the use of automated physical security is unprecedented. There have been several instances where the use AI in security has gone beyond the mere monitoring level, particularly in defense services. However, as far as the government is concerned the requirement for such security has not yet arisen with the exception of the postal workers going ‘postal’ back in the 90s. But that was an internal strife and there is no man-made machine that can stop the wrath of the postmen. Anyway sensor based security in government could be used in the widespread implementation of tracking services such as the RFID chip and biometric transaction/enrolling services.

Conclusion

While the potential for AI in government yet again is quite vast, adoption in this sector is not as swift as other verticals. This is quite understandable considering how slowly digitalization seeped into government and the process is not yet complete in developed countries let alone countries that don’t yet have widespread internet access. So, AI in government is something that could bring about huge changes along with massive layoffs, but the time it would take for such an event to come to pass would be much like waiting at the ATMs back in 2016 when the generous government of India decided to alleviate all our monetary issues. What fun times we live in indeed.

Artificial Intelligence’s Role in Customer Service

Customer service has always been a vital aspect of any business and often dictates the manner in which a company is perceived in a market. For today’s businesses, however, customer service is a much more important process which also serves as a comprehensive part of their sales and marketing strategies. Being a continuous and often mechanical process, most aspects of the customer service process are being automated. A study by Oracle from 2016 reports that approximately 8 out of 10 businesses in the world today have either switched or are planning to switch to Artificial Intelligence for their customer service needs by the year 2020. The recent developments in AI have largely attributed to a surge in investments into the technology. For customer service, there are various aspects that today apply AI and automation on some level.

AI beyond Chatbots

The most common features of any website today are chatbots. Chatbots are those small pop-ups that appear when you log on to a website assisting you with the basic queries. As of now, that is the extent of their purpose. Responding to FAQs from a pre-programmed set of answers and contingencies, Chatbots could be considered the beginnings of Artificial Intelligence, although calling them that would be a gross overstatement. Yet, chatbots have allowed companies to improve the efficiency of their customer service teams. Being available 24/7 they offer an edge for companies in maintaining a strong presence in the market. No matter how advanced AI may be than chatbots, it is meant to serve only these two purposes and all AI designed for Customer Care function along these lines. However, AI adds layers to those functions often allowing for several functions that involve augmented AI-Human coordinated efforts.

AI-Assisted Human Agent

This is a model that companies follow as an alternative the Chatbots. This model offers the best of both worlds as human agents work hand in hand or rather hand on the mouse. Here the AI serves as a relay between the customer and the human agent. Using data and predictive analysis the AI analyses various previous conversation pertaining to the query in question and generates a custom answer based on the current inputs from the customer. The human agent then edits the answer to make it sound more ‘human’ and delivers it in a simple and understandable manner. After each query is answered the AI learns from the edited answer offered by the agent to enhance its own database the next time it answers. The advantage of this model is its versatility across platforms as well as media.

Unlike chatbots, this model is not limited to text-based queries and can function on a voice level as well. By applying Natural Language Processing, the AI recognizes human features associated with a voice to authenticate calls as well as make suggestions. It is predicted that in the next couple of years a healthy percentage of companies using voice call based business processes will be enhanced by the use of AI.

Swift Response AI

If we had a rupee for every time we refused our patronage to a service because of bad customer care, all of us would be quite rich indeed. In the digital age, quick and efficient customer care is an essential need and people lose patience and loyalty towards a company when they are made to wait and put through a ton of procedures. The AI today are capable of interacting directly with customers for any requests pertaining to information or service and by drawing from a database, offers appropriate suggestions and processes them. If there are queries that are beyond the AI’s processing capabilities it automatically initiates the process and redirects the customer to the concerned human agent. This swift AI servicing is proving to be quite effective.

AI-Derived Insights

One of the most comprehensive applications of AI in customer service has been data analysis. Where humans often fail in general is when we are expected to see the big picture. We have done this for centuries and it seems we never learn. But, AI today is able to grasp each and every aspect of a conversation, transaction or interaction of any kind and process it to derive actionable insights. By using technologies such as natural language processing and machine learning, AI will thoroughly analyze all data available and checks for anomalies and vital information such as trends and patterns which could be used in the future to improve conversion rates and so on. While not used as widely as the previous two, this form of AI has been quite effective as a business accelerator and it is predicted that with the growing popularity of AI it will eventually become a customer service staple.

Conclusion

While the application of AI in customer service still has a long way to go, the changes it is has brought about so far have been substantial. Despite the ‘Don’t fix it, if it is not broken’ attitude that is symbolic of the customer service market, there has been quite a push for the adoption of AI. Given the various possibilities that AI presents, the customer service industry, for now, is in a safe haven with this technology by their side.