The current boom of exponential technologies of today is causing strong disruption in the talent availability landscape, with traditional, more mechanical roles being wiped out and paving way for huge demand for learning and design thinking based skills and professions. The World Economic Forum said in 2016 that 60% of children entering school today will work in jobs that do not yet exist.

While there is a risk to jobs due to these trends, the good news is that a huge number of new jobs are getting created as well in areas like AI, Machine Learning, Robotic Process Automation (RPA), Blockchain, Cybersecurity, etc. It is clearly a time of career pivot for IT professionals to make sure they are where the growth is.

AI and Machine Learning upending the traditional IT Skill Requirement

AI and Machine Learning will create a new demand for skills to guide its growth and development. These emerging areas of expertise will likely be technical or knowledge-intensive fields. In the near term, the competition for workers in these areas may change how companies focus their talent strategies.

At a time when the demand for data scientists and engineers will grow 39% by 2020, employers are seeking out leaders who can effectively work with technologists to ask the right questions and apply the insight to solve business problems. The business schools are, hence, launching more programs to equip graduates with the skills they need to succeed. Toronto’s Rotman School of Management, for example, last week launched a nine-month program which provides recent college graduates with advanced data management, analytical and communication skills. 

According to the Organization of Economic Cooperation and Development, only 5-10% of labor would be displaced by intelligent automation, and new job creation will offset losses.

The future will increase the value of workers with a strong learning ability and strength in human interaction. On the other hand, today’s highly paid, experienced, and skilled knowledge workers may be at risk of losing their jobs to automation.

Many occupations that might appear to require experience and judgment — such as commodity traders — are being outdone by increasingly sophisticated machine-learning programs capable of quickly teasing subtle patterns out of large volumes of data. If your job involves distracting a patient while delivering an injection, guessing whether a crying baby wants a bottle or a diaper change, or expressing sympathy to calm an irate customer, you needn’t worry that a robot will take your job, at least for the foreseeable future.

Ironically, the best qualities for tomorrow’s worker may be the strengths usually associated with children. Learning has been at the centre of the new revival of AI. But the best learners in the universe, by far, are still human children. At first, it was thought that the quintessential preoccupations of the officially smart few, like playing chess or proving theorems — the corridas of nerd machismo — would prove to be hardest for computers. In fact, they turn out to be easy. Things every dummy can do like recognizing objects or picking them up are much harder. And it turns out to be much easier to simulate the reasoning of a highly trained adult expert than to mimic the ordinary learning of every baby. The emphasis on learning is a key change from previous decades and rounds of automation.

According to Pew Research, 47% of all employment opportunities will be occupied by machines within the next two decades.

What types of skills will be needed to fuel the development of AI over the next several years? These prospects include:

• Ethics: The only clear “new” job category is that of AI ethicist, a role that will manage the risks and liabilities associated with AI, as well as transparency requirements. Such a role might be imagined as a cross between a data scientist and a compliance officer.
• AI Training: Machine learning will require companies to invest in personnel capable of training AI models successfully, and then they must be able to manage their operations, requiring deep expertise in data science and an advanced business degree.
• Internet of Things (IoT): Strong demand is anticipated for individuals to support the emerging IoT, which will require electrical engineering, radio propagation, and network infrastructure skills at a minimum, plus specific skills related to AI and IoT.
• Data Science: Current shortages for data scientists and individuals with skills associated with human/machine parity will likely continue.
• Additional Skill Areas: Related to emerging fields of expertise are a number of specific skills, many of which overlap various fields of expertise. Examples of potentially high-demand skills include modeling, computational intelligence, machine learning, mathematics, psychology, linguistics, and neuroscience.

In addition to its effect on traditional knowledge workers and skilled positions, AI may influence another aspect of the workplace: gender diversity. Men hold 97 percent of the 2.5 million U.S. construction and carpentry jobs. These male workers stand more than a 70 percent chance of being replaced by robotic workers. By contrast, women hold 93 percent of the registered nurse positions. Their risk of obsolescence is vanishingly small: .009 percent.

RPA disrupting the traditional computing jobs significantly

RPA is not true AI. RPA uses traditional computing technology to drive its decisions and responses, but it does this on a scale large and fast enough to roughly mimic the human perspective. AI, on the other hand, applies machine and deep learning capabilities to go beyond massive computing to understand, learn, and advance its competency without human direction or intervention — a truly intelligent capability. RPA is delivering more near-term impact, but the future may be shaped by more advanced applications of true AI.

In 2016, a KPMG study estimated that 100 million global knowledge workers could be affected by robotic process automation by 2025.

The first reaction would be that in the back office and the middle office, all those roles which are currently handling repetitive tasks would become redundant. 47% of all American job functions could be automated within 20 years, according to the Oxford Martin School on Economics in a 2013 report.

Indeed, India’s IT services industry is set to lose 6.4 lakh low-skilled positions to automation by 2021, according to U.S.-based HfS Research. It said this was mainly because there were a large number of non-customer facing roles at the low-skill level in countries like India, with a significant amount of “back office” processing and IT support work likely to be automated and consolidated across a smaller number of workers.

Automation threatens 69% of the jobs in India, while it’s 77% in China, according to a World Bank research.

Job displacement would be the eventual outcome however, there would be several other situations and dimensions which need to be factored. Effective automation with the help of AI should create new roles and new opportunities hitherto not experienced. Those who currently possess traditional programming skills have to rapidly acquire new capabilities in machine learning, develop understanding of RPA and its integration with multiple systems. Unlike traditional IT applications, planning and implementation could be done in small patches in shorter span of time and therefore software developers have to reorient themselves.

For those entering into the workforce for the first time, there would be a demand for talent with traditional programming skills along with the skills for developing RPA frameworks or for customising the frameworks. For those entering the workforce for being part of the business process outsourcing functions, it would be important to develop capability in data interpretation and analysis as increasingly more recruitment at the entry level would be for such skills and not just for their communication or transaction handling skills.

Blockchain – A blue ocean of a New kind of Financial Industry Skillset

A technology as revolutionary as blockchain will undoubtedly have a major impact on the financial services landscape. Many herald blockchain for its potential to demystify the complex financial services industry, while also reducing costs, improving transparency to reduce the regulatory burden on the industry. But despite its potential role as a precursor to extend financial services to the unbanked, many fear that its effect on the industry may have more cons than pros.

30–60% of jobs could be rendered redundant by the simple fact that people are able to share data securely with a common record, using Blockchain

Industries including payments, banking, security and more will all feel the impact of the growing adoption of this technology. Jobs potentially in jeopardy include those involving tasks such as processing and reconciling transactions and verifying documentation. Profit centers that leverage financial inefficiencies will be stressed. Companies will lose their value proposition and a loss of sustainable jobs will follow. The introduction of blockchain to the finance industry is similar to the effect of robotics in manufacturing: change in the way we do things, leading to fewer jobs, is inevitable.

Nevertheless, the nature of such jobs is likely to evolve.  While Blockchain creates an immutable record that is resistant to tampering, fraud may still occur at any stage in the process but will be captured in the record and there easily detected. This is where we can predict new job opportunities. There could be a whole class of professions around encryption and identity protection.

So far, the number of jobs created by the industry appears to exceed the number of available professionals qualified to fill them, but some aren’t satisfied this trend will continue. Still, the study of the potential impact of blockchain tech on jobs has been largely qualitative to date. Aite Group released a report that found the largest employers in the blockchain industry each employ about 100 people.

Over the years, the influence of AI has spread to almost every aspect of the travel and the hospitality industry. 30% of hospitality businesses use artificial intelligence to augment at least one of their primary sales processes and most customer personalization is done using AI.

30% of hospitality businesses use artificial intelligence to augment at least one of their primary sales processes.

The sudden popularity of Artificial Intelligence in the Travel industry can be credited to the humongous amount of data being generated today. Artificial Intelligence helps analyse unstructured data, brings value in partnership with Big Data and turns it into meaningful and actionable insights. Trends, outliers and patterns are figured out using this smart data which helps in guiding a Travel company to make informed decisions. The discounts, schemes, tour packages, seasons to target and people to target are formulated using this data. Usually, surveys and social media sensing are done to know customer’s insights and behaviour.

Let’s look at how AI has influenced each aspect of the business

Bleisure – Personalized Experience

There are always a few who are up for a new challenge and adopt to new technology. Many hotels have started using an AI concierge. One great example of an AI concierge is Hilton World wide’s Connie, who is the first true AI-powered concierge bot.

Connie stands at 2 feet high and guests can interact with it during their check-in. Connie is powered by IBM’s Watson AI and uses WayBlazer travel database. It can provide information to guests on local attractions, places to visit, etc. Being an AI, it can learn and adapt and respond to each guest.

In the Travel business, Mezi, using Artificial Intelligence and Natural Language Processing, provides a personalized experience to Business travellers who usually are strapped for time. It talks about bringing on a concept of bleisure  (business+leisure) to address the needs of the workforce. A research done by them states that 84% of business travellers return feeling frustrated, burnt out and unmotivated. The kind of tedious and monotonous planning that goes into the travel booking could be the reason for it. With AI and NLP, Mezi collects preferences and generates suggestions so that a customized and streamlined experience is given and the issues faced by them are addressed properly.

Increased Productivity – Instant Connectivity

Increased productivity now begins with the search for the hotel, and technology has paved its way for the customer to access more data than ever before. Booking sites like Lola (www.lola.com) who provide on-demand travel services have developed technologies that can not only instantly connects people to their team of travel agents who find and book flights, hotels, and cars but have been able to empower their agents with tremendous technology to make research and decisions an easy process.

Intelligent Travel Assistants – Chatbots

Chatbot technology is another big strand of AI, and unsurprisingly, many travel brands have already launched their own versions in the past year or so. Skyscanner is just one example, creating a bot to help consumers find flights in Facebook Messenger. Users can also use it to request travel recommendations and random suggestions. Unlike ecommerce or retail brands using chatbots, which can appear gimmicky, there is an argument that examples like Skyscanner are much more relevant and useful for everyday consumers.

After all, with the arrival of many more travel search websites, consumers are being overwhelmed by choice – not necessarily helped by it.  Consequently, a bot like Skyscanner is able to cut through the noise, connecting with consumers in their own time and in the social media spaces they most frequently visit.

Recently, Aeromexico started using Facebook Messenger chatbot to answer the very generic questions by the customers. The main idea was to cater to 80% of questions which are usually the repeated ones and about common topics. Thus, to avoid a repetitive process, artificial intelligence is of great application. Airlines hugely benefit from this. KLM Royal Dutch Airlines uses artificial intelligence to respond to the queries of customers on twitter and facebook. It uses an algorithm from a company called Digital Genius which is trained on 60,000 questions and answers. Not only this, Deutsche Lufthansa’s bot Mildred can help in searching the cheapest fares.

Discovery & Data Analysis – Intelligent Recommendations

International hotel search engine Trivago acquired Hamburg, Germany machine learning startup, Tripl, as it ramps up its product with recommendation and personalization technology, giving them a customer-centric approach.

The AI algorithm gives tailored travel recommendations by identifying trends in users’ social media activities and comparing it with in-app data of like-minded users. With its launch in July 2015, users could sign up only through Facebook, potentially sharing oodles of profile information such as friends, relationship status, hometown, and birthday.

Persona based travel recommendations, use of customised pictures and text are now gaining ground to entice travellers to book your hotels. KePSLA’s travel recommendation platform is one of the first in the world to do this by using deep learning and NLP solutions.

With 81% of people believing that robots would be better at handling data than humans, there is also a certain level of confidence in this area from consumers.

Knowing your Travellers – Deep Customer Behaviour

Dorchester Collection is another hotel chain to make use of AI. However, instead of using it to provide a front-of-house service, it has adopted it to interpret and analyse customer behaviour in the form of raw data.

Partnering with technology company, RicheyTX, Dorchester Collection has helped to develop an AI platform called Metis.

Delving into swathes of customer feedback such as surveys and reviews (which would take an inordinate amount of time to manually find and analyse) it is able to measure performance and instantly discover what really matters to guests.

For example, Metis helped Dorchester to discover that breakfast it not merely an expectation – but something guests place huge importance on. As a result, the hotels began to think about how they could enhance and personalise the breakfast experience.

Flight Fare and Hotel Price Forecasting

Flight fares and hotel prices are ever-changing and vary greatly depending on the provider. No one has time to track all those changes manually. Thus, smart tools which monitor and send out timely alerts with hot deals are currently in high demand in the travel industry.

The AltexSoft data science team has built such an innovative fare predictor tool for one of their clients, a global online travel agency, Fareboom.com. Working on its core product, a digital travel booking website, they could access and collect historical data about millions of fare searches going back several years. Armed with such information, they created a self-learning algorithm, capable of predicting the future price movements based on a number of factors, such as seasonal trends, demand growth, airlines special offers, and deals.

With the average confidence rate at 75 percent, the tool can make short-term (several days) as well as long-term (a couple of months) forecasts.

Optimized Disruption Management

While the previous case is focused mostly on planning trips and helping users navigate most common issues while traveling, automated disruption management is somewhat different. It aims at resolving actual problems a traveler might face on his/her way to a destination point.

Mostly applied to business and corporate travel, disruption management is always a time-sensitive task, requiring instant response. While the chances to get impacted by a storm or a volcano eruption are very small, the risk of a travel disruption is still quite high: there are thousands of delays and several hundreds of canceled flights every day.

With the recent advances in technology, it became possible to predict such disruptions and efficiently mitigate the loss for both the traveler and the carrier. The 4site tool, built by Cornerstone Information Systems, aims at enhancing the efficiency of enterprise travel. The product caters to travelers, travel management companies, and enterprise clients, providing a unique set of features for real-time travel disruption management.

For example, if there is a heavy snowfall at your destination point and all flights are redirected to another airport, a smart assistant can check for available hotels there or book a transfer from your actual place of arrival to your initial destination.

Not only passengers are affected by travel disruptions; airlines bear significant losses every time a flight is canceled or delayed. Thus, Amadeus, one of the leading global distribution systems (GDS), has introduced Schedule Recovery system, aiming to help airlines mitigate the risks of travel disruption. The tool helps airlines instantly address and efficiently handle any threats and disruptions in their operations.

Future potential

So, we’ve already seen the travel industry capitalise on AI to a certain extent. But how will it evolve in the coming year?

Business travel

Undoubtedly, we’ll see many more brands using AI for data analysis as well as launching their own chatbots. There’s already been a suggestion that Expedia is next in line, but it is reportedly set to focus on business travel rather than holidaymakers. Due to the greater need for structure and less of a desire for discovery, it certainly makes sense that artificial intelligence would be more suited to business travellers.

Specifically, it could help to simplify the booking process for companies, as well as help eliminate discrepancies around employee expenses. With reducing costs and improving efficiency two of the biggest benefits, AI could start to infiltrate business travel even more so than leisure in the next 12 months.

Voice technology

Lastly, we can expect to see greater development in voice-activated technology.

With voice-activated search, the experience of researching and booking travel has the potential to become quicker and easier than ever before. Similarly, as Amazon Echo and Google Home start to become commonplace, more hotels could start to experiment with speech recognition to ramp up customer service.

This means devices and bots  could become the norm for brands in the travel industry.

Today’s artificial intelligence (AI) revolution has been made possible by the algorithm revolution. The machine learning algorithms researchers have been developing for decades, when cleverly applied to today’s web-scale data sets, can yield surprisingly good forms of intelligence. For instance, the United States Postal Service has long used neural network models to automatically read handwritten zip code digits. Today’s deep learning neural networks can be trained on millions of electronic photographs to identify faces, and similar algorithms may increasingly be used to navigate automobiles and identify tumors in X-rays. The IBM Watson information retrieval system could triumph on the game show “Jeopardy!” partly because most human knowledge is now stored electronically.

But current AI technologies are a collection of big data-driven point solutions, and algorithms are reliable only to the extent that the data used to train them is complete and appropriate. One-off or unforeseen events that humans can navigate using common sense can lead algorithms to yield nonsensical outputs.

Design thinking is defined as human-centric design that builds upon the deep understanding of our users (e.g., their tendencies, propensities, inclinations, behaviours) to generate ideas, build prototypes, share what you’ve made, embrace the art of failure (i.e., fail fast but learn faster) and eventually put your innovative solution out into the world.  And fortunately for us humans (who really excel at human-centric things), there is a tight correlation between the design thinking and artificial intelligence.

Artificial intelligence technologies could reshape economies and societies, but more powerful algorithms do not automatically yield improved business or societal outcomes. Human-centered design thinking can help organizations get the most out of cognitive technologies.

Divergence from More Powerful Intelligence To More Creative Intelligence

While algorithms can automate many routine tasks, the narrow nature of data-driven AI implies that many other tasks will require human involvement. In such cases, algorithms should be viewed as cognitive tools capable of augmenting human capabilities and integrated into systems designed to go with the grain of human—and organizational—psychology. We don’t want to ascribe to AI algorithms more intelligence than is really there. They may be smarter than humans at certain tasks, but more generally we need to make sure algorithms are designed to help us, not do an end run around our common sense.

Design Thinking at Enterprise Premise

Although cognitive design thinking is in its early stages in many enterprises, the implications are evident. Eschewing versus embracing design thinking can mean the difference between failure and success. For example, a legacy company that believes photography hinges on printing photographs could falter compared to an internet startup that realizes many customers would prefer to share images online without making prints, and embraces technology that learns faces and automatically generates albums to enhance their experience.

To make design thinking meaningful for consumers, companies can benefit from carefully selecting use cases and the information they feed into AI technologies. In determining which available data is likely to generate desired results, enterprises can start by focusing on their individual problems and business cases, create cognitive centres of excellence, adopt common platforms to digest and analyze data, enforce strong data governance practices, and crowdsource ideas from employees and customers alike.

In assessing what constitutes proper algorithmic design, organizations may confront ethical quandaries that expose them to potential risk. Unintended algorithmic bias can lead to exclusionary and even discriminatory practices. For example, facial recognition software trained on insufficiently diverse data sets may be largely incapable of recognizing individuals with different skin tones. This could cause problems in predictive policing, and even lead to misidentification of crime suspects. If the training data sets aren’t really that diverse, any face that deviates too much from the established norm will be harder to detect. Accordingly, across many fields, we can start thinking about how we create more inclusive code and employ inclusive coding practices.

CXO Strategy for Cognitive Design Thinking

CIOs can introduce cognitive design thinking to their organizations by first determining how it can address problems that conventional technologies alone cannot solve. The technology works with the right use cases, data, and people, but demonstrating value is not always simple. However, once CIOs have proof points that show the value of cognitive design thinking, they can scale them up over time.

CIOs benefit from working with business stakeholders to identify sources of value. It is also important to involve end users in the design and conception of algorithms used to automate or augment cognitive tasks. Make sure people understand the premise of the model so they can pragmatically balance algorithm results with other information.

Enterprise Behavioral Science – From Insights to Influencing Business Decisions

Every January, how many people do you know say that they want to resolve to save more, spend less, eat better, or exercise more? These admirable goals are often proclaimed with the best of intentions, but are rarely achieved. If people were purely logical, we would all be the healthiest versions of ourselves.

However, the truth is that humans are not 100% rational; we are emotional creatures that are not always predictable. Behavioral economics evolved from this recognition of human irrationality. Behavioral economics is a method of economic analysis that applies psychological insights into human behavior to explain economic decision-making.

Decision making is one of the central activities of business – hundreds of billions of decisions are made everyday. Decision making sits at the heart of innovation, growth, and profitability, and is foundational to competitiveness. Despite this degree of importance, decision making is poorly understood, and badly supported by tools. A study by Bain & Company found that decision effectiveness is 95% correlated with companies’ financial performance.

Enterprise Behavioral Science is not only about understanding potential outcomes, but to completely change outcomes, and more specifically, change the way in which people behave. Behavioral Science tells us that to make a fundamental change in behavior that will affect the long-term outcome of a process, we must insert an inflection point.

As an example, you are a sales rep and two years ago your revenue was $1 million. Last year it was $1.1 million, and this year you expect $1.2 million in sales. The trend is clear, and your growth has been linear and predictable. However, there is a change in company leadership and your management has increased your quota to $2 million for next year. What is going to motivate you to almost double your revenues? The difference between expectations ($2 million) and reality ($1.2 million) is often referred to as the “behavioral gap” . When the behavioral gap is significant, an inflection point is needed to close that gap. The right incentive can initiate an inflection point and influence a change in behavior. Perhaps that incentive is an added bonus, President’s Club eligibility, a promotion, etc.

Cognitive Design Thinking – The New Indispensable Reskilling Avenue

Artificial intelligence, machine learning, big data analytics and mobile and software development will be the top technology areas where the need for re-skilling will be the highest. India will need 700 million skilled workforce by 2022 to meet the demands of a growing economy. Hence, while there is a high probability that machine learning and artificial intelligence will play an important role in whatever job you hold in the future, there is one way to “future-proof” your career…embrace the power of design thinking.

In fact, integrating design thinking and artificial intelligence can give you “super powers” that future-proof whatever career you decide to pursue. To meld these two disciplines together, one must:

• Understand where and how artificial intelligence and behavioural science can impact your business initiatives. While you won’t need to write machine learning algorithms, business leaders do need to learn how to “Think like a data scientist” in order understand how AI can optimize key operational processes, reduce security and regulatory risks, uncover new monetization opportunities.
• Understand how design thinking techniques, concepts and tools can create a more compelling and emphatic user experience with a “delightful” user engagement through superior insights into your customers’ usage objectives, operating environment and impediments to success.

Design thinking is a mindset. IT firms are trying to move up the curve. Higher-end services that companies can charge more is to provide value and for that you need to know that end-customers needs. For example, to provide value services to banking customers is to find out what the bank’s customer needs are in that country the banking client is based. Latent needs come from a design thinking philosophy where you observe customer data, patterns and provide a solution that the customer does not know. Therefore, Companies will hire design thinkers as they can predict what the consumer does not know and hence charge for the product/service from their clients. Idea in design thinking is to provide agile product creation or solutions.

Without Design Thinking & Behavioural Science, AI Will be Only an Incremental Value

Though organizations understand the opportunity that big data presents, many struggles to find a way to unlock its value and use it in tandem with design thinking – making “big data a colossal waste of time & money.” Only by combining quantitative insights gathered using AI, machine/deep learning, and qualitative research through behavioural science, and finally design thinking to uncover hidden patterns and leveraging it to understand what the customer would want, will we be able to paint a complete picture of the problem at hand, and help drive towards a solution that would create value for all stakeholders.

AlphaGo caused a stir by defeating 18-time world champion Lee Sedol in Go, a game thought to be impenetrable by AI for another 10 years. AlphaGo’s success is emblematic of a broader trend: An explosion of data and advances in algorithms have made technology smarter than ever before. Machines can now carry out tasks ranging from recommending movies to diagnosing cancer — independently of, and in many cases better than, humans. In addition to executing well-defined tasks, technology is starting to address broader, more ambiguous problems. It’s not implausible to imagine that one day a “strategist in a box” could autonomously develop and execute a business strategy. We’ve spoken to CXOs and leaders who express such a vision — and companies such as Amazon and Alibaba are already beginning to make it a reality.

Business Processes – Increasing productivity by reducing disruptions

AI algorithms are not natively “intelligent.” They learn inductively by analyzing data. While most leaders are investing in AI talent and have built robust information infrastructures,

As Airbus started to ramp up production of its new A350 aircraft, the company faced a multibillion-euro challenge. The plan was to increase the production rate of that aircraft faster than ever before. To do that, they needed to address issues like responding quickly to disruptions in the factory. Because they will happen. Airbus turned to artificial intelligence. It combined data from past production programs, continuing input from the A350 program, fuzzy matching, and a self-learning algorithm to identify patterns in production problems.

AI led to rectification of about 70% of the production disruptions for Airbus, by matching to solutions used previously — in near real time.

Just as it is enabling speed and efficiency at Airbus, AI capabilities are leading directly to new, better processes and results at other pioneering organizations. Other large companies, such as BP, Infosys, Wells Fargo, and Ping An Insurance, are already solving important business problems with AI. Many others, however, have yet to get started.

Integrated Strategy Machine – The Implementation Scope Augmented AI

The integrated strategy machine is the AI analog of what new factory designs were for electricity. In other words, the increasing intelligence of machines could be wasted unless businesses reshape the way they develop and execute their strategies. No matter how advanced technology is, it needs human partners to enhance competitive advantage. It must be embedded in what we call the integrated strategy machine. An integrated strategy machine is the collection of resources, both technological and human, that act in concert to develop and execute business strategies. It comprises a range of conceptual and analytical operations, including problem definition, signal processing, pattern recognition, abstraction and conceptualization, analysis, and prediction. One of its critical functions is reframing, which is repeatedly redefining the problem to enable deeper insights.

Amazon represents the state-of-the-art in deploying an integrated strategy machine. It has at least 21 data science systems, which include several supply chain optimization systems, an inventory forecasting system, a sales forecasting system, a profit optimization system, a recommendation engine, and many others. These systems are closely intertwined with each other and with human strategists to create an integrated, well-oiled machine. If the sales forecasting system detects that the popularity of an item is increasing, it starts a cascade of changes throughout the system: The inventory forecast is updated, causing the supply chain system to optimize inventory across its warehouses; the recommendation engine pushes the item more, causing sales forecasts to increase; the profit optimization system adjusts pricing, again updating the sales forecast.

Manufacturing Operations – An AI assistant on the floor

CXOs at industrial companies expect the largest effect in operations and manufacturing. BP plc, for example, augments human skills with AI in order to improve operations in the field. They have something called the BP well advisor that takes all of the data that’s coming off of the drilling systems and creates advice for the engineers to adjust their drilling parameters to remain in the optimum zone and alerts them to potential operational upsets and risks down the road. They  are also trying to automate root-cause failure analysis to where the system trains itself over time and it has the intelligence to rapidly assess and move from description to prediction to prescription.

Customer-facing activities – near real time scoring

Ping An Insurance Co. of China Ltd., the second-largest insurer in China, with a market capitalization of $120 billion, is improving customer service across its insurance and financial services portfolio with AI. For example, it now offers an online loan in three minutes, thanks in part to a customer scoring tool that uses an internally developed AI-based face-recognition capability that is more accurate than humans. The tool has verified more than 300 million faces in various uses and now complements Ping An’s cognitive AI capabilities including voice and imaging recognition.

AI Strategy for Different Operational Models

To make the most of this technology implementation in various business operations in your enterprise, consider the three main ways that businesses can or will use AI:

Assisted intelligence

Now widely available, improves what people and organizations are already doing. For example, Google’s Gmail sorts incoming email into “Primary,” “Social,” and “Promotion” default tabs. The algorithm, trained with data from millions of other users’ emails, makes people more efficient without changing the way they use email or altering the value it provides. Assisted intelligence tends to involve clearly defined, rules-based, repeatable tasks.

Assisted intelligence apps often involve computer models of complex realities that allow businesses to test decisions with less risk. For example, one auto manufacturer has developed a simulation of consumer behavior, incorporating data about the types of trips people make, the ways those affect supply and demand for motor vehicles, and the variations in those patterns for different city topologies, marketing approaches, and vehicle price ranges. The model spells out more than 200,000 variations for the automaker to consider and simulates the potential success of any tested variation, thus assisting in the design of car launches. As the automaker introduces new cars and the simulator incorporates the data on outcomes from each launch, the model’s predictions will become ever more accurate.

Augmented intelligence

Augmented Intelligence, emerging today, enables organizations and people to do things they couldn’t otherwise do. Unlike assisted intelligence, it fundamentally alters the nature of the task, and business models change accordingly.

For example, Netflix uses machine learning algorithms to do something media has never done before: suggest choices customers would probably not have found themselves, based not just on the customer’s patterns of behavior, but on those of the audience at large. A Netflix user, unlike a cable TV pay-per-view customer, can easily switch from one premium video to another without penalty, after just a few minutes. This gives consumers more control over their time. They use it to choose videos more tailored to the way they feel at any given moment. Every time that happens, the system records that observation and adjusts its recommendation list — and it enables Netflix to tailor its next round of videos to user preferences more accurately. This leads to reduced costs and higher profits per movie, and a more enthusiastic audience, which then enables more investments in personalization (and AI).

Autonomous intelligence

Being developed for the future, Autonomous Intelligence creates and deploys machines that act on their own. Very few autonomous intelligence systems — systems that make decisions without direct human involvement or oversight — are in widespread use today. Early examples include automated trading in the stock market (about 75 percent of Nasdaq trading is conducted autonomously) and facial recognition. In some circumstances, algorithms are better than people at identifying other people. Other early examples include robots that dispose of bombs, gather deep-sea data, maintain space stations, and perform other tasks inherently unsafe for people.

As you contemplate the introduction of artificial intelligence, articulate what mix of the three approaches works best for you.

• Are you primarily interested in upgrading your existing processes, reducing costs, and improving productivity? If so, then start with assisted intelligence, probably with a small group of services from a cloud-based provider.
• Do you seek to build your business around something new — responsive and self-driven products, or services and experiences that incorporate AI? Then pursue an augmented intelligence approach, probably with more complex AI applications resident on the cloud.
• Are you developing a genuinely new technology? Most companies will be better off primarily using someone else’s AI platforms, but if you can justify building your own, you may become one of the leaders in your market.

The transition among these forms of AI is not clean-cut; they sit on a continuum. In developing their own AI strategy, many companies begin somewhere between assisted and augmented, while expecting to move toward autonomous eventually.

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