The buzzword among the business and tech communities in China for the past year has been ‘AI’, or artificial intelligence. Artificial intelligence, which allows software to “learn” human ways of thinking, is being incorporated into the largest e-commerce platforms, including Baidu, Alibaba, and Tencent, as well as into data-intensive traditional sectors. With strong government backing and concentrated research in this area, AI is poised to drive China’s economy forward toward higher levels of growth.

China is developing artificial intelligence in improving the capabilities of robotics, developing driverless cars, divining consumer preferences, inventory forecasting, selling enhanced products, and marketing goods and services. According to Liu Lihua, Vice Minister of Industry and Information Technology, China has thus far applied for 15,745 AI patents.

China plans to launch a national AI plan, which will strengthen AI development and application, introduce policies to contain risks associated with AI, and work toward international cooperation. The plan will also provide funds to back these endeavors. Some municipalities also support AI research programs. Beijing, for example, is home to the CAS Institute of Automation, a consortium of universities and firms that provides venture capital funding of 1 billion RMB ($150 million) to AI development. Zhejiang province has also embraced AI programs. Already, Geely Automobile in Zhejiang is using intelligent manufacturing and internet marketing services based on AI to boost sales.

BAT – Chinese AI Frontier Giants

China’s BAT, or Baidu, Alibaba and Tencent, is leading the way for AI in China. Baidu was the first Chinese company to embark upon research in AI, using a system known as Duer to be used in home devices and driverless cars. Driverless auto software provided by Baidu will be made available to car manufacturers under the Apollo Project. Alibaba is using AI to forecast regional order quantities and to improve logistics efficiency, while Tencent has released a platform for deep learning using social data.

Baidu, Alibaba and Tencent have been vying for top talent in AI in order to become leaders in this area. Making headlines several days ago, Alibaba lured Ren Xiaofeng from Amazon.com to lead its own technology lab, which aims to make headway in artificial intelligence. Tencent brought Baidu’s AI expert Zhang Tong on board in March. In 2014, Baidu poached Andrew Ng from the Google Brain project to lead the Baidu Research Institute (though he recently stepped down).

Bay Area dominates this year’s AI funding

Venture investment in startups that are applying artificial intelligence or machine learning has more than tripled in the U.S. since 2013, according to PitchBook Data, with about 60 percent of that coming to founders in the Silicon Valley Bay Area.

The Seattle investment research firm put together a ranking of the top 20 AI deals done around the world this year for me while I was researching this week’s Silicon Valley Business Journal cover story. Almost half of the startups that were funded and nearly three-quarters of the investors involved were from San Francisco and the Silicon Valley region.

The new era in Silicon Valley centers on artificial intelligence and robots, a transformation that many believe will have a payoff on the scale of the personal computing industry or the commercial internet, two previous generations that spread computing globally. Computers have begun to speak, listen and see, as well as sprout legs, wings and wheels to move unfettered in the world.

Silicon Valley’s financiers and entrepreneurs are digging into artificial intelligence with remarkable exuberance. The region now has at least 19 companies designing self-driving cars and trucks, up from a handful five years ago. There are also more than a half-dozen types of mobile robots, including robotic bellhops and aerial drones, being commercialized.

Funding in A.I. start-ups has increased more than fourfold to $681 million in 2015, from $145 million in 2011, according to the market research firm CB Insights. The firm estimates that new investments will reach $1.2 billion this year, up 76 percent from last year.

Even Silicon Valley’s biggest social media companies are now getting into artificial intelligence, as are other tech behemoths. Facebook is using A.I. to improve its products. Google will soon compete with Amazon’s Echo and Apple’s Siri, which are based on A.I., with a device that listens in the home, answers questions and places e-commerce orders. Satya Nadella, Microsoft’s chief executive, recently appeared at the Aspen Ideas Conference and called for a partnership between humans and artificial intelligence systems in which machines are designed to augment humans.

The auto industry has also set up camp in the valley to learn how to make cars that can do the driving for you. Both technology and car companies are making claims that increasingly powerful sensors and A.I. software will enable cars to drive themselves with the push of a button as soon as the end of this decade — despite recent Tesla crashes that have raised the question of how quickly human drivers will be completely replaced by the technology.

AI is in it for the long-haul

Whenever there is a new idea, the valley swarms it. But you have to wait for a good idea, and good ideas don’t happen every day. Silicon Valley’s new A.I. era underscores the region’s ability to opportunistically reinvent itself and quickly follow the latest tech trend. This is at the heart of the region’s culture that goes all the way back to the Gold Rush. The valley is built on the idea that there is always a way to start over and find a new beginning.

Artificial intelligence is transforming our lives at home and at work. At home, you may be one of the 1.8 million people who use Amazon’s Alexa to control the lights, unlock your car, and receive the latest stock quotes for the companies in your portfolio. In total, Alexa is touted as having more than 3,000 skills and growing daily. In the workplace, artificial intelligence is evolving into an intelligent assistant to help us work smarter. Artificial intelligence is not the future of the workplace, it is the present and happening today.

Investment in AI has accelerated from $282 million in 2011 to $2.4 billion in 2015, a 746% increase in five years. In 2016, this continued to increase with roughly another $1.5 billion being invested in more than 200 AI-focused companies in 2016. AI is becoming indispensable in the healthcare industry. Will we be saying the same thing about CHROs using artificial intelligence in the workplace? Will we consider it unthinkable not to use intelligent assistants to transform recruiting, HR service centers, and learning and development? I believe the answer is yes.  HR leaders will need to begin experimenting with all facets of AI to deliver value to their organizations. As intelligent assistants become more widely used in our personal lives, we will expect to see similar usage in the workplace.

For employees, chatbots deliver an unmatched level of employee experience, from real time answers for HR questions to personalized learning and development. In addition, they are critically important to the 3.7 million workers, or 2.8% of the workforce, who work remotely at least half time and do not have easy access to an HR department.

For HR leaders, chatbots are well suited to improving talent acquisition and on-boarding processes by increasing speed and providing greater consistency in answering frequently asked HR questions, improving the talent acquisition process, and enhancing the online learning experience.

AI Chatbots To Answer Frequently Asked Employee Questions

Let’s consider Jane, a chatbot created by Loka, in 2014. Jane provides real time answers to a range of HR questions, including, “Are we off on President’s Day?” or “What are my dental benefits?” Jane is capable of answering any question and answer set that can be stored in a database. In addition to answering frequently asked questions, CEO Bobby Mukherjee says Jane is designed to proactively promote benefits to employees they may not yet know about. Says Mukherjee, “Companies are coming up with lots of new benefits, but they do not have an effective way to promote usage.” Imagine Jane can reach out to employees with, “Hey John, have you tried our Yoga class that we are offering in your building today at 3:00 pm? Click here to automatically book yourself. You’ve been working hard and you deserve it!”

Another value of Jane is the opportunity to track employee issues using real time analytics and then apply sentiment analysis to address these issues. Let’s say that a majority of employees are asking questions about late payments for travel reimbursements. This data can indicate something in the system isn’t working correctly. Before things become a full blown issue, HR leaders can uncover the issue and communicate a solution.

Granted there will be questions Jane cannot answer yet, but the opportunity is here to provide AI for all types of HR related questions that might be coming into your HR Service Center.

AI  To Improve Talent Acquisition 

Talent acquisition and new hire on-boarding are ripe areas where intelligent assistants can tap multiple data sources to develop candidate profiles, schedule interviews, and make decisions about prospective job candidates.

Talla is a chatbot designed to augment the HR processes that source job candidates. Talla can provide a set of interview questions based upon the role, and can even conduct a Net Promoter Score survey following the recruiting process. Rob May, CEO of Talla, sees, “an intelligent assistant as being able to augment a mid-level HR professionals’ job so she can focus on more strategic HR issues.” The vision behind launching Talla is to ultimately become a real time advisor to HR professionals in how they source and on-board new hires.

May estimates that Talla will save many hours in recruiting and on-boarding new hires and will greatly enhance the employee experience. Improving talent acquisition and new hire on-boarding is a priority for CHROs. According to Eric Lesser, Research Director of IBM Institute for Business Value, “More than half of the CHROs surveyed believe cognitive computing will affect a wide range of roles in the HR organization, ranging from senior executives to individuals working in service centers.”

AI for Talent Management Inside Workplace

AI can also be used for intricate data collection of employees in their workplace in an automated fashion. One option would be to track the movements of the employees inside office space to assess their activities on ground. Companies can track employees’ whereabouts in the office. Bluvision makes radio badges that track movement of people or objects in a building, and display it in an app and send an alert if a badge wearer violates a policy set by the customer—say, when a person without proper credentials enters a sensitive area. The system can also be used to track time employees spend, say, at their desks, in the cafeteria or in a restroom.

Bluvision’s AI compensates for the margin of error in determining location of radio transmitters, allowing the system to locate badges with one-meter accuracy, according to COO John Sailer. Without it, people near one another would be indistinguishable, and the positions of doors, desks, walls and the like—useful information for security and optimizing use of space—would be blurred.

The system is also useful in situations where contractors are paid hourly or piecemeal, such as on a construction site, where subcontractors must complete work in order and on schedule to avoid cost overruns. Although Bluvision tracks individuals, it can also be set to present only aggregate trends. That allows customers to take advantage of location tracking without breaking privacy laws or agreements protecting personally identifying information about employees.

The limits of AI Currently

For all their promise, these systems raise a number of issues. Some are evident today, in the early stages of adoption, while others may take time to become clear.

Privacy is an obvious concern when tracking employees, particularly personal behavior. Systems that sort job candidates also raise questions. Entelo’s may emphasize people with a large online footprint; SAP’s might prefer those who best match characteristics of people who were hired in the past.

Also, the limitations of current approaches boil down to the difficulty of drawing valid conclusions from incomplete data. For instance, measurements of employee performance at any given company are based on the set of people hired and lack information about candidates who were passed over—or weren’t even interviewed—who may have, say, produced more in less time. Aggregating data from many customers, as some larger vendors including SAP and Workday do, can reduce bias, but the problem remains that different companies may not track the same variables in the same way, and subtle but important ones are likely to be missing.

Despite nationwide venture funding hitting a multiyear low, venture capital deployed to artificial intelligence (AI) startups has reached a record high.

Last year, VCs struck 658 deals with AI companies, nearly five times the number that signed on the dotted line four years before. To date, the market contains 2,045 AI startups and more than 17,000 market followers, with more joining by the day.

AI’s rapid rise has swept up startups and enterprises alike, including U.S. automaker Ford, which recently bought AI startup Argo for $1 billion. The acquisition cements experts’ suspicions of Ford’s coming foray into self-driving technology. Other startups — so many, in fact, that entrepreneurs need a “best of” guide — are betting heavily on bot platforms.

So while we’ve just glimpsed the tip of this innovation iceberg, it’s clear AI is no longer some nebulous technology of the future. Sixty-eight percent of marketing executives, report using AI in their operations. For a technology that only went mainstream in 2016 and barely existed four years ago, that’s a remarkable adoption rate. How, regardless of the platform you choose, can you join forward-thinking entrepreneurs and build your business with AI? Over the last few years , I have worked closely with multiple start ups across genres and ,So far, four ways stand out:

1. Get to know your next customer.

A politician wouldn’t dream of delivering a small-town stump speech to her urban constituents. Why? Because you’ve got to know your audience. The same is true for entrepreneurs. Before you broadcast your message, you need to know who you’re trying to reach.

Node, an account-based intelligence startup, uses natural language processing — a fancy term for teaching a computer to understand how we humans speak and write — to develop customer profiles. Node is crunching vast swaths of data to connect the dots between marketers and the companies they’re trying to reach.

Once you have ample customer data — Node uses data crawlers to scrape information from social media, news sites and more — pair machine learning and natural language processing models to extract sentiments from unstructured data. Then, just as senators segment constituents into demographic groups, Node uses cluster analysis to sort clients’ customers into like cohorts.

2. See how people truly use your product.

If, heaven forbid, you forgot to tag your neighbor at last week’s house party, Facebook was no doubt there to remind you of your error. How does Facebook know which of your friends you left untagged? It has gone all-in on an AI technique called convolutional neural networks.

Convolutional neural networks, which loosely model how the brain’s visual cortex interacts with the eyes, work by separating an image into tiny portions before running each of those specks through a multilayered filter. It then “sees” where each speck overlaps with other parts of the image, and through automated iterations, it puts together a full image.

Many different ways exist to apply this technology, but retail businesses can start with image classification. Try using a convolutional neural network to break down photos of your products posted online. The model can identify customer segments that frequently use your product, where they’re using it and whether they commonly pair other products with yours. Essentially, this automated image analysis can show you how your products fit into customers’ lives, allowing you to tailor your marketing materials to fit.

3. Get inside the user’s head.

Success on social media requires careful listening and quick action. When a social campaign isn’t working, it’s best to put it out of its misery quickly. On the other hand, when one strikes a chord with customers, doubling down pays dividends.

But to do so, you need real-time insights about customers’ reactions to your content. Fortunately, AI can take the emotional temperature of thousands of customers at once. Dumbstruck, a video-testing and analytics startup that I advise, has added natural language processing to its emotional analytics stack. This allows it to provide moment-by-moment insights into viewers’ reactions to media. Dumbstruck’s model grows stronger with each reaction analyzed, producing a program that perceives human emotions even better than some people can.

4. Provide affordable, always-on support.

Customer service is — or should be, according to consumers — the department that never sleeps. More than half of people, 50.6 percent to be precise, believe a business should be available 24/7 to answer their every question and concern. When asked whether businesses should be available via a messaging app, the “yes” votes jump to nearly two in three.

  Fortunately, bots don’t sleep, eat or go off-script. A well-built bot can offer cost-effective, constant customer service. Of course, grooming your bot to serve customers requires front-end data — ideally hundreds of thousands of example conversations — but you can get started with a human-chatbot hybrid. With this approach, the bot answers run-of-the-mill questions, while a human takes over for the more complex ones. Then, as the data builds and the model matures, you can phase in full automation.

AI’s Impact on small businesses and startups

Small enterprises will begin to use the tried and tested platforms in innovative ways. While startups will gain a competitive edge in capturing the AI market, the larger enterprises will provide the infrastructure to startups for building innovative services. It is somewhat similar to the business model followed when the cable technology was introduced.

Startups leveraging AI technology for industry verticals, like agriculture, manufacturing or insurance are bound to be successful.

Startups can empower established insurance companies like State Farm, Allstate and Farmers with technology enabling them to become more proactive in policy planning. For instance, a new AI insurance underwriter will help to forecast natural disasters and accidents, and adjust premiums. 

The predictive decision-making capabilities are more than just a novel technology. You can manage food supply chains with the help of AI. Startups could develop end-to-end farming solutions with AI analytics for reducing food waste. It will have a huge impact in tackling global issues of hunger and famine.

Whether serving as a research assistant in a large corporation, acting as a voice-activated resource in difficult medical procedures, AI is fast becoming a reality. The AI revolution will benefit new players who learn quickly to use it to their advantage. AI will be a fundamental predictive enabler helping us solve large-scale problems, and startups are poised to gain a competitive edge.

So what’s the ground level AI sentiment of Startups? – Mix of Hope & Fear

Regardless of which industry you operate, be careful that AI will affect your world in some way. Look into what is present now and how you can utilize it to gain a competitive edge.

The possibilities with AI are endless; enterprises will become efficient, intelligent and cost-effective.  

Undoubtedly, the digital revolution and AI will advance to a point where it will offer real-world benefits to every business- large and small.

Mark Zuckerberg says, “We’re working on AI because we think more intelligent services will be much more useful for you to use.”

AI is relevant because of its immense power to deliver useful solutions; its other building blocks including cloud computing and superfast connectivity. But, if you want to take advantage of this novel technology you will need a reliable, secure, and continuously evolving infrastructure.

The Goods and Services Tax has come into effect on July 1st and is pegged to be one of the most significant economic tax reforms carried out by PM Narendra Modi. While it will usher in greater transparency and create a simplified channel for tracking through data, it has also spawned the need for ERP and data analytics solutions. Other IT solutions include building capabilities such as billing software and payment gateways, thus creating plentiful opportunities across the IT spectrum. Industry experts say there is a $1 billion opportunity for IT vendors over the next two years.

According to an industry expert, GST will a) remove tax barriers in a fragmented market b) will introduce a transparent and predictable tax regime and boost local and foreign investment in India c) integrate existing multiple taxes into a single GST.

GST – A Data Analytics Powerhouse

In terms of data analytics, the GST rollout is expected to become a “data analytics powerhouse”. According to Goods and Services Tax Network, a not-for-profit organization operated by the government and private players jointly, GST will give enormous amount of data to the tax department to work with, that will eventually rule out discrepancies and help tax sleuths to go after tax evaders. Once sufficient amount of data is generated, GSTN will be able to generate analytics based on the requirements of various stakeholders. Companies in the coming time are expected to build programmes and analytical tools as per the data requirements of both central and state tax departments. The data generated could be on real-time basis, if not near real time.

According to GSTN, the body is building the “information technology backbone for the goods and services tax (GST)” and implement analytics solutions. Here are the features:

• The platform is expected to store information related to relevant transactions
• Based on the data filed by millions of taxpayers that will migrate to the system, analytics will help in identifying leakages and ensure more focused economic-policymaking.
• As per the GST system architecture, the decision-making will be based on data rather than assumptions
• The system shall feature more meta tags so that the time taken by various functions in capturing/entering the data is verified.

Nab Tax Evaders, Boost Domestic IT Biz

The data generated through the technology backbone of the Goods and Services tax regime would, over time, be able to solve issue such as tax evasion and help compliance ratings in the country, according to the GST Network chairman. Navin Kumar, chairman of the GSTN, the entity that handles the information technology backbone of the GST, said that GSTN would soon have enough data to be able to run business analytics and find meaningful ways to interpret and help make sense of the filings in tandem with other government departments. So there is great potential for that (leveraging analytics), but that will be possible only once they have data, maybe after two or three years. GSTN will start developing the applications for that next year.

Among the potential use cases for business analytics, Kumar said GSTN would look to do a rating of the taxpayers, such as a compliance ratings, look at sectoral studies and detection of tax frauds and tax evasion. There, collaboration with income tax will be very useful, to see whether the volume of business reported here (GSTN) is reported to income tax or whether that data syncs with their data. Existing analytics tools available in the market could be used, as well as some new applications that would be developed by IT / Analytics Companies.

According to research firm Gartner, Indian business intelligence software revenue is forecast to reach USD 245 million in constant currency in 2017, a 24.4 percent increase over last year.

GST a boon for small and medium IT cos. The new tax regime would prove to be a boon for the small and medium IT companies in the country. They will have lot of opportunity to provide solutions to businesses, not just become GSPs (GST Suvidha Provider). And the wider roll-out of GST has spawned many opportunities in IT, such as developing ERP packages for the 5 million SMBs that are not yet digitally-empowered. SMBs need to record the GST transactions, upload invoices and do the return filing. This spells a big opportunity for IT vendors who are quick to fill the gap with their enterprise ready solutions. According to news sources, the government expects close to nine million returns to be filed in the first month of its roll-out.

These companies could also develop the functionalities or applications that could help GSPs better. For example, the small and medium IT firms could develop an invoicing system for taxpayers, software for inventory management, and so on, which would provide a boost to the domestic business of the IT services companies.

Here’s a look at some enterprise ready solutions:

SAP HANA:

Earlier in the year, SAP announced ‘GST in a Box’, an all-inclusive solution portfolio, to help Indian organizations of all sizes and across industry verticals to become GST compliant. The solution It also enables organizations to effectively manage suppliers, customer engagement and supply chain in the new tax regime. According to Neeraj Athalye, Head, S/4HANA & GST Adoption Drive, SAP India, businesses need to go digital.  “Out of an estimated 4-5 billion invoice uploads that will happen every month, since more than 40% of transactions will pass through an SAP system, it is upon us to not only help Indian corporates swiftly get compliant with this new law, but also ensure that businesses benefit from the GST vision,” he said.

Microsoft India:

EasemyGST, a cloud-based comprehensive GST compliance platform that integrates with ERP, and Microsoft India teamed up together last month to provide a “simple and affordable platform that will ease their GST requirements, thus, saving them from the expense of separate compliance products”. EasemyGST will integrate its solution with Microsoft’s core business products including Office 365, Dynamics Navision and Axapta. The solutions will run in Microsoft Azure, from India data centres to ensure data sovereignty.

Intuit:

infact, Intuit is betting big on GST rollout, and expects revenues to double. Intuit’s QuickBooks, a cloud-based accounting software for small businesses will help SMBs to stay on top of their business in real time and get paid faster. The company’s cloud-based accounting software QuickBooks already has a slew of big companies on board that will use the ERP system.

So how does this all start ?

The GSTN Company would be on a hiring mode over the next few weeks to cater to these new requirements. They plan to double our workforce from 50 to about 100 over the next few months. In the first phase, GSTN is in the process of building and testing the software interfaces for the taxpayers and the back-end to be used by the tax departments of the Centre and states. In the second phase, the roll out will take place and the company is working to ensure at least one critical process of approval of registration on the back-end is ready from day one. This will eventually bring GST platform to become an analytics powerhouse.

The worldwide cyber attack that began last Friday and goes by the name of “WannaCry” has highlighted the need for governments and businesses to strengthen their security infrastructure, in addition to calling attention to the need to mandate security updates and educate lawmakers about the intricacies of cyber security.

During the WannaCry attacks, hospitals had to turn away patients, and their ability to provide care was altered significantly. Even though the threat is widely acknowledged to be real by the information security community and anyone not living under a rock, and the stakes are higher than ever, most organizations and almost all healthcare providers are still using old-school cybersecurity technologies and retain their reactive security postures.

The WannaCry ransomware attack moved too quickly for security teams to respond, but a few organizations were able to spot the early indicators of the ransomware and contain it before the infection spread across their networks. While it wreaked havoc across the globe, there was nothing subtle about it. All of the signs of highly abnormal behavior on the networks were there, but the pace of the attack was far beyond the capacity of human teams contain it. The latest generation of AI technology enabled those few organizations to defend their networks at the first sign of threat.

Meanwhile, threats of similar – or perhaps worse – attacks have continued to surface. This was not the big one. This was a precursor of a far worse attack that will inevitably strike — and it is likely, unfortunately, that [the next] attack will not have a kill switch. This is an urgent call for action for all of us to get the fundamentals finally in place to enable us to withstand robustly this type of a crisis situation when the next one hits.

 Modern malware is now almost exclusively polymorphic and designed in such a way as to spread immediately upon intrusion into a network, infecting every sub-net and system it encounters in near real-time speed. Effective defense systems have to be able to respond to these threats in real time and take on an active reconnaissance posture to seek out these attacks during the infiltration phase. We now have defense systems that have applied artificial intelligence and advanced machine learning techniques and are able to detect and eradicate these new forms of malware before they become fully capable of executing a breach, but their adoption has not matched the early expectations.

As of today, the vast majority of businesses and institutions have not adopted nor installed these systems and they remain at high risk. The risk is exacerbated further by targets that are increasingly involved with life or death outcomes like hospitals and medical centers. All of the new forms of ransomware and extortionware will increasingly be aimed at high-leverage opportunities like insulin pumps, defibrillators, drug delivery systems and operating room robotics.

Network behavioral analytics that leverage artificial intelligence can stop malware like WannaCry and all of its strains before it can form into a breach. And new strains are coming. In fact, by the time this is published, it would not surprise me to see a similar attack in the headlines.

Aanlytics is Turning the Table on Security Threats

The more comprehensive, sensitive and greater volume of end user and customer data you store, the more tempting you are to someone wanting to do harm. That said, the same data attracting the threat can be used to thwart an attack. Analytics includes all events, activities, actions, and occurrences associated with a threat or attack:

• User: authentication and access location, access date and time, user profiles, privileges, roles, travel and business itineraries, activity behaviors, normal working hours, typical data accessed, application usage
• Device: type, software revision, security certificates, protocols
• Network: locations, destinations, date and time, new and non-standard ports, code installation, log data, activity and bandwidth
• Customer: customer database, credit/debit card numbers, purchase histories, authentication, addresses, personal data
• Content: documents, files, email, application availability, intellectual property

The more log data you amass, the greater the opportunity to detect, diagnose and protect an organization from cyber-attacks by identifying anomalies within the data and correlating them to other events falling outside of expected behaviors, indicating a potential security breach. The challenge lies in analyzing large amounts of data to uncover unexpected patterns in a timely manner. That’s where analytics comes into play.

Leveraging Data Science & Analytics to Catch a Thief

Using data science, organizations can exercise real-time monitoring of network and user behaviors, identifying suspicious activity as it occurs. Organizations can model various network, user, application and service profiles to create intelligence-driven security measures capable of quickly identifying anomalies and correlating events indicating a threat or attack:

• Traffic anomalies to, from or between data warehouses
• Suspicious activity in high value or sensitive resources of your data network
• Suspicious user behaviors such as varied access times, levels, location, information queries and destinations
• Newly installed software or different protocols used to access sensitive information
• Identify ports used to aggregate traffic for external offload of data
• Unauthorized or dated devices accessing a network
• Suspicious customer transactions

Analytics can be highly effective in identifying an attack not quite underway or recommending an action to counter an attack, thus minimizing or eliminating losses. Analytics makes use of large sets of data with timely analysis of disparate events to thwart both the smallest and largest scale attacks.

The Analytics Solution to Security Monitoring

If security monitoring is a data storage problem, then it requires a analytics solution capable of analyzing large amounts of data in real time. The natural place to look for that solution is within Apache Hadoop, and the ecosystem of dependent technologies. But although Hadoop does a good job performing analytics on large amounts of data, it was developed to provide batch analysis, not real-time streaming analytics required to detect security threats.

In contrast, the solution for real-time streaming analytics is Apache Storm, a free and open source real-time computation system. Storm functions similar to Hadoop, but was developed for real-time analytics. Storm is fast and scalable, supporting not only real-time analytics but machine learning as well, necessary to reduce the number of false positives found in security monitoring. Storm is commonly found in cloud solutions supporting antivirus programs, where large amounts of data is analyzed to identify threats, supporting quick data processing and anomaly detection.

The key is real-time analysis. Big data contains the activities and events signaling a potential threat, but it takes real-time analytics to make it an effective security tool, and the statistical analysis of data science tools to prevent security breaches.

When do you need to start? – Yesterday

Yesterday would have been a good time for companies and institutions to arm themselves against this pandemic. Tomorrow will be too late.

In recent years, there has been a lot of hype around “big” data in the marketing world. Big data is extremely helpful with gathering quantitative information about new trends, behaviors and preferences, so it’s no wonder companies invest a lot of time and money sifting through and analyzing massive sets of data. However, what big data fails to do is explain why we do what we do.

“Thick” data fills the gap. Thick data is qualitative information that provides insights into the everyday emotional lives of consumers. It goes beyond big data to explain why consumers have certain preferences, the reasons they behave the way they do, why certain trends stick and so on. Companies gather this data by conducting primary and secondary research in the form of surveys, focus groups, interviews, questionnaires, videos and other various methods. Ultimately, to understand people’s actions and what drives them to your business (or not), you need to understand the humanistic context in which they pursue these actions.

Human Behavior vs Human Data

It’s crucial for successful companies to analyze the emotional way in which people use their products or services to develop a better understanding of their customers. By using thick data, companies can develop a positive relationship with their customers and it becomes easier for those companies to maintain happy customers and attract new ones.

Take for example Lego, a successful company that was near collapse in the early 2000’s because they lost touch with their customers. After failed attempts to reposition the company with action figures and other concepts, Jørgen Vig Knudstorp, CEO of the Danish Lego firm, decided to engage in a major qualitative research project. Children in five major global cities were studied to help Lego better understand the emotional needs of children in relation to legos. After evaluating hours of video recordings of children playing with legos, a pattern emerged. Children were passionate about the “play experience” and the process of playing. Rather than the instant gratification of toys like action figures, children valued the experience of imagining and creating. The results were clear; Lego needed to go back to marketing its traditional building blocks and focus less on action figures and toys. Today, Lego is once again a successful company, and thick data proved to be its savior.

While it’s impossible to read the minds of customers, thick data allows us to be closer than ever to predicting the quirks of human behavior. The problem with big data is that companies can get too caught up in numbers and charts and forget the humanistic reality of their customers’ lives. By outsourcing our thinking to Big Data, our ability to make sense of the world by careful observation begins to wither, just as you miss the feel and texture of a new city by navigating it only with the help of a GPS.

The Perils of Big Data Exceptionalism

As the concept of “Big Data” has become mainstream, many practitioners and experts have cautioned organizations to adopt Big Data in a balanced way. Many qualitative researchers from Genevieve Bell to Kate Crawford and danah boyd have written essays on the limitations of Big Data from the perspective of Big Data as a person, algorithmic illusion, data fundamentalism, and privacy concerns respectively. Journalists have also added to the conversation.  Inside organizations Big Data can be dangerous.  People are getting caught up on the quantity side of the equation rather than the quality of the business insights that analytics can unearth. More numbers do not necessarily produce more insights.

Another problem is that Big Data tends to place a huge value on quantitative results, while devaluing the importance of qualitative results. This leads to the dangerous idea that statistically normalized and standardized data is more useful and objective than qualitative data, reinforcing the notion that qualitative data is small data.

These two problems, in combination, reinforce and empower decades of corporate management decision-making based on quantitative data alone. Corporate management consultants have long been working with quantitative data to create more efficient and profitable companies.

Without a counterbalance the risk in a Big Data world is that organizations and individuals start making decisions and optimizing performance for metrics — metrics that are derived from algorithms. And in this whole optimization process, people, stories, actual experiences, are all but forgotten. By taking human decision-making out of the equation, we’re slowly stripping away deliberation — moments where we reflect on the morality of our actions.

Where does Thick Data come from ?

Harvard Business Review (HBR) defines thick data as a tool for developing ‘hypotheses’ about ‘why people behave’ in certain ways. While big data can indicate trends in behavior that allow marketers to form hypotheses, thick data can fill in the gaps and allow marketers to understand why their customers are likely to take certain actions.

While ‘thick data’ is recently receiving a great deal of attention among big data thought leaders, it’s not a new concept. There’s little difference between ‘thick’ data and ‘prescriptive analytics,’ both of which represent advanced maturity in marketing big data. By shifting your focus from predictive big data to forming and testing hypotheses, marketers can better understand how their buyers will act in the future.

Historically, big data has been transactional, while thick data has been qualitative. For data-driven brands of years past, insights into consumer behavior were typically derived from behavioral observation, voice of the customer (VOC) or Net Promoter Score (NPS) surveying, focus groups, or other time-intensive research methods.

Today, insights into consumer behavior can come from a variety of sources. Thanks to social media, internet of things technologies and other drivers of big data, marketers can gain insight into why humans act the way they do with data sources such as:

• Online or Mobile Behavior
• User-generated social media content
• 3rd-party transactional data

Studies indicate that currently, 95% of brand research into consumer preferences is performed manually, using methods such as surveying or focus groups. However, in an era where consumers produce thousands of insights each day from mobile usage, online shopping and social media updates, the insights are easy to obtain.

Finally, will Thick Data take over Big Data ?

This is not to say big data is useless. It is a powerful and helpful tool companies should invest in. However, companies should also invest in gathering and analyzing thick data to uncover the deeper, more human meaning of big data. Together, thick data and big data give you an incredibly insightful advantage.

While some predict mass unemployment or all-out war between humans and artificial intelligence, others foresee a less bleak future. A future looks promising, in which humans and intelligent systems are inseparable, bound together in a continual exchange of information and goals, a “symbiotic autonomy.” If you may. It will be hard to distinguish human agency from automated assistance — but neither people nor software will be much use without the other.

Mutual Co-existence – A Symbiotic Autonomy

In the future, I believe that there will be a co-existence between humans and artificial intelligence systems that will be hopefully of service to humanity. These AI systems will involve software systems that handle the digital world, and also systems that move around in physical space, like drones, and robots, and autonomous cars, and also systems that process the physical space, like the Internet of Things.

I don’t think at AI will become an existential threat to humanity. Not that it’s impossible, but we would have to be very stupid to let that happen. Others have claimed that we would have to be very smart to prevent that from happening, but I don’t think it’s true.

If we are smart enough to build machine with super-human intelligence, chances are we will not be stupid enough to give them infinite power to destroy humanity. Also, there is a complete fallacy due to the fact that our only exposure to intelligence is through other humans. There are absolutely no reason that intelligent machines will even want to dominate the world and/or threaten humanity. The will to dominate is a very human one (and only for certain humans).

Even in humans, intelligence is not correlated with a desire for power. In fact, current events tell us that the thirst for power can be excessive (and somewhat successful) in people with limited intelligence.

You will have more intelligent systems in the physical world, too — not just on your cell phone or computer, but physically present around us, processing and sensing information about the physical world and helping us with decisions that include knowing a lot about features of the physical world. As time goes by, we’ll also see these AI systems having an impact on broader problems in society: managing traffic in a big city, for instance; making complex predictions about the climate; supporting humans in the big decisions they have to make.

Intelligence of Accountability

A lot of companies are working hard on making machines to be able to explain themselves — to be accountable for the decisions they make, to be transparent. A lot of the research we do is letting humans or users query the system. When Cobot, my robot, arrives to my office slightly late, a person can ask , “Why are you late?” or “Which route did you take?”

So they are working on the ability for these AI systems to explain themselves, while they learn, while they improve, in order to provide explanations with different levels of detail. People want to interact with these robots in ways that make us humans eventually trust AI systems more. You would like to be able to say, “Why are you saying that?” or “Why are you recommending this?” Providing that explanation is a lot of the research that is being done, and I believe robots being able to do that will lead to better understanding and trust in these AI systems. Eventually, through these interactions, humans are also going to be able to correct the AI systems. So they are trying to incorporate these corrections and have the systems learn from instruction. I think that’s a big part of our ability to coexist with these AI systems.

The Worst Case Contingency

A lot of the bad things humans do to each other are very specific to human nature. Behavior like becoming violent when we feel threatened, being jealous, wanting exclusive access to resources, preferring our next of kin to strangers, etc were built into us by evolution for the survival of the species. Intelligent machines will not have these basic behavior unless we explicitly build these behaviors into them. Why would we?

Also, if someone deliberately builds a dangerous and generally-intelligent AI, other will be able to build a second, narrower AI whose only purpose will be to destroy the first one. If both AIs have access to the same amount of computing resources, the second one will win, just like a tiger a shark or a virus can kill a human of superior intelligence.

In October 2014, Musk ignited a global discussion on the perils of artificial intelligence. Humans might be doomed if we make machines that are smarter than us, Musk warned. He called artificial intelligence our greatest existential threat.

Musk explained that his attempt to sound the alarm on artificial intelligence didn’t have an impact, so he decided to try to develop artificial intelligence in a way that will have a positive affect on humanity

Brain-machine interfaces could overhaul what it means to be human and how we live. Today, technology is implanted in brains in very limited cases, such as to treat Parkinson’s Disease. Musk wants to go farther, creating a robust plug-in for our brains that every human could use. The brain plug-in would connect to the cloud, allowing anyone with a device to immediately share thoughts.

Humans could communicate without having to talk, call, email or text. Colleagues scattered throughout the globe could brainstorm via a mindmeld. Learning would be instantaneous. Entertainment would be any experience we desired. Ideas and experiences could be shared from brain to brain.

We would be living in virtual reality, without having to wear cumbersome goggles. You could re-live a friend’s trip to Antarctica — hearing the sound of penguins, feeling the cold ice — all while your body sits on your couch.

Final Word – Is AI Uncertainty really about AI ?

I think that the research that is being done on autonomous systems — autonomous cars, autonomous robots — it’s a call to humanity to be responsible. In some sense, it has nothing to do with the AI. The technology will be developed. It was invented by us — by humans. It didn’t come from the sky. It’s our own discovery. It’s the human mind that conceived such technology, and it’s up to the human mind also to make good use of it.

I’m optimistic because I really think that humanity is aware that they need to handle this technology carefully. It’s a question of being responsible, just like being responsible with any other technology every conceived, including the potentially devastating ones like nuclear armaments. But the best thing to do is invest in education. Leave the robots alone. The robots will keep getting better, but focus on education, people knowing each other, caring for each other. Caring for the advancement of society. Caring for the advancement of Earth, of nature, improving science. There are so many things we can get involved in as humankind that could make good use of this technology we’re developing.

More investors are setting their sights on the financial technology (Fintech) arena. According to consulting firm Accenture, investment in Fintech firms rose by 10 percent worldwide to the tune of $23.2 billion in 2016.

China is leading the charge after securing $10 billion in investments in 55 deals which account for 90 percent of investments in Asia-Pacific. The US came second taking in $6.2 billion in funding. Europe, also saw an 11 percent increase in deals despite Britain seeing a decrease in funding due to the uncertainty from the Brexit vote.

The excitement stems from the disruption of traditional financial institutions (FIs) such as banks, insurance, and credit companies by technology. The next unicorn might be among the hundreds of tech startups that are giving Fintech a go.

What exactly is going to be the next big thing has yet to be determined, but artificial intelligence (AI) will play a huge part.

Stiffening competition

The growing reality is that, while opportunities are abound, competition is also heating up.

Take, for example, the number of Fintech startups that aim to digitize routine financial tasks like payments. In the US, the digital wallet and payments segment is fiercely competitive. Pioneers like PayPal see themselves being taken on by other tech giants like Google and Apple, by niche-oriented ventures like Venmo, and even by traditional FIs.

Most recently, the California-based robo-advisor, Wealthfront, has added artificial intelligence capabilities to track account activity on its own product and other integrated services such as Venmo, to analyze and understand how account holders are spending, investing and making their financial decisions, in an effort to provide more customized advice to their customers.  Sentient Technologies, which has offices in both California and Hong Kong, is using artificial intelligence to continually analyze data and improve investment strategies.  The company has several other AI initiatives in addition to its own equity fund.  AI is even being used for banking customer service. RBS has developed Luvo, a technology which assists it service agents in finding answers to customer queries. The AI technology can search through a database, but also has a human personality and is built to learn continually and improve over time.

Some ventures are seeing bluer oceans by focusing on local and regional markets where conditions are somewhat favorable.

The growth of China’s Fintech was largely made possible by the relative age of its current banking system. It was easier for people to use mobile and web-based financial services such as Alibaba’s Ant Financial and Tencent since phones were more pervasive and more convenient to access than traditional financial instruments.

In Europe, the new Payment Services Directive (PSD2) set to take effect in 2018 has busted the game wide open. Banks are obligated to open up their application program interfaces (APIs) enabling Fintech apps and services to tap into users’ bank accounts. The line between banks and fintech companies are set to blur so just about everyone in finance is set to compete with old and new players alike.

Leveraging Digital

Convenience has become a fundamental selling point to many users that a number of Fintech ventures have zeroed in on delivering better user experiences for an assortment of financial tasks such as payments, budgeting, banking, and even loan applications.

There is a mad scramble among companies to leverage cutting-edge technologies for competitive advantage. Even established tech companies like e-commerce giant Amazon had to give due attention to mobile as users shift their computing habits towards phones and tablets. Enterprises are also working on transitioning to cloud computing for infrastructure.

But where do more advanced technologies such as AI come in?

The drive to eliminate human fallibility has also made artificial intelligence (AI) driven to the forefront of research and development. Its applications range from sorting what gets shown on your social media newsfeed to self-driving cars. It’s also expected to have a major impact in Fintech due to potential of game changing insights that can be derived from the sheer volume of data that humanity is generating. Enterprising ventures are banking on it to expose the gap in the market that has become increasingly small due to competition.

All about algorithms

AI and finance are no strangers to each other. Traditional banking and finance have relied heavily on algorithms for automation and analysis. However, these were exclusive only to large and established institutions. Fintech is being aimed at empowering smaller organizations and consumers, and AI is expected to make its benefits accessible to a wider audience.

AI has a wide variety of consumer-level applications for smarter and more error-free user experiences. Personal finance applications are now using AI to balance people’s budgets based specifically to a user’s behavior. AI now also serves as robo-advisors to casual traders to guide them in managing their stock portfolios.

For enterprises, AI is expected to continue serving functions such as business intelligence and predictive analytics. Merchant services such as payments and fraud detection are also relying on AI to seek out patterns in customer behavior in order to weed out bad transactions.

People may soon have very little excuse of not having a handle of their money because of these services

Concerns Going Forward

While artificial intelligence holds the promise of efficiency, better decision-making, stronger compliance and potentially even more profits for investors, the technology is young. Banks need to find ways to lower costs and technology is the most obvious answer. A logical response by banks is to automate as much decision-making as possible, hence the number of banks enthusiastically embracing AI and automation. But the unknown risks inherent in aspects of AI have not been eliminated. According to a Euromoney Survey and report commissioned by Baker & McKenzie, out of 424 financial professionals, 76% believe that financial regulators are not up to speed on AI and 47% are not confident that their own organizations understand the risks of using AI.  Additionally an increasing reliance on artificial intelligence technologies comes with a reduction in jobs.  Many argue that the human intuition plays a valuable role in risk assessment and that the black box nature of AI makes it difficult to understand certain unexpected outcomes or decisions produced by the technology.

Towards the future

With the stiff competition in Fintech, ventures have to deliver a truly valuable products and services in order to stand out. The venture that provides the best user experience often wins but finding this X factor has become increasingly challenging.

The developments in AI may provide that something extra especially if it could promise to eliminate the guess work and human error out of finance. It’s for these reasons that AI might just hold the key to what further Fintech innovations can be made.

The Banking and Finance sector (BFSI) is witnessing one of its most interesting and enriching phases. Apart from the evident shift from traditional methods of banking and payments, technology has started playing a vital role in defining this change.

Mobile apps, plastic money, e-wallets and bots have aided the phenomenal swing from offline payments to online payments over the last two decades. Now, the use of Artificial Intelligence (AI) in BFSI is expediting the evolution of this industry.

But as the proliferation of digital continues, the number of ways one can commit fraud has also increased. Issuers, merchants, and acquirers of credit, debit, and prepaid general purpose and private label payment cards worldwide experienced gross fraud losses of US$11.27 billion in 2012, up 14.6% over the previous year1. Fraud losses on all general purpose and private label, signature and PIN payment cards reached US$5.33 billion in United States in the same period, up 14.5%1. These are truly big numbers, and present the single-biggest challenge to the trust reposed in banks by customers. Besides the risk of losing customers, direct financial impact for banks is also a significant factor.

Upon reporting of a fraudulent transaction by a customer, the bank is liable for the transaction cost, it has to refund merchant chargeback fee, as well as additional fee. Fraud also invites fines from regulatory authorities. The recently-passed Durbin Amendment caps processing fee that can be charged per transaction, and this increases the damage caused by unexpected fraud losses. The rapidly rising use of electronic payment modes has also increased the need for effective, efficient, and real-time methods to detect, deter, and prevent fraud.

Nuances of Banking Fraud Prevention Using AI

AI enables a computer to behave and take decisions like a human being. Coined in 1956 by John McCarthy at MIT, the term AI was little known to the layman and merely a subject of interest to academicians, researchers and technologists. However, over the past few years, it is more commonly seen in our everyday lives; in our smartphones, shopping experiences, hospitals, travel, etc.

Machine Learning, Deep Learning, NLP Platforms, Predictive APIs and Image and Speech Recognition are some core AI technologies used in BFSI today. Machine Learning recognises data patterns and highlights deviations in data observed. Data is analysed and then compared with existing data to look for patterns. This can help in fraud detection, prediction of spending patterns and subsequently, the development of new products.

Key Stroke Dynamics

Key Stroke Dynamics can be used for analysing transactions made by customers. They capture strokes when the key is pressed (dwell time) and released on a keyboard, along with vibration information.

As second factor authentication is mandatory for electronic payments, this can help detect fraud, especially if the user’s credentials are compromised. Deep Learning is a new area in Machine Learning research and consists of multiple linear and non-linear transformations. It is based on learning and improving representations of data. A common application of this can be found in the crypto-currency, Bitcoin.

Adaptive Learning

Adaptive Learning is another form of AI currently used by banks for fraud detection and mitigation. A model is created using existing rules or data in the bank’s system. Incremental learning algorithms are then used to update the models based on changes observed in the data patterns.

AI instances in Insurance for Fraud Prevention

Applying for Insurance

When a customer submits their application for insurance, there is an expectation that the potential policyholder provides honest and truthful information. However, some applicants choose to falsify information to manipulate the quote they receive.

To prevent this, insurers could use AI to analyse an applicant’s social media profiles and activity for confirmation that the information provided is not fraudulent. For example, in life insurance policies, social media pictures and posts may confirm whether an applicant is a smoker, is highly active, drinks a lot or is prone to taking risks. Similarly, social media may be able to indicate whether “fronting” (high-risk driver added as a named driver to a policy when they are in fact the main driver) is present in car insurance applications. This could be achieved by analysing posts to see if the named driver indicates that the car is solely used by them, or by assessing whether the various drivers on the policy live in a situation that would permit the declared sharing of the car.

Claims Management & Fraud Prevention

Insurance carriers can greatly benefit from the recent advances in artificial intelligence and machine learning. A lot of approaches have proven to be successful in solving problems of claims management and fraud detection. Claims management can be augmented using machine learning techniques in different stages of the claim handling process. By leveraging AI and handling massive amounts of data in a short time, insurers can automate much of the handling process, and for example fast-track certain claims, to reduce the overall processing time and in turn the handling costs while enhancing customer experience.

The algorithms can also reliably identify patterns in the data and thus help to recognize fraudulent claims in the process. With their self-learning abilities, AI systems can then adapt to new unseen cases and further improve the detection over time. Furthermore, machine learning models can automatically assess the severity of damages and predict the repair costs from historical data, sensors, and images.

Two companies tackling the management of claims are Shift Technology who offer a solution for claims management and fraud detection and RightIndem with the vision to eliminate friction on claims. Motionscloud offer a mobile solution for the claims handling process, including evidence collection and storage in various data formats, customer interaction and automatic cost estimation. ControlExpert handle claims for the auto insurance, with AI replacing specialized experts in the long-run. Cognotekt optimize business processes using artificial intelligence. Therefore the current business processes are analyzed to find the automation potentials. Applications include claims management, where processes are automated to speed up the circle time and for detecting patterns that would be otherwise invisible to the human eye, underwriting, and fraud detection, among others. AI techniques are potential game changers in the area of fraud. Fraudulent cases may be detected easier, sooner, more reliable and even in cases invisible to the human eye.

Conclusion

Those who wish to defraud insurance companies currently do so by finding ways to “beat” the system. For some uses of AI, fraudsters can simply modify their techniques to “beat” the AI system. In these circumstances, whilst AI creates an extra barrier to prevent and deter fraud, it does not eradicate the ability to commit insurance fraud. However, with other uses of AI, the software is able to create larger blockades through its use of “big data”. It can therefore provide more preventative assistance. As AI continues to develop, this assistance will become of greater use to the insurance industry in their fight against fraud.

Rapidly evolving technology and a digitally focused world have opened the door for a new wave of automation to enter the workforce. Robots already stand side-by-side with their human counterparts on many manufacturing floors, adding efficiency, capacity (robots don’t need to sleep!) and dependability. Add in drones and self-driving vehicles and it’s no wonder many are questioning the role of humans going forward.

Supply chains, although automated to a degree, still face challenges brought about by the amount of slow, manual tasks required, and the daily management of a complex web of interdependent parts. The next generation of process efficiency gains and visibility could be on your doorstep with artificial intelligence in supply chain management, if only you’d let the robots automatically open it for you.

Robotic Process Automation

RPA works by automating the end-to-end supply chain, enabling the management of all tasks and sections in tandem. It allows you to spend less time on low value, high frequency activities like managing day-to-day processes, and provides more time to work on high value, exception-based requirements, which ultimately drives value for the entire business.

PwC estimates businesses could automate up to 45% of current work, saving $2 trillion in annual wages. “In addition to the cost and efficiency advantages, RPA can take a business to the next level of productivity optimization,” the firm says. Those ‘lights out’ factories and warehouses are becoming closer to a reality.

Four key elements need to be in place for you to take full advantage of robotic process automation in your supply chain:

• robots for picking orders and moving them through the facility;
• sensors to ensure product quality and stock;
• cognitive learning systems;
• and, artificial intelligence to turn processes into algorithms to guide the entire operation.

In addition, you’ll need strong collaboration internally and among suppliers and customers to tie all management systems back to order management and enterprise resource planning platforms.

Artificial Intelligence In Supply Chain Automation

AI is changing the traditional way in which companies are operating. Siemens in its “lights out” manufacturing plant, has automated some of its production lines to a point where they are run unsupervised for several weeks.

Siemens is also taking a step towards a larger goal of creating Industrie 4.0 or a fully self-organizing factory which will automate the entire supply chain. Here, the demand and order information would automatically get converted into work orders and be incorporated into the production process.

This would streamline manufacturing of highly customized products.

 Artificial Intelligence In Supplier Management And Customer Service

Organizations are also increasingly leveraging AI for supplier management and customer management. IPsoft’s AI platform, Amelia automates work knowledge and is able to speak to the customers in more than 20 languages. A global oil and gas company has trained Amelia to help provide prompt and more efficient ways of answering invoicing queries from its suppliers. A large US-based media services organization taught Amelia how to support first line agents in order to raise the bar for customer service.

 Artificial Intelligence In Logistics & Warehousing

Logistics function will undergo a fundamental change as artificial intelligence gets deployed to handle domestic and international movement of goods. DHL has stated that its use of autonomous fork lifts is “reaching a level of maturity” in warehouse operations. The next step would be driver less autonomous vehicles undertaking goods delivery operations.

 Artificial Intelligence In Procurement

AI is helping drive cost reduction and compliance agenda through procurement by generating real time visibility of the spend data. The spend data is automatically classified by AI software and is checked for compliance and any exceptions in real time. Singapore government is carrying out trials of using artificial intelligence to identify and prevent cases of procurement fraud.

The AI algorithm analyzes HR and finance data, procurement requests, tender approvals, workflows, non-financial data like government employee’s family details and vendor employee to identify potentially corrupt or negligent practices. AI will also take up basic procurement activities in the near future thereby helping improve the procurement productivity.

Artificial Intelligence in new product development

AI has totally overhauled the new product development process.by reducing the time to market for new products. Instead of developing physical prototypes and testing the same, innovators are now creating 3D digital models of the product. AI facilitates interaction of the product developers in the digital space by recognizing the gestures and position of hand. For example, the act of switching on a button of a digital prototype can be accomplished by a gesture.

 AI In Demand Planning And Forecasting

Getting the demand planning right is a pain point for many companies. A leading health food company leveraged analytics with machine learning capabilities to analyze their demand variations and trends during promotions.

The outcome of this exercise was a reliable, detailed model highlighting expected results of the trade promotion for the sales and marketing department. Gains included a rapid 20 percent reduction in forecast error and a 30 percent reduction in lost sales.

 AI in Smart Logistics

The impact of data-driven and autonomous supply chains provides an opportunity for previously unimaginable levels of optimization in manufacturing, logistics, warehousing and last mile delivery that could become a reality in less than half a decade despite high set-up costs deterring early adoption in logistics.

Changing consumer behavior and the desire for personalization are behind two other top trends Batch Size One and On-demand Delivery: Set to have a big impact on logistics, on-demand delivery will enable consumers to have their purchases delivered where and when they need them by using flexible courier services.

A study by MHI and Deloitte found more than half (51%) of supply chain and logistics professionals believe robotics and automation will provide a competitive advantage. That’s up from 39% last year. While only 35% of the respondents said they’ve already adopted robotics, 74% plan to do so within the next 10 years. And that’s likely in part to keep up with key players like Amazon, who have been leading the robotics charge for the past few years.

 What is the mantra ?

These examples showcase that in today’s dynamic world, AI embedded supply chains offer a competitive advantage. AI armed with predictive analytics can analyze massive amounts of data generated by the supply chains and help organizations move to a more proactive form of supply chain management.

Thus, in this digital age where the mantra is “evolve or be disrupted”, companies are leveraging AI to reinvent themselves and scale their businesses quickly. AI is becoming a key enabler of the changes that businesses need to make and is helping them manage complexity of the constant digital change.