Global Capability Centers (GCC’s) in India are at an important inflection point. As multinational corporations continue to move to a digital and AI-first paradigm, they are looking at their GCC’s to provide emerging technologies leadership to drive this transformation.

It’s been an exciting evolution for the GCC’s over the last few years. In the not too distant past, multinational corporations look at their offshore captives to contain costs for repetitive, low-value business processes. From there, we saw shared services centers capture a larger slice of the pie in day-to-day business operations of their MNC counterparts, alongside developing centers for research, development, innovation and business transformation. Captives morphed into capability centers, wherein new skills and competencies could be swiftly incubated and scaled.

The numbers pan out well for GCC’s – with nearly a million professionals employed, across 1,500 GCC’s in India, netting an export revenue of over $23mn, the sun is shining brightly for GCC’s. Indian GCCs account for over a fifth of IT-BPM exports and a fourth of India’s export employees. According to a report by analyst firm Nomura, GCCs are growing faster today in terms of revenue attribution than their large outsourcing counterparts (12.4% CAGR for GCCs vs 10.7% for service providers, over the last 5 years). 27% of US-based Fortune 2000 companies already have GCCs in India. GCCs are becoming the centralized technology procurement arm for MNCs as 50% of the Fortune 2000 are planning to shift vendor management to their offshore entities, for the synergistic benefits, as well as to drive outsourcing costs down.

Here’s the inflection point though – as MNCs grapple in an uncertain business environment and business models, changing consumer preferences and consumption modes and digitalization in most areas of the business, they are looking at their GCC leaders to provide the technology disruption that their traditional business desperately needs. For the past few years, analytics and AI has taken a robust foothold in the GCCs, with their India-based talent powering evidence-backed, data-driven decisions for their parent organizations. The next generation of the GCC’s will be expected to provide autonomous decision support and an AI-augmented human intelligence. GCC leaders will need to harness the burgeoning power of AI technologies to power corporate decisions, automate repetitive, low-value tasks through robotization and reinvent business models for the continued success of their business in the new world of business. Digital will be the core element of business model re-design.

Of the multiple reasons driving insourcing decisions, perhaps the most important one is the strong business process integration that GCCs provide. Rather than relying on the volume provided by outsourced companies, MNCs realize that they need to meld quality output with high productivity, delivered by professionals that can reimagine current business functions. Enterprises are increasingly seeing the long-term benefits of investing in a world-class offshore capability center and prioritizing driving investments to these entities. With great investments come great expectations – they need their offshore GCC leaders to have a multidimensional business orientation and act as the key intermediary between the strategic boardroom and the operational engine room.

The future of the GCC is digital and AI-first and to that end, we need to re-imagine the future of the GCC in that direction. Here’s a primer on how AI transformation can be shaped within GCC’s :

Assess Maturity and Develop Roadmap

The first step is doubtless to assess the current state, the desired future state and the gap that exists between the two. Assessments and roadmap development need to be performed in two vital areas – technology and people.

Technology Assessment and Roadmap:

The first step is foundational to the AI and digital reengineering for the GCC. GCC leaders need to take stock of all the processes performed at the center, along with the tools and software driving them. The first step is to classify these processes into traditional vs digital IT. Once this is done, leaders need to further split the traditional IT processes into 3 sub-segments – reimagine, leave as-is or scrap. Whether a software-enabled process has strong business justification for the present and the future will define whether it is scrapped or not.

For the processes that do not get junked, leaders need to check if there are powerful, maturing digital options available – that can improve speed, accuracy and outcomes from the process through digital reengineering. If there is – then that process is ripe for reimagination. If not, and there is a strong business case to keep it as-is, leaders need to put it on a ‘Watch list’ and keep track of technology evolution and commercial-grade solutions emerging in this space. Further, for the reimagined processes, GCC leaders need to also assess the range of technology options available – from RPA to Deep Learning – and develop a roadmap for the automatization of these processes. For instance, deep learning could be progressively applied for high-value tasks which execute complex decision-making, while RPA could be quickly implemented to automate routine tasks, such as report generation etc.

People Assessment and Roadmap:

A similar exercise should also be done for the GCC employees. Leaders need to take stock of the talent pool available within the GCC and map it with the future skills required. Is there enough talent within the current GCC that can be updated with digital skills to develop and run future applications? Or would there be a need to augment internal talent with external consultants – is a key question to ask on the journey to GCCs’ digital transformation. This skill assessment needs to be combined with internal trainings to move existing employees into new roles. For instance, could a portion of the analytics team be moved into automated insight generation, using machine learning? Or can some of the better developers be trained into full-stack developers to build the technology backbone for the organization?

This kind of skill assessment and continuous training will provide the GCC leaders with a continuously updated understanding of the human assets available that can drive enterprise digital transformation. Where certain niche skills may not be available, leaders can look to outsource from topical service providers to help set up their processes and transfer the day-to-day system updates back to the GCC.

Re-engineering the Entity

Once the skills and technology are suitably assessed, the next step is to gear the GCC towards a new set of processes and practices that will help it sustain this digital drive. The new digital and AI-first GCC needs an entirely new set of standards to measure business value delivered and technology performance. This requires a reengineering exercise to change processes, evaluation metrics, and mindsets. Three key factors are at play here:

Process Augmentation:

First, the GCC needs to identify a whole new set of program management practices to build and sustain a digital mindset.

The first of these is the Automation Scorecard. Once the technology assessment and roadmap are completed and the automatable processes are identified, they should be listed onto this scorecard to track and monitor the extend of automation performed on each process.

The second intervention is progressively prioritizing scalable, cloud-based, digital-first software. There is often a strong proclivity to trust and use traditional IT software and this mindset needs to be evolved towards more SaaS-based, API-driven software – which can help organizations dynamically scale the costs and utilization up or down, based on business needs. By moving to a more service-oriented architecture model, GCCs can improve system availability and uptime.

The final intervention is people augmentation. While GCCs have progressively started and scaled their accelerator programs to identify breakthrough technologies solutions, they need to take the people and software integration to the next level. The mandate for these accelerators should be closely tied to the business expectations (as per the technology assessment and roadmap and automation scorecard mentioned above) and their success should be measured through the exponentiality of the results they deliver, not just basic productivity improvements. Additionally, GCC leaders should also seek process and technology guidance from outside consultants so that the accelerator remains true to its purpose and channels the needs of the business

New Metrics Development

The world of digital and AI will require an entirely new set of metrics. While cost optimization and quality of outcomes will remain paramount for any GCC, leaders need to reinvent the intermediate metrics that contribute to productivity and quality metrics. For instance, GCC leaders need to actively capture the extent of automatization delivered in the enterprise, by measuring the man-hours saved (total and monthly). Additionally, they could also leverage the automation scorecard to show progress on the automatization of processes. Thirdly, they need to measure and showcase the quantum of speed and accuracy that is delivered by the new digital process as opposed to traditional IT to their HQs, to highlight outcomes and achievements. Fourth, GCC employees need to be measured for their adeptness at emerging technologies, how much training has been delivered and internalized by employees. 

Evangelize Reverse Innovation

While several GCCs do deliver reverse innovation, the research and development of industry-specific commercial-grade AI and digital solutions should be one of the top evaluation criteria for GCC leaders. Indian executives have a strong frugal mindset, which can naturally deliver innovation under cost constraints – which can then be progressively leveraged by others in similar markets and situations. Identifying processes where reverse innovation can be applied and then commercialized upstream needs to be a top priority for GCC leaders to improve the revenue attributed to their entities. To do so, it is critical to first assess which technology and operational assets they own, that could be useful across new markets.

As Cisco VP – Dan Scheinman once famously said, “We came to India for the costs, we stayed for the quality, and we are now investing for the innovation”. GCCs have quickly moved from invisible, low-value business processing units to invisible high-value technology centers to now visible, high-value AI and Digital innovation hubs. The expectation is to now deliver the digital and AI-centric future for their parent enterprises .

Technology proliferation and changing socio-economic factors is ushering tumultuous change in the old paradigm of work. Today, with the anvil of ‘gig-economy’ – a collective talent marketplace of independent workforce working on recurring short-term assignments; we are now at the definitive cusp of a new reality of the workforce – working professionals will not only change jobs but will take multiple career switches while being expected to continuously unlearn and relearn new skills along the way. 

The future of work is here. It is time to shape the future of education.

According to multiple studies, success in the gig economy will be centered around 3 competencies which I call the 3 C’s – Creativity, Curiosity, and Collaboration. While children are naturally curious and creative, it is more important than ever before for academia nurture and sharpens these two qualities, while adding a core competency of collaboration, by imbibing them in their teaching methods. 

In the continuum, I strongly believe that education in the time to come will go in for a fundamental change. Here’s my take on the future of education: 

Gamified Education

According to a World Economic Forum report, there is enormous potential to improve the social and emotional skills of students by incorporating the use of play in their education – which in turn can provide a boost to their collaborative skills and drive curiosity. Developing these skills will require three types of games, namely 

Role-playing Games -creating a narrative arc through a sequence of events and providing them with a variety of options for interacting with the game through their characters. Role-playing games also allow students to explore multiple paths and revisit previously explored times and experiences.

Strategy Games -multiple students partaking in a quest to manage the strategic planning and deployment of scarce resources

Sandbox Games – focusing on open-ended exploration, being resourceful and taking initiative among a group of players to achieve a shared goal.

Unbundled Curriculums

It may no longer be productive to attend 3 -4 years of graduate school, followed by post-graduate education. In the gig economy, students and corporates will unlock shared benefits of skills-centric learning, followed by a stint at the workplace, before going back to school and acquiring new skills. While this will reduce the time and cost of learning; it will also help students apply their skills in the workforce and gain the much-needed hands-on experience. By seeing their classroom learnings in action, it will also spur curiosity to learn more and do more in the future.

Increased Mobility Between Institutes

While our generation uses MOOCs for furthering our education, MOOCs will become mainstream for future generations. MOOCs provide a wonderful counterweight to the natural curiosity of students while helping institutions extend their curricula into subjects they currently do not have the capacity to address. MOOCs will also become more social and collaborative, encouraging students to learn with each other and improve their overall performance.

Technology Augmentation

We will also see a rise in Virtual Reality (VR) and robots in the classroom. VR will help create more immersive learning experiences for students, thus stroking their natural curiosity to learn. Robots, on the other hand, will take the scud work from teachers – and provide inputs on skills assessments, personalized curriculum pathways, and attention tracking – allowing teachers to focus more on coaching and mentoring.

The future of education will define how our next generations shape up and succeed in the workplace. It is critical that we understand the value of developing the 3C’s – Creativity, Curiosity, and Collaboration – from an early age so that our next generation can achieve their full potential and value in the workforce.

The GDPR General Data Protection Regulation (GDPR), which goes into effect May 25, 2018, requires all companies that collect data on citizens in EU countries to provide a “reasonable” level of protection for personal data. The ramifications for non-compliance are significant, with fines of up to 4% of a firm’s global revenues.

This European Union’s sweeping new data privacy law, is triggering a lot of sleepless nights for CIOs grappling with how to effectively comply with the new regulations and help their organizations avoid potentially hefty penalties.

Will AI be the only answer to the highly regulated GDPR to come?

The bar for GDPR compliance is set high. The regulation broadly interprets what constitutes personal data, covering everything from basic identity information to web data such as IP addresses and cookies, along with more personal artifacts including biometric data, sexual orientation, and even political opinions. The new regulation mandates, among other things, that personal data be erased if deemed unnecessary. Maintaining compliance over such a broad data set is all the more challenging when it is distributed among on-premises data centers, cloud offerings, and business partner systems.

The complexity of the problem has made GDPR a top data protection priority. A PwC survey found that 77% of U.S. organizations plan to spend $1 million or more to meet GDPR requirements. An Ovum report found that two-thirds of U.S. companies believe they will have to modify their global business strategies to accommodate new data privacy laws, and over half are expecting to face fines for non-compliance with the pending GDPR legislation.

This begs the question: Can AI help organizations meet the GDPR’s compliance deadline and avoid penalties? After all, AI is all about handling and deriving insights from vast amounts of data, and GDPR demands that organizations comb through their databases for rafts of personal information that falls under GDPR’s purview. The answer is not only in the affirmative, but there are several significant instances where AI solutions to regulation compliance and governance are already on the high.

For example, Informatica is utilizing advances in artificial intelligence (AI) to help their organizations improve visibility and control over geographically dispersed data. It will provide companies with a holistic, intelligent, and automated approach to governance, for the challenges posed by GDPR.

AI interventions in Data Regulation Compliance and Governance

Data location Discovery and PII Management

It’s essential to learn the location of all customer data in all systems. The first action a company need to do is creating a risk assessment with a guess about what kind of data is likely to be requested how many requests might be expected. Locating all customer data and ensuring GDPR compliant management can be a daunting task, but there are options for automating those processes.

With AI, one can quite easily recognize concepts like ‘person names,’ which is important in this context. To find out how many documents you have that refer to persons (as opposed to companies), or to find out how many documents, social security numbers, phone numbers you have in any one repository, one can combine those analytics, and then begin to understand that the odds are that they have a lot of personal data in this repository, which provides a way to prioritize in the context of GDPR.

For example, M-Files uses Artificial Intelligence to streamline the process of locating and managing PII (personally identifiable information), which often resides in a host of different systems, network folders and other information silos, making it even more challenging for companies to control and protect it.

AI based data cataloguing

A solution that utilizes AI-based machine learning techniques to improve tracking and cataloging data across hybrid deployments can help companies do more accurate reporting while boosting overall efforts to achieve GDPR compliance. By automating the process of discovering and properly recording all types of data and data relationships, organizations can develop a comprehensive view of compliance-related data tucked away in non-traditional sources such as email, social media, and financial transactions – a near-impossible task using traditional solutions and manual processes.

Contextual Engines for Diversely Changing Data Environments

The GDPR changes how companies should look at storage of data. The risk of data getting compromised is increased based on how is stored, in how many different systems it’s stored, how many people are involved in that process, and how long it’s kept. Now that PII on job applications is regulated under GDPR, a company may want to routinely get rid of that data fairly quickly to avoid risk of data breach or audit. There are those kinds of procedural things that organizations will have to really think about.

There are instances where completely removing all data is impossible. You have to retain some data like billing records and there might be conflicting regulations, such as records retention laws. Now, if the citizen asks you to remove that, it’s going to add a lot of complexity to the process, in terms of understanding what data can be removed from the system and what cannot be removed. There will be conflicting situations where this regulation says something, and then you might have an Accounting Act or something in a local or state regulation that says something else.

This requires contextual engines built using AI that can be highly context aware based on the changing circumstances around the data and create a plan of how each data should be stored, managed and purged. This can also provide accurate insights on the levels of encryption and complex data storage techniques that need to be implemented for different data, thereby conserving hardware resources and increasing protection against malignant attacks and data breaches while minimizing risk of GDPR violations.

Working out the Kinks in AI led GDPR

GDPR aims to give EU citizens greater control over their personal data and to hold companies accountable on matters such as data use consent, data anonymization, breach notification, cross-border data transfer, and appointment of data protection officers. For example, organizations will have to honor individuals’ “right to be forgotten,” where applicable — fulfilling requests to delete information and providing proof that it was done. They must also obtain explicit, rather than implied, permission to gather data. And they are required to allow people to see their own data in a commonly readable format.

The system will undoubtedly work those issues out, but, in the meantime, companies should roll up their sleeves and take a thorough, systematic multi-step approach. The multi-step strategy should include:

Data. A comprehensive plan to document and categorize the personal data an organization has, where it came from, and who it is shared with.

Privacy notices. A review of privacy notices to align with new GDPR requirements.

Individuals’ rights. People have enhanced rights, such as the right to be forgotten, and new rights, such as data portability. This demands a check of procedures, processes, and data formats to ensure the new terms can be met.

Legal basis for processing personal data. Companies will need to document the legal basis for processing personal data, in privacy notices and other places.

Consent. Companies should review how they obtain and record consent, as they will be required to document it. Consent must be a positive indication; it cannot be inferred. An audit trail is necessary.

Children. There will be new safeguards for children’s data. Companies will need to establish systems to verify individuals’ ages and gather parental or guardian consent for data-processing activity.

Data breaches. New breach notification rules and new fines will affect many organizations, making it essential to understand how to detect, report, and investigate personal data breaches.

Privacy by design. A privacy by design and data minimization approach will become an express legal requirement. It’s important for organizations to plan how to meet the new terms.

Data protection officers. Organizations may need to designate a data protection officer and figure out who will take responsibility for compliance and how they will position the role.

Will GDPR Aligning Measures Be Necessarily Disruptive?

Many companies are going through significant changes as a result of the new regulations, and the efficiency and speed the AI-powered regulation compliance platform offer can significantly help streamline the entire process if companies want to ensure compliance.

Hence, there are plenty of challenges keeping CIOs up at night. By taking a more intelligence-driven approach to data discovery, preparation, management, and governance, the impending GDPR mandate doesn’t have to be one of them.

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.

This detailed primer will provide you crucial overview of the AI’s increasing prevalence amongst Indian industry, government and peripheral ecosystem and the significant impact AI will have in your organizations to remodel strategic and business models accordingly. The ensuing details also highlights the relative comparison amongst India, China and USA on the steady progress being done in AI adoption.

VC’s, PE funds and investors attempting to understand where to target investment, what offerings and capabilities would lead to better performance and gains, and how to capitalize on AI opportunities, it’s crucial for them to understand the International economic potential of AI for now and projections in the coming years.

Cutting across all these considerations is how to build responsible AI operating models and keep it transparent enough to maintain the confidence of customers and wider stakeholders.

International AI Capitalization Report – China & NA Leads, India hot in the heels

Without doubt, AI is going to be a big game changer in the international setting. A recent PwC report concludes that AI could contribute up to $15.7 trillion to the global economy in 2030, more than the current output of China and India combined. Of this, $6.6 trillion is likely to come from increased productivity and $9.1 trillion is likely to come from consumption-side effects.

Global GDP will be up to 14% higher in 2030 as a result of the accelerating development and take-up of AI

from the standpoint of direct economic impact of AI, China and North America will have greatest gains in GDP. Even though NA will reach its peak of AI led growth faster due to huge opportunities in parallel technologies implementations and advanced customer readiness for AI. NA is supposed to reach the peak of macroeconomic gains by 2020, following which there would be a relative slowdown in the growth.

Figure 1: Souce – PwC Analysis

China, on the other hand will have a slower but stable rise in GDP gains, even post mid 2020s because  a large portion of Chinese GDP comes from manufacturing, a sector which is highly susceptible to AI disruption in its operation, and also a higher rate of capital re-investment within Chinese economy compared to EU and NA. As productivity in China eventually catches up with North America, NA will focus more on importing AI-enabled products from China due to economically cheap alternative China provides. Hence by 2030, China will see much larger impact in its GDP.

Sector-wise AI Consumption Impact Index  –  The sector-wise impact of AI and its constituent offerings space will give crucial overview for investors to get a clear understanding of opportunities and threats in AI investment space. AI is set to be the key source of transformation, disruption and competitive advantage in today’s fast changing economy. Drawing on the findings of our AI Impact Index, we look at how quickly change is coming and where your business can expect the greatest return.

Figure 2: AI Consumption Impact (Src: PwC Analysis)

Deriving from the detailed PwC analysis report, which includes an AI impact index rating which gives an indication of enhancing quality and personalization for consumers and freeing up their time to invest their preferences in other endeavours.

The areas with the biggest potential representation will help your business target investment in the short to medium term. Some aspects could be even more disruptive or even revolutionary, such as robotic doctors, but they would be further off in the timeline of mainstream implementation.

Is the Differential for Developing countries like India too steep in catching up with AI?  –   AI is still at its early stages, which means that irrespective of the fact that the technology landscape is skewed towards the developed economies as compared to developing, the developing economies and their markets could still lead the developed markets from AI standpoint. This makes countries like India, the second largest economy with a strong focus in IT sector, a strong contender.

The economic impact of AI in GDP for developing countries, will be driven by:

1. Productivity gains from businesses automating processes (including use of robots and autonomous vehicles).
2. Productivity gains from businesses augmenting their existing labour force with AI technologies (assisted and augmented intelligence).
3. Increased consumer demand resulting from the availability of personalised and/or higher-quality AI-enhanced products and services.

The consumer revolution set off by AI opens the way for massive disruption as both established businesses and new entrants drive innovation and develop new business models. A key part of the impact of AI will come from its ability to make the most of parallel developments such as IoT connectivity.

India’s Macroeconomic Landscape of AI

India is already way ahead of many other countries in implementing artificial intelligence (AI). More than half of the companies are going beyond pilot and test projects and adopting the technology at a larger scale. This statistic is largely driven by American firms such as Accenture, Microsoft, the successful implementers’ toolkit, said. Last year, India was the second-largest global site for new centres, after the US.

Well over 58% of the companies that are using AI in India are working with the technology at significant scale

The Indian government’s Digital India initiative, too, has created a favourable regulatory environment for increased use of AI.

Recipe for AI Success in India – Digital & Data Bedrock

As India undergoes rapid digital transformation, data centres and the intelligence behind the data collected will enable the government and industry to make effective decisions based on algorithms. This means increasing opportunities for using (and investing over) AI in the country.

For example, Intel is betting on Artificial Intelligence (AI) to drive demand for its electronic chips, for which it is aiming to train 15,000 scientists, developers, engineers and students on AI in India over the next one year. The company will host 60 courses under its ‘AI Developer Education Program’. These will train people on ways they can adopt AI for better research, testing or even building of products. Intel is looking at India due to the country’s large base of technical talent. The country is the third largest global site for AI companies.

As India’s largest e-commerce marketplace Flipkart closes in on completing a decade in the business, it is looking to put in use its mammoth pile of data to predict sales of products months in advance. The company is working on an artificial intelligence (AI) solution that will give it an edge over rivals by helping it make smarter decisions in ordering, distribution and pricing products on its platform. Ultimately, the AI system will allow Flipkart to boost efficiency and reduce the cost of products for customers.

While rival Amazon, which has around a 10-year headstart over Flipkart, is known to have some of the most advanced sales prediction engines, the Indian company has the advantage of having a bigger data set of the country’s online consumer market.

AI Inroads in the Private Sector

AI has now a significant impact in the day to day lives of the regular mass of the country. Now that the Indian IT sector has reached a certain intermediary peak of digitization, the focus, now , is more on automating the repetitive problems and finding more optimized, efficient or refined methods of performing the same tasks, with less time duration and lesser manpower. The result is the standardization of some very critical app based services like virtual assistants, cab aggregators, shopping recommendations etc. This will eventually lead to AI solutions to real world problems.

The AI Startups Sphere of India- Startups are clearly playing a major role in innovating faster than corporates, which has led to several curious partnerships. SAP India has invested in Niki.ai, a bot that improves the ordering experience. Then there’s Ractrack.AI, where a bot improves customer engagement and provides insights; it functions as a virtual communications assistant to convert the customer into a client. Racetrack is helping companies turn leads into meaningful engagements by using AI. Another startup, LUCEP, converts all potential queries into leads with their AI engine.

The objective is to generate insights from data and simplify customer interaction with a business and also convert them into leads.

Indian startups saw $4 billion in risk capital being deployed across 1,040 angel and VC/PE deals between January and December 2016.

Disclosed funding announcements have shown a decreased value of 55 percent from the same period last year (2015) and a decrease of 20 percent from 2014. About $9 billion in VC/PE capital had been invested in 2015. The number of deals in 2016, however, has increased by 3 percent over the last year. On an average, four startup deals were announced every weekday throughout 2016. VCs predict that going forward machine learning and AI would be key themes to invest in.

AI in Public Sector– Ripe for  Digital Revamp and AI Adoption

A Blue Ocean for AI Investment due to Digital India Initiatives – Though both corporates and startups are making significant inroads in instituting AI in their service architecture and product offerings, and sometimes as part of their core business strategy itself, the challenges in the public sector in instituting AI can be quickly overcome due to huge Digital Movements instituted by the Indian Govt. like Digital India, Skill India and Make in India. This will create a solid bedrock of Data and Digital Footprint which will act as a foundational infrastructure to base AI implementation on, opening a huge blue ocean in public sector, rich for AI investment.

A New Workaround for Regulatory Challenges in Public Sector AI Implementation  – One of the peculiar problems the public sector faces in mainstream implementation of AI is the fact that since AI is a continuously self-learning system, capable of analytical or creative decision making and autonomous implementation of actions, who will then be accountable in taking responsibility for its actions, should they turn out to be not so favourable. This is because of the fact that since AI has a degree of autonomous decision making, it makes it difficult to pre-meditate its consequence. The AI systems are meant to augment and enrich the life of the consumers.  In such a situation, deciding liability of AI system’s actions will be difficult. Therefore, a lot of deliberation will be required to carefully come to a precise conclusion surrounding implementing these systems with ethical foundation and propriety.

Although many countries like US and some European countries are in the verge of implementing regulations and laws surrounding concepts like driverless vehicles, India still don’t have the regulations sanctioned. This, but need not be a bad news. India is cut to establish a completely revamped legal infrastructure, thereby completely circumventing the need for continuous regulatory intervention. Also, there is a favourable atmosphere in India as far as AI is concerned which will foster a spike in activities in that avenue.

Indian Governance Initiatives – Huge Scope for Investment of AI – As India emerges as a premier destination for AI, scope for investment opens in the governance aspect, in several ways. Governance schemes have a unique trait of the baggage of large volume and large scale implementation need, which can be tackled with Deep learning. For example, in Swachh Bharat Initiative, specifically construction of toilets in rural India, public servants are tasked with uploading images of these toilet constructions to a central server for assessment. Image recognition can be used to target unfinished toilets. It can also be used to identify whether the same official appears in multiple images or if photos were uploaded from a different location other than the intended place.

Other initiatives such as the Make in India, Digital India & Skill India can be augmented with AI to deal with scale. The range of application for AI techniques could range from crop insurance schemes, tax fraud detection, and detecting subsidy leakage and defence and security strategy.

An AI system can improve and enrich the agriculture of India by enhancing the bodies like The Department of Agriculture Cooperation and Farmers Welfare, Ministry of Agriculture runs the Kisan Call Centers across the country etc. It can help assist the call centre by linking various available information like soil reports from government agencies and link them to the environmental conditions. It will then provide advice on the optimal crop that can be sown in that land pocket.

As the need for large scale implementation and monitoring of governance initiative becomes more pronounced, the need for AI becomes absolute and it will open doors to considerable AI investment in the future of India.

Finally, Looking Ahead – A Collaborative Innovation Environment due to AI

AI innovations which fall under assisted, augmented and autonomous intelligence will help users understand and decide which level of intelligence is helpful and required in their context, thereby making AI Acceptance easier for the people. At the same time, this AI continuum can be used to understand economic ramifications, usage complexity and decision-making implications. While academia and the private sector conduct research on various AI problems with diverse implications in mind, the public sector with its various digital initiatives (Digital India, Make in India, etc.) can identify areas where parts of the AI continuum can be utilised to increase reach, effectiveness and efficiency, thereby giving direction to AI Innovative Research.

A collaborative innovation environment between academia and the private and public sectors will help provide holistic and proactive advisory delivery to the population, for eg. through public call centres, linking information from various government sources. At the same time, the rich data generated from these interactions can be used to draw deep conclusions. Collaboration between the three pillars could further help get a comprehensive view of problems and find intelligent and innovative ways to increase the efficiency and effectiveness of services delivered to society.

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.