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

AI today can be described in terms of three application domains: cognitive automation, cognitive engagement and cognitive insight.

• Cognitive automation: In the first AI domain are machine learning (ML), Robotics Process Automation (RPA), natural language processing (NLP) and other cognitive tools to develop deep domain-specific expertise and then automate related tasks.
• Cognitive engagement: At the next level of the AI value tree lies cognitive ‘agents’: systems that employ cognitive technology to engage with people, unlocking the power of unstructured data (industry reports / financial news) leveraging text/image/video understanding, offering a personalized engagement between banks and customers with personalized product offerings and unlocking new revenue streams.
• Cognitive insights: Cognitive Insights refer to the extraction of concepts and relationships from various data streams to generate personalized and relevant answers hidden within a mass of structured and unstructured data. Cognitive Insights allow to detect real time key patterns and relationships from large amount of data across multiple sources to derive deep and actionable insights.

  Here are five key applications of artificial intelligence in the Banking industry that will revolutionize the industry in the next 5 years.

AML Pattern Detection

Anti-money laundering (AML) refers to a set of procedures, laws or regulations designed to stop the practice of generating income through illegal actions. In most cases, money launderers hide their actions through a series of steps that make it look like money that came from illegal or unethical sources are earned legitimately.

HSBC has partnered with Silicon Valley-based artificial intelligence startup Ayasdi to automate some of its compliance processes in a bid to become more efficient. The banking group is implementing the company’s AI technology to automate anti money-laundering investigations that have traditionally been conducted by thousands of humans, the bank’s Chief Operating Officer Andy Maguire said in an interview last week.

Chatbots

Chat bots are already being extensively used in the banking industry to revolutionize the customer relationship management at personal level. Bank of America plans to provide customers with a virtual assistant named “Erica” who will use artificial intelligence to make suggestions over mobile phones for improving their financial affairs. Allo, released by Google is another generic realization of chat bots.

The State Bank of India (SBI) on Monday announced SBI Intelligent Assistant (SIA) — a chat assistant aimed to address customer enquiries like a “bank representative” does. Developed by Payjo, an artificial intelligence (AI) banking platform, “SIA” is equipped to handle nearly 10,000 enquiries per second or 864 million in a day — which is nearly 25 per cent of the queries processed by Google each day.

Algorithmic Trading

Plenty of Hedge funds across the globe are using high end systems to deploy artificial intelligence models which learn by taking input from several sources of variation in financial markets and sentiments about the entity to make investment decisions on the fly. Reports claim that more than 70% of the trading today is carried out by automated artificial intelligence systems. Most of these hedge funds follow different strategies for making high frequency trades (HFTs) as soon as they identify a trading opportunity based on the inputs.

A few hedge funds active in AI space are: Two Sigma, PDT Partners, DE Shaw, Winton Capital Management, Ketchum Trading, LLC, Citadel, Voleon, Vatic Labs, Cubist, Point72, Man AHL.

Fraud Detection

Fraud detection is one of the fields which has received massive boost in providing accurate and superior results with the intervention of artificial intelligence. It’s one of the key areas in banking sector where artificial intelligence systems have excelled the most. Starting from the early example of successful implementation of data analysis techniques in the banking industry is the FICO Falcon fraud assessment system, which is based on a neural network shell to deployment of sophisticated deep learning based artificial intelligence systems today, fraud detection has come a long way and is expected to further grow in coming years.

Mastercard announced the acquisition of Brighterion. Brighterion’s portfolio of AI and machine learning technologies provide real-time intelligence from all data sources regardless of type, complexity and volume. Its smart agent technology will be added to Mastercard’s suite of security products already using AI.

Customer Recommendations

Recommendation engines are a key contribution of artificial intelligence in banking sector. It is based on using the data from the past about users and/ or various offerings from a bank like credit card plans, investment strategies, funds, etc. to make the most appropriate recommendation to the user based on their preferences and the users’ history. Recommendation engines have been very successful and a key component in revenue growth accomplished by major banks in recent times.

With Big Data and faster computations, machines coupled with accurate artificial intelligence algorithms are set to play a major role in how recommendations are made in banking sector. For further reading on recommendation engines, you can refer to the complete guide of how recommendation engines work.

JPMorgan, which is spending big on technology as it looks to cut costs and increase efficiency, last year launched a predictive recommendation engine to identify those clients which should issue or sell equity. And now, given the initial success of the engine, it’s being rolled out to other areas.

Strategic Challenges of AI

As with any new endeavor, there are several challenges associated with the development and application of AI solutions.

• Most banks and credit unions are in the early stages of adopting AI technologies. According to a survey conducted by Narrative Science in conjunction with the National Business Research Institute, 32% of financial services executives surveyed confirmed using AI technologies such as predictive analytics, recommendation engines, voice recognition and response.
• Also, one of the biggest challenges is finding the right talent. With only slightly more than half of survey respondents (55%) stating they have identified an AI leader within their company, more than half of those have appointed the head of innovation as the leader.
• In some cases, current employees will not be well positioned for the ‘new age of banking.’ In other cases, the transformation of labor caused by the advances of AI will eliminate some positions entirely.
• 12% of the overall group weren’t using AI yet because they felt it was too new, untested or weren’t sure about the security.
• There is no clear internal ownership of testing emerging technologies— only 6% of those surveyed having an innovation leader or an executive dedicated to testing new ideas and processes.  

How to make AI Part of Banking Ecosystem

The potential of open banking and artificial intelligence are intertwined, making up the foundation for a new banking ecosystem that will most likely include both financial and non-financial components. By partnering with fintech providers and data analytic professionals, the power of organizational data and insights can be realized. The partnerships and structure decided upon today will determine an organization’s competitive differentiation in the future.

Multiple providers are offering AI-based solutions and, as a result, banks need to navigate between specialist players and AI powerhouses. The goal will not to become more automated and less personalized, but to use technology and customer insights to become a lot more personalized and contextual.

The banking industry is still in the early stages of developing strong AI solutions. While these solutions can impact the cost and revenue structures of financial organizations, the real potential is with how artificial intelligence can improve the customer experience. Singaporean bank DBS had the vision to launch Digi bank, India’s first mobile-only bank. Being paperless and branchless, Digi bank had to rely on emerging technologies like conversational AI to succeed. Digi bank was built with one-fifth of the cost of a regular retail bank and can contain 82% of customer inquiries with bots. Some banks just want to hand off responsibility to the vendor but Digi bank’s approach is to empower the customer with self-service tools. They don’t want to be professional services

There are four key recommendations that experts make to financial services firms who are looking to effectively exploit the value of AI. These are:

• Look to invest, learn and pair up with experts from outside of the industry
• Make use of cognitive computing to make better use of data
• Implement the right mix of platform technologies
• Strive to maintain a human touch.

   In conclusion, it is evident that AI is here to stay, and is impacting a large number of industries, Banking is an early adopter of this trend. This trend is likely to grow exponentially in the future. Companies that embrace this trend are likely to be winners

Previous week , I had an opportunity to moderate a fireside chat at NASSCOM Martech conference that carried a theme around changing role for CMO with the advent of AI and I could notice a substantial set of queries during the conference on how AI will redefine marketing. As understandable, each new technology can create fear, uncertainty, and doubt until we understand it better. And AI, with all its hype, fits that bill. But to remain current and relevant, CMOs must quickly understand and apply AI. Here’s a short AI CMO Primer.

Can I put off AI until later?

The answer is no! AI is here. Waiting to deal with it could put you well behind the curve. Leading businesses are already either using AI to profound effect, or actively planning for it.

• Amazon, the company that wants to eat everyone’s lunch, is already driving a third of its business from a AI-powered function: its recommended purchases.
• In a June 2016 report, Weber Shandwick found that 68% of CMOs report their company is “planning for business in the AI era” with 55% of CMOs expecting AI to have a “greater impact on marketing and communications than social media ever had.”

To wait is to get left behind. And as you’ll see later, getting started doesn’t have to be painful or costly.

 What is AI, machine learning, and cognitive intelligence?

Academic experts might hate my explanation, but differentiating between AI, machine learning, and cognitive intelligence from a practical CMO perspective isn’t necessary. I use AI as an umbrella term to refers to software that carries out a task which normally requires human intuition—including learning and problem solving.

AI can be thought of as a set of repeatable steps and, while AI doesn’t technically replicate free-will and decision making, it does map out these steps and use computer processing speed to make its way through them to come to an outcome—like how a person would. It can do this much faster, and taking into account far more relevant data than a human would.

Is AI ready for marketing now?

AI has come at the right time, along with the explosion of Big Data. In essence, with access to an incredible amount of data, it’s never been more important for organizations to make sense of it and leverage important pieces out of the noise.

With the exponential growth of cheap, fast, scalable, and interconnected computing and storage in the cloud, the horsepower and data to efficiently run AI algorithms is now within everyone’s reach.

But, that being said, it is also sadly true that there’s one very simple reason why progress towards full automation and AI marketing is relatively sluggish – because most machines aren’t actually learning anything. All of these platforms that exist today, there’s no machine learning. And if it is, their machine learning is, ‘Did someone open an email? Yes, give them a point. That’s not real machine learning. Which is a problem, because effective automation is fast becoming a prerequisite of effective marketing. From chatbots to real-time contextual geographic marketing, modern marketing solutions demand insight-driven automation to deploy the right message quickly, at scale.

marketing automation (especially AI marketing) will have to eventually free marketers from manual work which comprises ‘98% of their eight hours a day’, empowering them to spend their time more productively tackling the creative jobs that machines aren’t well suited to. This requires three key problems AI marketing providers need to solve:

1. The creation of effective, scalable machine learning which can optimize a campaign without human input.

2. Ensuring that decision-making system’s logic is transparent and easily comprehensible by marketers seeking to analyze and augment those automated insights.

3. Designing a prescriptive system which can not only predict future actions – but understand why the user would make those actions. 

How can AI be applied to marketing?

AI has the potential to revolutionize customer engagement, customer service, and marketing automation. It can enhance the way we communicate with new, current, and inactive customers, and automate admin functions at the backend. In other words, it can help make marketing operations more efficient and effective.

AI can far more accurately predict next best action, by churning through (in real-time) all relevant data about the customers – purchases, interactions, social media posts, email exchanges – and then learn from the results and do it on a scale not previously possible.

For example, let’s say you have a few million customers and want to communicate with them as if you know them very well, providing everyone the right offer at the right time. AI can enable this level of personalization at a scale of millions of individuals, and in near real time.

In essence, AI can save marketers time and bring companies far closer to their customers, without worrying about IT, data lakes, data quality, or hiring armies of data scientists. 

Do I need to become an AI expert?

The short answer is no. AI systems shouldn’t require you to become a mathematician. With AI system, you’ll be able to focus on the results not the process of churning through of thousands, millions, or trillions of data points to arrive at the insights you need about your customers.

How much will it cost?

Surprisingly, AI systems can reduce costs and eliminate waste. AI systems can significantly reduce the requirement for data engineers and data scientists, or the need to depend on IT teams.

  And AI can take wasted effort out of the system by providing a deeper understanding of what your customers want and how to interact with them effectively.

How do I get started?

First, start exploring today. Read, talk to people, and evaluate first hand. Select a contained, but impactful area business problem. A subset of your customer loyalty system could make a great initial project. Loyal customers should be the life blood of most companies, but often are underserved as it’s difficult to pull together and analyze all relevant data in a timely manner. This is a perfect fit for AI because there’s typically a lot more known data for AI to analyze about current customers, as compared to prospects. And it’s a project where you can start seeing high-impact results in weeks—perhaps even new revenue from customers who were previously inactive.

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 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.