Why AI in construction?

Of all the game-changing innovations driven by technology and artificial intelligence in the world today, the potential of one key sector remains untapped – the construction industry.

According to McKinsey, the engineering and construction sector globally is valued to be worth $10 tn per year. While that is a respectable size, the construction industry overall has largely been slow in the uptake of inventions in the technology arena. In fact, several construction business houses in India tend to be family-owned and extremely traditionally-run, and have tremendous inertia in embracing new age technologies. 

However, the past few years is seeing a change in the way construction firms operate. With well-funded global start-ups such as WeWork entering the fray with an AI and analytics forward approach to real estate development; industry incumbents need to up their game in order to stay relevant. While McKinsey expects the permeation of AI in the construction industry to be modest right now, it does represent an opportunity for early adopters to catch the bull by the horns and build a sizeable competitive advantage. Those from this industry that have a ponderous and slow uptake of new technology will surely be eaten up by their competitors. Through this article, we explore some artificial intelligence interventions that can be transformative for the construction and real estate industry at large.

Image recognition for managing risk, safety and quality

The definition of Data Breaches in current times have evolved from, happening under ‘malicious intent’, to also cover those which have been occurring as a consequences of bad data policies and regulation oversight. This means even policies that have been deemed legally screened might end up, in certain circumstances, in opening doors to some significant breach of data, user privacy and ultimately user trust.

For example, recently, Facebook banned data analytics company Cambridge Analytica from buying ads from its platform. The voter profiling firm allegedly procured 50 million physiological profiles of people through a research application developer Aleksandr Kogan, who broke Facebook’s data policies by sharing data from his personality-prediction app, that mined information from the social network’s users.

Kogan’s app, ‘thisisyourdigitallife’ harvested data not only from the individuals participating in the game, but also from everyone on their friend list. Since Facebook’s terms of services weren’t so clear back in 2014 the app allowed Kogan to share the data with third parties like Cambridge Analytica. This means policy wise it is a grey area whether the breach could be considered ‘unauthorized’, but it is clear that it happened without any express authorization from Facebook. This personal information was subsequently used to target voters and sway public opinion

This is different than the site hackings where credit card information was actually stolen at major retailers, the company in question, Cambridge Analytica, actually had the right to use this data. The problem is they used this information without permission in a way that was overtly deceptive to both Facebook users and Facebook itself.

Fallouts of Data Breaches: Developers left to deal with Tighter Controls

Facebook will become less attractive to app developers if it tightens norms for data usage as a fallout of the prevailing controversy over alleged misuse of personal information mined from its platform, say industry members.

India has the second largest developer base for Facebook, a community that builds apps and games on the platform and engage its users. With 241 million users, the country last July over took the US as the largest userbase for the social network platform.

There will be more scrutiny now. When you do, say, a sign on. The basic data (you can get) is the user’s name and email address, even which will undergo tremendous scrutiny before they approve it. That will have an impact on the timeline. The viral effect) could decrease. Now, without explicit rights from users, you cannot reach out to his/her contacts. Thus, the overhead goes on to the developers because of such data breaches, which shouldn’t have occurred in the first place had the policies surrounding user data were more distinct and clear.

Renewed Focus to Conflicting Data Policies and Human Factors

These kinds of passive breaches that happen because of unclear and conflicting policies instituted by Facebook provides us a very clear example of how active breaches (involving malicious attacks) and passive breaches (involving technically authorized but legally unsavoury data sharing) need to be given equal priority and should both be considered pertinent focus of data protection.

While Facebook CEO Mark Zuckerberg has vowed to make changes to prevent these types of information grabs from happening in the future, many of those tweaks will be presumably made internally. Individuals and companies still need to take their own action to ensure their information remains as protected and secure as possible.

Humans are the weakest link in data protection, and potentially even the leading cause for the majority of incidents in recent years. This debacle demonstrates that cliché to its full extent. Experts believe that any privacy policy needs to take into account all third parties who get access to the data too. While designing a privacy policy one needs to keep the entire ecosystem in mind. For instance, a telecom player or a bank while designing their privacy policy will have to take into account third parties like courier agencies, teleworking agencies, and call centers who have access to all their data and what kind of access they have.

Dealing with Privacy in Analytics: Privacy-Preserving Data Mining Algorithms

The problem of privacy-preserving data mining has become more important in recent years because of the increasing ability to store personal data about users, and the increasing sophistication of data mining algorithms to leverage this information. A number of algorithmic techniques such as randomization and k-anonymity, have been suggested in recent years in order to perform privacy-preserving data mining. Different communities have explored parallel lines of work in regards to privacy preserving data mining:

Privacy-Preserving Data Publishing: These techniques tend to study different transformation methods associated with privacy. These techniques include methods such as randomization, k-anonymity, and l-diversity. Another related issue is how the perturbed data can be used in conjunction with classical data mining methods such as association rule mining.

Changing the results of Data Mining Applications to preserve privacy: In many cases, the results of data mining applications such as association rule or classification rule mining can compromise the privacy of the data. This has spawned a field of privacy in which the results of data mining algorithms such as association rule mining are modified in order to preserve the privacy of the data.

Query Auditing: Such methods are akin to the previous case of modifying the results of data mining algorithms. Here, we are either modifying or restricting the results of queries.

Cryptographic Methods for Distributed Privacy: In many cases, the data may be distributed across multiple sites, and the owners of the data across these different sites may wish to compute a common function. In such cases, a variety of cryptographic protocols may be used in order to communicate among the different sites, so that secure function computation is possible without revealing sensitive information.

Privacy Engineering with AI

Privacy by Design is a policy concept was introduced the Data Commissioner’s Conference in Jerusalem, and over 120 different countries agreed they should contemplate privacy in the build, in the design. That means not just the technical tools you buy and consume, [but] how you operationalize, how you run your business; how you organize around your business and data.

Privacy engineering is using the techniques of the technical, the social, the procedural, the training tools that we have available, and in the most basic sense of engineering to say, “What are the routinized systems? What are the frameworks? What are the techniques that we use to mobilize privacy-enhancing technologies that exist today, and look across the processing lifecycle to build in and solve for privacy challenges?”

It’s not just about individual machines making correlations; it’s about different data feeds streaming in from different networks where you might make a correlation that the individual has not given consent to with personally identifiable information. For AI, it is just sort of the next layer of that. We’ve gone from individual machines, networks, to now we have something that is looking for patterns at an unprecedented capability, that at the end of the day, it still goes back to what is coming from what the individual has given consent to? What is being handed off by those machines? What are those data streams?

Also, there is the question of ‘context’. The simplistic policy of asking users if an application can access different venues of their data is very reductive. This does not, in an measure give an understanding of how that data is going to be leveraged and what other information about the users would the application be able to deduce and mine from the said data? The concept of privacy is extremely sensitive and not only depends on what data but also for what purpose. Have you given consent to having it used for a particular purpose? So, I think AI could play a role in making sense of whether data is processed securely.

The Final Word: Breach of Privacy as Crucial as Breach of Data

It is undeniably so that we are slowly giving equal, if not more importance to breach of privacy as compared to breach of data, which will eventually target even the policies which though legally acceptable or passively mandated but resulted in compromise of privacy and loss of trust. Because there is no point claiming one is legally safe in their policy perusal if the end result leads to the users being at the receiving end.

This would require a comprehensive analysis of data streams, not only internal to an application ecosystem, like Facebook, but also the extended ecosystem involving all the players it is channeling the data sharing to, albeit in a policy-protected manner. This will require AI enabled contextual decision making to come to terms as what policies could be considered as eventually breaching the privacy in certain circumstances.

Longer-term, though, you’ve got to write that ombudsman. We need to be able to engineer an AI to serve as an ombudsman for the AI itself.

When it comes to AI, businesses think ambitiously. Nearly 85% of executives believe AI will allow their company to obtain or sustain a competitive advantage in the marketplace. Contrastingly, just one in five companies have incorporated AI into their organization and less than 39% of companies have an AI strategy.

Exactly why is AI so disruptive to traditional business models and traditional notions of industry competition? A useful way to analyse the situation is by looking at Porter’s model of the five forces of industry competition and exploring how artificial intelligence is impacting each of the various forces.

According to Michael E. Porter, in one of his landmark books, titled Competitive Strategy, “In any industry, whether it is domestic or international or produces a product or a service, the rules of competition are embodied in five competitive forces: the entry of new competitors, the threat of substitutes, the bargaining power of buyers, the bargaining power of suppliers, and the rivalry among the existing competitors.”

Figure 1: Porter’s Five Forces

Let’s look at each of these five forces and examine the role and impact of AI:

The entry of new competitors

There’s no doubt that AI is changing the nature of competition. Today, it’s not just traditional industry competitors you need to worry about, but new entrants from outside your industry, equipped with new AI based business models and value propositions.

This is often tech giants and startups that have envisioned and built a new business model from the ground up, powered by a new platform ecosystem for AI. They’re leveraging the familiar social, mobile, analytics and cloud technologies, but are often adding in personas and context, intelligent automation, chatbots and the Internet of Things, to further enhance the value proposition of their platform.

Why can new entrants move in so easily? Digital business changes the rules by lowering the traditional barriers to entry. A digitally based business model requires far less capital and can bring large economies of scale for example. Read more about how AI Startups are creating disruptive competition here.

The threat of substitutes

The threat of substitutes is high in many industries since switching costs are low and buyer propensity to substitute is high. For example, In the taxi services, customers can easily switch from traditional models to the new digital app based taxi services, employing AI routines to create differential pricing and intelligent route mapping to increase margin as well as decrease price for the customers. Propensity to switch from the traditional model is high due to consumer wait times for taxis, lack of visibility into taxi location and so on.

In case of BPO industry, the advent of AI has been extremely disruptive, with their clients completely substituting their services with building in-house automation offerings and circumventing their need, sometimes completely. Read more in detail about the disruption of BPO/BPM by AI here.

The bargaining power of buyers

Perhaps the strongest of the five forces impacting industry competition is the bargaining power of buyers since the biggest driver of AI and digital business comes from the needs and expectations of consumers and customers themselves.

This bargaining power lays out a new set of expectations for the AI and digital customer experience and necessitates continual corporate innovation across business models, processes, operations, products and services.

For example, the most used instances of chatbots are through customer support, and now they are heading in the direction of changing the retail sector altogether. The expectations of the Millennials are directing the course of this new technology. This is why chatbots have the burden to exceed the expectations in the retail sector.

Also, in another example, in the customer facing marketing aspect, AI is causing circular rise in customer expectations as rise of expectations, mostly from millennials, has forced the companies to adopt an AI solution to the problem, which further has emboldened their expectations. 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. Read more about how AI is accentuating customer experience to address rising expectations Here.

The bargaining power of suppliers

Suppliers can accelerate or slow down the adoption of a AI based business model based upon how it impacts their own situation. Those pursuing AI models themselves, such as the use of APIs to streamline their ability to form new partnerships and manage existing ones, may help accelerate your own model.

Those who are suppliers to the traditional models, and who question or are still determining their new role in the digital equivalent, may use their bargaining power to slow down or dispute the validity or legality of the new model.

Good examples are the legal and business issues surfacing around the digital-sharing economy (i.e. ride-sharing, room-sharing etc.) where suppliers and other constituents work to ensure the AI based business model and process innovations (like route optimization, or deep customer behaviour analysis using private data) still adhere to established rules, regulations, privacy, security and safety. This is a positive and needed development since, coupled with bargaining power of buyers, it can help to keep new models “honest” in terms of how they operate.

The rivalry among the existing competitors

A lot of organisations are in exploratory stages as they realise that their strategy and customer engagement needs to get smarter. The combination of optimism and fear that clients today have shows that for them it is a competitive necessity to adopt AI and digital technologies.

In 20 years, probably every job will be touched by AI. The technology is growing universally. WhatsApp and Facebook — everything is driven by AI. And what this means is that on the job front, there may be blood. Once AI, ML, and virtual and augmented reality go mainstream, these technologies will prove to be a huge job creator.

But currently, the most competitive space in AI adoption is in the implementation of chatbots across industries and functions. While we might see chatbots starting to appear through the likes of Facebook Messenger and WhatsApp platforms in the coming 12 months, and will be dedicating teams of engineers to train the platforms, rather than relying on the general public. Read more about the competitive atmosphere and underlying need to better customer experience using chatbot here.

How AI will transform Strategic Planning Process

How can managers — from the front lines to the C-suite — thrive in the age of AI? In many ways, the lack of understanding when it comes to AI is due to the variety of ways AI can be implemented as a part of strategic planning for a business. Different industries, or even different companies within the same industry, may use AI in different ways. Ping An, which employs 110 data scientists, has launched about 30 CEO-sponsored AI initiatives that support, in part, its vision – that technology will be the key driver to deliver top-line growth for the company in the years to come. Yet in sharp contrast, elsewhere in the insurance industry, other large companies’ AI initiatives are limited to experimenting with chatbots. Obviously, integrating AI is not going to be simple. There will be a massive learning curve for organizations before they’re able to start implementing AI effectively. But the core shift in strategic planning will happen in the following ways:

AI will take over almost all Administrative Tasks

According to an HBR report, managers across all levels spend more than half of their time on administrative coordination and control tasks. (For instance, a typical store manager or a lead nurse at a nursing home must constantly juggle shift schedules because of staff members’ illnesses, vacations, or sudden departures.) These are the very responsibilities that the same managers expect to see AI affecting the most. And they are correct: AI will automate many of these tasks.

Figure 2: Source –  HBR (How Artificial Intelligence Will Redefine Management)

For example, in case of report writing The Associated Press expanded its quarterly earnings reporting from approximately 300 stories to 4,400 with the help of AI-powered software robots. In doing so, technology freed up journalists to conduct more investigative and interpretive reporting.

Strategy Managers will focus more on Judgement-oriented Creative Thinking Work

The human factor, which AI still cannot permeate – the application of experience, expertise and a capacity to improvise, to critical business decisions and practices – need to be focused on by strategy managers. Many decisions require insight beyond what artificial intelligence can squeeze from data alone. Managers use their knowledge of organizational history and culture, as well as empathy and ethical reflection. Managers we surveyed have a sense of a shift in this direction and identify the creative thinking skills and experimentation, data analysis and interpretation, and strategy development as three of the four top new skills that will be required to succeed in the future. And since the potential of machine learning is the ability to help make decisions, the AI technology would be better placed as an assisting hand than administrative mind.

Think of AI not as Machines, but Colleagues

Managers who view AI as a kind of colleague will recognize that there’s no need to “race against a machine.” While human judgment is unlikely to be automated, intelligent machines can add enormously to this type of work, assisting in decision support and data-driven simulations as well as search and discovery activities. In fact, 78% of the surveyed managers believe that they will trust the advice of intelligent systems in making business decisions in the future.

Not only will AI augment managers’ work, but it will also enable managers to interact with intelligent machines in collegial ways, through conversation or other intuitive interfaces.

For example, Kensho Technologies, a provider of next-generation investment analytics, allows investment managers to ask investment-related questions in plain English, such as, “What sectors and industries perform best three months before and after a rate hike?” and get answers within minutes.

Design Thinking needs to be adopted both ways – Managers & AI

While managers’ own creative abilities are vital, perhaps even more important is their ability to harness others’ creativity. Manager-designers bring together diverse ideas into integrated, workable, and appealing solutions. Creative thinking and experimentation is a key skill area that managers need to learn to stay successful as AI increasingly takes over administrative work. ‘Collaborative Creativity’ is the operating word here.

But this doesn’t mean that design thinking necessarily need to become a forte exclusive to managers. Even though AI engines may not have reached radical thinking and improvisation as humans, AI 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. This calls for Divergence from More Powerful Intelligence To More Creative Intelligence in AI.

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. Read more about Design Thinking in AI here.

Create New Business Processes manifested from Augmented Working Strategy

Simply put, my recommendation is to adopt AI in order to automate administration and to augment but not replace human judgment. If the current shortage of analytical talent is any indication, organizations can ill afford to wait and see whether their managers are equipped to work alongside AI. This calls for change in business processes, and the way they are implemented itself. To navigate in an uncertain future, managers must explore early, and experiment with AI and apply their insights to the next cycle of experiments.

AI augmentation will drive the adoption of new key performance indicators. AI will bring new criteria for success: collaboration capabilities, information sharing, experimentation, learning and decision-making effectiveness, and the ability to reach beyond the organization for insights.

Accordingly, organizations need to develop training and recruitment strategies for creativity, collaboration, empathy, and judgment skills. Leaders should develop a diverse workforce and team of managers that balance experience with creative and social intelligence — each side complementing the other to support sound collective judgment.

Final Word

While oncoming AI disruptions in Management Principles and Strategic Planning space won’t arrive all at once, the pace of development is faster and the implications more far-reaching than most executives and managers realize. Those managers capable of assessing what the workforce of the future will look like can prepare themselves for the arrival of AI.

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.

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

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.

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

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

GST – A Data Analytics Powerhouse

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

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

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

Nab Tax Evaders, Boost Domestic IT Biz

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

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

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

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

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

Here’s a look at some enterprise ready solutions:

SAP HANA:

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

Microsoft India:

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

Intuit:

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

So how does this all start ?

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

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

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

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

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

Stiffening competition

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

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

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

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

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

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

Leveraging Digital

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

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

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

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

All about algorithms

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

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

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

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

Concerns Going Forward

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

Towards the future

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

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