For the larger mass of professionals, the words “artificial intelligence,” or AI, often conjure up images of robots, the sorts of robots that might someday take their jobs. But at the enterprise level, AI means something different. It has enormous power and potential: it can disrupt, innovate, enhance, and in many cases totally transform a business. Forrester Research predicts a 300% increase in AI investment in 2017 from last year, and IDC estimates that the AI market will surge from about $8 billion in 2016 to more than $47 billion in 2020. There’s solid proof that the investment can pay off—if CEO’s can adopt the right strategy. Organizations that deploy AI strategically enjoy advantages ranging from cost reductions and higher productivity to top-line benefits such as increasing revenue and profits, richer customer experiences, and working-capital optimization. The survey shows that the companies winning at AI are also more likely to enjoy broader business success.

So How to make your Enterprise AI Ready?

just one quarter of organizations say they are getting significant impact from it. But these leading businesses have taken clear, practical steps to get the results they want. Here are five of their key strategies:

• Core AI Resource Assimilation using Funding or Acquisition
• Gain senior management support
• Focus on process, not function
• Reskill your teams and foster a learning culture
• Shift from system-of-record to system-of-intelligence apps, platforms
• Encourage innovation

Core AI Resource Assimilation using Funding or Acquisition

As per insights from Forbes and Cowen & Company, 81% of IT leaders are currently investing in or planning to invest in Artificial Intelligence (AI). Based on the study, CIOs have a new mandate to integrate AI into IT technology stacks. The study found that 43% are evaluating and doing a Proof of Concept (POC) and 38% are already live and planning to invest more. The following graphic provides an overview of company readiness for machine learning and AI projects.

Through 2020, organization using cognitive ergonomics and system design in new AI projects will achieve long term success four times more often than others    

 – Gartner

With $1.7 billion invested in AI startups in Q1 2017 alone, and the exponential efficiencies created by this sort of technology, this evolution will happen quicker than many business leaders are prepared for. If you aren’t sure where to start, don’t worry – you’re not alone. The good news is that you still have options:

• You can acquire, or invest in, an innovative technology company applying AI/ML in your market, and gain access to new product and AI/ML talent.
• You can seek to invest as a limited partner in a few early stage AI focused VC firms, gaining immediate access and exposure to vetted early stage innovation, a community of experts and market trends.
• You can set out to build an AI-focused division to optimize your internal processes using AI, and map out how AI can be integrated into your future products. But recruiting in the space is painful and you will need a strong vision and sense of purpose to attract and retain the best.
• You can use outside development-for-hire shops like new entrant Element.ai, who raised over $100M last June, or more traditional consulting firms, to fill the gaps or get the ball rolling.

Process Based Focus Rather than Function Based

One critical element differentiates AI success from AI failure: strategy. AI cannot be implemented piecemeal. It must be part of the organization’s overall business plan, along with aligned resources, structures, and processes. How a company prepares its corporate culture for this transformation is vital to its long-term success. That includes preparing people by having senior management that understands the benefits of AI; fostering the right skills, talent, and training; managing change; and creating an environment with processes that welcome innovation before, during, and after the transition.

The challenge of AI isn’t just the automation of processes—it’s about the up-front process design and governance you put in to manage the automated enterprise. The ability to trace the reasoning path AI technologies use to make decisions is important. This visibility is crucial in financial services, where auditors and regulators require firms to understand the source of a machine’s decision.

Taking down Resistance to change of Upper Management

One of the biggest challenges to digital transformation is resistance to change. The survey found that upper management is the group most strongly opposed to AI implementation. C-suite executives may not have warmed up to it either. There is such a lack of understanding about the benefits which the technology can bring that the C-suite or board members simply don’t want to invest in it, nor do they understand that failing to do so will adversely affect their bottom line and even cause them to go out of business. Regulatory uncertainty about AI, rough experiences with previous technological innovation, and a defensive posture to better protect shareholders, not stakeholders, may be contributing factors.

Pursuing AI without senior management support is difficult. Here the numbers again speak for themselves. The majority of leading AI companies (68%) strongly agree that their senior management understands the benefits AI offers. By contrast, only 7% of laggard firms agree with this view. Curiously, though, the leading group still cites the lack of senior management vision as one of the top two barriers to the adoption of AI.

Reskilling Teams and HR Redeployment

HR and corporate management will need to figure out new jobs for people to do. Redeployment is going to be a huge factor that the better companies will learn how to handle. The question of job losses is a sensitive one, most often played up in news headlines. But AI also creates numerous job opportunities in new and different areas, often enabling employees to learn higher-level skills. In healthcare for example, physicians are learning to work with AI-powered diagnostic tools to avoid mistakes and make better decisions. The question is who owns the data. If HR retains ownership of people data, it continues to have a role. If it loses that, all bets are off.

HR’s other role in an AI future will be to help make decisions about if and when to automate, whether to reskill or redeploy the human workforce, and the moral and ethical aspects of such decisions. Companies which are experimenting with bots and AI with no thought for the implications need to realize that HR should be central to the governance of AI automation.

Given the potential of AI to complement human intelligence, it is vital for top-level executives to be educated about reskilling possibilities. It is in the best interest of companies to train workers who are being moved from jobs that are automated by AI to jobs in which their work is augmented by AI.

The Dawn of System-of-Intelligence Apps & Platforms

Cowen predicts that an Intelligent App Stack will gain rapid adoption in enterprises as IT departments shift from system-of-record to system-of-intelligence apps, platforms, and priorities. The future of enterprise software is being defined by increasingly intelligent applications today, and this will accelerate in the future.

By 2019, AI platform services will cannibalize revenues for 30% of market leading companies    -Gartner

Cowen predicts it will be commonplace for enterprise apps to have machine learning algorithms that can provide predictive insights across a broad base of scenarios encompassing a company’s entire value chain. The potential exists for enterprise apps to change selling and buying behaviour, tailoring specific responses based on real-time data to optimize discounting, pricing, proposal and quoting decisions.

The Process of Supporting Innovation

Besides developing capabilities among employees, an organization’s culture and processes must also support new approaches and technologies. Innovation waves take a lot longer because of the human element. You can’t just put posters on the walls and say, ‘Hey, we have become an AI-enabled company, so let’s change the culture.’ The way it works is to identify and drive visible examples of adoption.

Algorithmic trading, image recognition/tagging, and patient data processing are predicted to the top AI uses cases by 2025. Tractica forecasts predictive maintenance and content distribution on social media will be the fourth and fifth highest revenue producing AI uses cases over the next eight years.

In the End, it’s about Transforming Enterprise

AI is part of a much bigger process of re-engineering enterprises. That is the major difference between the sci-fi robots of yesteryear and today’s AI: the technologies of the latter are completely integrated into the fabric of business, allowing private and public-sector organizations to transform themselves and society in profound ways. You don’t have to turn to sci-fi. The story of human/machine collaboration is already playing at an enterprise near

There seems to be a glaring ambiguity as to exactly what artificial intelligence (AI) is, and how the discipline of AI should be categorized. Is AI a form of analytics or is it a totally new discipline that is distinct from analytics? I firmly believe that AI is more closely related to predictive analytics and data sciences than to any other discipline. One might even argue that AI is the next generation of predictive analytics and is born out of sophistication of analytics . Additionally, AI is often utilized in situations where it is necessary to operationalize the analytics process. So, in that sense, AI is also often pushing the envelope of prescriptive, operationalized analytics. It would be a mistake to say that AI is not a form of analytics.

I’ve seen AI applied to some of the most obscure topics you can imagine, ranging from industrial energy usage all the way to finding the right GIFs. Using Artificial Intelligence to improve and create solutions to today’s pressing business and social problems is one of the defining trends of the tech world for me.

So, if you are a PE / VC entity and are looking for investment opportunity in AI space, you will have to understand what kinds of AI companies exist and how this AI practice has evolved from Analytics practice.

There are three types of AI companies — core, applied, and industry

1. Core AI Companies

Core AI companies develop technology that improves parts of the AI creation or deployment process itself. Here are a few selected parts of that process and a few companies that are innovating in each:

Data scrubbing and cleaning: Trifacta, Paxata, Wealthport, Datalogue
Modeling: Sentient, Petuum, MLJar
Deployment: Yhat, Seldon
These companies all innovate in some specific, industry-agnostic part of the AI pipeline. Some of them are specific tools, while others purport to have an entirely new approach to AI that will revolutionize how it’s done (see Geometric Intelligence circa 2015).

If you’re investing in Core AI companies, you should probably have a good understanding of how this pipeline works. If you’re founding one of these companies, you should probably have experience deploying Machine Learning and AI at scale.

2. Applied AI Companies

A bit on the more specific side, Applied AI start-ups develop technology that helps companies across different industries perform a specific task using AI. As with the above, here are some examples of those applications and a few interesting companies in each:

Analysing and understanding text: Indico, Synapsify, Lexalytics
Analysing and understanding images and videos: Clarifai, Kairos, Imagry, Affectiva, Deepomatic
Bots / Voice: Init.ai, MindMeld
While investors can get away with not having experience in one of these specific applications, founders will likely have done projects involving this stuff in the past.

The implementation of AI in this scenario corresponds to the implementation of predictive analytics. At its core, predictive analytics is, naturally, about predicting something. Who will buy? Will certain equipment break? Which price will maximize profits? Each of these questions can be addressed by following a familiar workflow – First, we identify a metric or state that we want to predict and gather historical information on that metric or state. Next, we gather additional data that we believe could be relevant to predicting our target. Then, we pass the data through one or more algorithms that attempt to find a relationship between the target and the additional data. Through this process, a model is created that produces a prediction if new data is fed to it. If a customer had this profile, how likely would she be to respond? If we priced at this point, how much profit might we expect?

The goals and steps followed within an AI process are the same. Let’s look at two examples:

Take image recognition. First, we identify a bunch of cat pictures. Then, we grab a bunch of non-cat pictures. We pass a deep learning algorithm over the images to learn to accurately predict whether an image is a cat. When provided with a new image, the model will answer with the probability that the image is a cat. Sounds a lot like predictive analytics, doesn’t it?

Let’s now consider natural language processing (NLP). We gather a wide range of statements that have specific meanings we care about. We also gather a wide range of other statements. We run NLP procedures against the data to try to tease out how to tell what is important and how to tell what is being asked. As we feed a new line of text to the process, it will identify what the point of the statement is in probabilistic terms. The NLP process will assign probabilities to various possible interpretations and send those back (think Watson playing jeopardy). This also sounds a lot like predictive analytics.

3. Industry AI Companies

The final category of ML/AI companies apply these techniques to specific business problems in specific verticals. This is undoubtedly the lion’s share of the actual number of companies being founded, and in many ways, represents the true promise of AI — solving actual and immediate problems with new techniques. Here, it’s easier to give companies as examples. The format is always “AI for ________”:

DigitalGenius: AI for customer support
Cylance: AI for cyber threat prevention
X.ai: AI for scheduling meetings
Drive.ai: AI for autonomous vehicles
The implementation of AI in this scenario corresponds to industrialized embedded analytics. A major trend today is to embed analytics into business applications so that the models are utilized in an automated, embedded, prescriptive fashion at the point of a business decision. For example, as a person navigates a web page, models are utilized to predict what offers should appear on the next page. There is no human intervention once the process is in place. The process makes offers until told to stop.

Many applications of AI today also require industrialization. For example, as an image is posted on social media, it is immediately analysed to identify who is present in the image. As I make a statement to Siri or Alexa, it attempts to determine what I said and what the best answer is. While this qualifies as a more advanced application of predictive analytics that moves into embedded, prescriptive, automated processes, it is still very much in line with how industrialized embedded analytics are being used today.

The common theme among these companies is that they take Machine Learning / AI and use it on a specific problem or space. When researching investments like this, investors should look at both the AI itself (if it works well) and the business case (whether it’s compelling). In x.ai’s case, investors need to know if the AI works, but they should also consider whether AI is the best way to solve the scheduling problem, and whether scheduling is a problem worth solving at all. With the other two types of companies, this is rarely a consideration. Founders of these types of companies can often not have AI experience and can even be non-technical (with the right supporting team and CTO, of course).

Final Word

Your journey to a fruitful AI investment will be far easier if you recognize and embrace AI as sophistication of analytics and understand the true categorization, and then task your analysts with leading the charge. Don’t cause confusion and redundancy by considering AI to be something completely different.

For organizations, transforming finance and accounting function  via adoption of topical technology means improving how they pre-empt red flags around the financial transactions within the organizations.  

In fact, prudent finance and accounting operability  represents the single biggest challenge firms have to deliver on their priorities, according to a survey by Econsultancy.

What’s more, 45% of respondents indicated that embedding analytics &AI  relevant as possible in the finance and accounting function is their key focus.

 CFOs around the world are not asking if digital disruption will occur, but instead, what it means for their function. So the question asked in this article, is how can CFO’s leverage digital transformation wave using AI to advance their organization’s competitive position and improve performance of their function ?

CFO of Tomorrow

With business around the world undergoing digital transformation, the roles of the c-suite is also changing. Perhaps most significantly, the role of the chief financial officer is moving from simply counting pennies to being a major driver of change within companies.

In the past, the role of the CFO was all about getting the numbers. That was 90 per cent of their time, but that is changing and now it’s about understanding the source of the number, understanding what created the number, and understanding the business drivers behind the number. A CFO then needs to try to make sense of the business drivers, and be able to present to the board what the outlook for the organization is, what the costs are, and what actions need to be taken.

This means that the CFO has to have a solid handle on data and analytics, and once they have that in their arsenal, they can become a strategic adviser to the business and they are able to tell the CEO things like what impact customer satisfaction has on the business.

I want to further highlight a few use cases showing how disruptive technologies such as artificial intelligence (AI) and machine learning will be used in the office of the CFO to increase productivity, simplify processes, and support decision-making, and aid in digital finance evolution:

Digital chatbots : Digital assistants for CFOs could impact analytics and the way they handle them. Today, almost everybody in Financial Planning & Analysis (FP&A) receives countless calls asking for information like, “What was our revenue in Q3 last year for this product? What has our growth been over the last three years for this line of business?”

Smart assistants like Amazon’s Alexa and Apple’s Siri can already answer questions on weather forecasts, stock quotes, and so forth, but what if they could provide the latest financial results and give decision makers instant access to information? A CFO could have a conversation with his or her ERP system using a digital assistant to get an immediate response or a clarifying question, without having to open a dashboard or dig into a database.

Risk assessments: When we assess commercial proposals for our services projects, we evaluate each project individually based on the customer characteristics – maturity, industry, size, current system landscape, and so on – as well as the complexity of the products to be implemented. To qualify this assessment, we depend on managers who have previously worked on similar projects. That can limit us to the individual perspective of those managers.

Machine learning could give finance teams and executives the power to access decades’ worth of projects, around the world, at the touch of a button. In levering these insights, teams could then develop a better-informed risk assessment, mapping the project against a much larger database of historical projects.

Invoice clearing: In finance departments today, accounts receivable or treasury clerks can often be challenged in clearing invoice payments, as customers often combine invoices in one payment, pay incorrect amounts, or forget to include invoice numbers with their payments. To clear the invoice, the employee then has two options: manually add up various invoices that could possibly match the payment amount, or reach out to the customer to clarify. In the case of short payment, the employee either has to ask for approvals to accept the short payment or request the remaining amount from the customer.

What if an intelligent system could help streamline this process by suggesting invoices in real time that might match the paid amount and, based on established thresholds, automatically clear the short payments or automatically generate a delta invoice?

Expense-claim auditing: Expense-claim auditing is another routine, transactional finance task. Finance teams are tasked with ensuring that receipts are genuine, match claimed amounts, and are in line with company policy. While state-of-the-art travel-and-expense solutions can simplify the process, a manual audit still needs to be performed.

Machine learning and AI technologies could improve this process, auditing 100% of all claims, and sending only questionable claims to a manager for approval. The machine could read receipts – regardless of language –  to ensure that they are genuine, and match them against the policy.

Accruals: Artificial intelligence and machine learning also offer promise when it comes to determining bonus accruals. Today, teams have a myriad of factors to consider when determining bonus accruals. CFO teams look at current headcount salaries and bonus plans, and try to forecast all KPIs in compensation plans. From there, teams try to calculate the most accurate accrual (likely adding a buffer, to be safe). However, oftentimes, accuracy ends of being a matter of luck more than anything else.

By applying machine learning to these calculations, predictive analytics could serve as a valuable tool to generate unbiased accrual figures, leaving finance teams more time during closing periods for other activities that require human review and judgment.

Customer Journey: This is an area where the CFO is ideally placed to play a greater role in contributing to company growth and profits. His perspective on new customer acquisition, retention activities, customer development and predictive customer behavior models is crucial.

AI is what’s making all of this possible. With his new 360° vision and customer knowledge, the CFO can become a strategic business leader. Via AI and the breaking down of old company silos, the customer journey becomes everyone’s concern. And customer engagement wins its rightful place at the heart of business strategy.

  The overall impact on jobs in finance: As these advanced technologies continue to penetrate the finance function, a new crop of skills are rising to the forefront when it comes to hiring finance talent. Routine, transactional roles will become less prevalent, while the need for strategic thinkers with cross-functional knowledge and technology prowess will be critical. Additionally, while transactional tasks will be fewer, digital transformation will require additional finance resources to be developed and supported, creating an opportunity to redefine processes and roles.

CFO’s, like everyone else, will have to adopt AI
In the future, it will be the companies that can harness AI that will set themselves apart. They will become fully digital businesses. Forward-thinking CFOs will help this happen. Because, by making AI accessible company-wide, they now have the power to unleash infinite company value.

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.

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.