Several pieces of research, studies and content have been published about the exponential growth witnessed in emerging technologies – AI, blockchain, RPA, cybersecurity, IoT, AR/VR. There is no doubt that these are the emerging technologies of the future, which will help catapult the next growth spurt of enterprises. Professionals across the information technology landscape are queuing up to upskill, reskill themselves across these mentioned areas to continue to be relevant in the workforce for tomorrow. But for those who already have learning, skills and experience in these emerging technologies, how can they take their career to the next level?

The flux of deployment-ready and value-generating use cases across industries suggest that a cross-technology expertise across these emerging technology areas would be the next big source of career growth for incumbent professionals. We witnessed few years ago, software developers were keen to reinvent themselves as full-stack developers. High performing technologists wanted to develop proficiency across the software architecture and the development life-cycle – from database to UI, and from infrastructure set-up to deployment. Similarly, IT professionals today should seek the synergistic benefits of combining areas across emerging technologies. This article will focus on what emerging technology areas are being effectively combined by enterprises.

AI + Blockchain

Artificial intelligence is the set of technologies that help machines mimic human functions. Blockchain is the emergent technology paradigm that helps build a distributed, immutable sequence of financial events and transactions. With a strong uptick in the dispersion of blockchain use cases, enterprises are also looking for a robust way to surface potential fraud and other anomalous events in real-time. The events of fraud attempt and security threats to blockchain systems are often very high-speed and require immediate attention and analysis to ensure that the perceived anomalies are rooted out. Using AI, specifically machine learning, we can rapidly parse through a log of events to find anomalous situations and flag them off in real-time, protecting the integrity of the blockchain.

AI +IOT

Internet of Things is the network of physical devices that exchange data. This very definition makes the case for combining artificial intelligence and IOT plainly clear. IOT-enabled sensors usually are a source of multitudinous data – based on the use case employed – which is increasingly being sent to the central controlling server in real-time, rather than in batches. Picking out key inferences from voluminous data, sent in real-time by numerous sensors is a task that is again handed over to AI systems – typically machine learning systems. While IOT systems can ably sense, transmit and store data, ML systems are required for making sense of the data and providing input on whether any action is required to be taken, and potentially even suggesting what best-case action could be taken.

IOT + Smart Cities

While the concept of a Smart City is a sub-segment of IOT, it’s the other way around. IOT forms only one of the component of powering a smart city. In addition to IOT, the Smart City stack would typically also include cloud (for running processes and storing data), Artificial Intelligence (for data analysis and learning) and an element of urban planning (for deciding the what and how of a Smart City design). By combining knowledge of IOT with these other ancillary areas can help IOT professionals become valuable and irreplaceable resources in this fast-growing technology area.

AI + Behavioral Sciences

This final combination may sound surprising, but one of the most valuable and high-impact grouping of skills might just be the combination of data science and behavioral science. While AI and data science can provide the what (‘What happened?’ And ‘What should we do now?’) of a business scenario, behavioral sciences inform the how part. Consider the example of Amazon, which has numerous examples of the coming together of behavioral sciences and AI. The recommendation engine uses artificial intelligence to answer the what part of ‘what are other people buying’. But, the idea that it will lead to continued stickiness on the website, serve as a wide showcase of SKUs available on it, while promoting product bundling and larger cart sizes is a clear behavioral sciences intervention and contributes massively to the success of AI applications.

If you are a professional already conversant with one of the emerging technology areas, combining that with another emerging area can be hugely beneficial. IT professionals today in these areas should strongly consider leveraging synergies across multiple technology areas – which can help them be better-rounded, high-value practitioners in an ever evolving areas of technology. 

For all its growth and development since independence, India faces many systemic problems. From our complex and labyrinthine legal system to the inefficiencies in our agricultural sector, large-scale problems still abound.

We need to better connect our burgeoning population with basic facilities. While Artificial Intelligence may not be the panacea in itself, we need to harness its potential to improve living conditions. Thankfully, we have the intellectual capital – our information technology peers – that can bring substantial dividend in this arena. By combining our inherent technological prowess and the keenness of our government in promoting technology-led interventions, AI can truly be a game-changer for India. Here’s an India-specific perspective on how AI can be a force for good for our country.

Agriculture Sector
Though the agricultural sector sees piecemeal improvements, numerous problems go unresolved – from low yield, low predictability of yield, poor access to institutional credit and financing to lack of transparency around pricing for produce. Using AI, agriculture can be transformed by:

• Provision of on-demand information on quality of seeds, fertilizers, pesticides and the track record of providers and opportunities for mechanisation through better equipment. This can be done through bot-enabled ‘Kisan Helplines’ that can provide guided advice for improving productivity

• Improving predictability of yield by ingesting data on soil health, equipment quality, farmer activity and weather conditions

• Improving visibility of market price trends for crops produced (domestic and international) so that they can make informed decisions on pricing, while exploring going to market without intermediary interference

• Leveraging data from productivity, yield and forecasts and potential prices to assess creditworthiness of individual farmers. This will speed up disbursement of finance and ensure farmers get better rates for crop insurance

Smart Cities
Indian cities have grown in an extremely unplanned manner, with public infrastructure and services struggling to catch up. Consider this – the cost of traffic congestion alone in just four major cities is estimated to be $22 billion annually. With AI, urban planners can:

• Track movement of traffic and people to identify opportunities for ‘decentralising’ major hubs and develop future-ready public infrastructure to facilitate smoother movement of people, vehicles and goods

• Model population density and availability of sanitation facilities to improve access. Additionally, by applying image analytics on drone surveilled images can help determine quality of sanitation facilities and accelerating their upkeep

• Identify and improve access to current and emergent residential and commercial hubs by creating more optimal public transport networks

• Align consumption of resources – energy, water, cooking gas – to actual needs

• Crowdsource, store and take action to improve infrastructure by directly soliciting participation from citizens
• Improve planning and forecasting for infrastructure development through better coordination between public works departments, leveraging traffic data and streamlining supply chains

Education System
The education system in India is among the most outdated and unequitable when compared with the developed world. Problems abound from a prominent level of student dropouts, to quality and methodology of teaching, lack of workforce readiness among students and outdated curricula. Here’s how AI can help improve certain facets:

• Track the demand for skills in the market and the educational infrastructure available to supply those skills through a National Skills Repository. This will help keep education concurrent with current market demands

• Automate routine, time-consuming tasks – from creating and grading test papers, developing personalised benchmarks for each student, identifying gaps in student development, tracking aptitude and attentiveness within each subject – and enabling teachers to focus on curriculum development, coaching and mentoring and improving behavioural and personality aspects of students

• Identify potential dropouts and root-causes, enabling educational institutions to take proactive steps to ensure student retention and course completion

Healthcare
The doctor-to-patient ratio in India is quite poor – with 0.62 doctors available per 1,000 people (WHO recommends a ratio of 1:1,000). When you add to that the inadequate spread of doctors across the country, we have a poorly served population, ranking 125th in the world for life expectancy. Using AI, we can:

• Identify areas with a high population density, which are underserved by public hospitals. Further, improve the deployment and availability of doctors, medical equipment and medication to better serve the population
• Track patient histories and clinical notes to prescribe evidence-based treatment

• Speed up routine processes such as scanning X-rays and CT-scans for malignancies using image analytics

• Improve public health studies by identifying early warning signals through alternative methods such as social media tracking

• Identify individuals without health insurance and incentivise their usage to improve patient medical adherence

Legal Challenges
When adjusted for VIP protection, India claims an extremely poor police-to-people ratio with 1 police for every 663 people. There are 27 million cases pending with courts, of which six million have been pending for over five years. AI can be a crucial enabler for our crumbling governance system and can help:

• Speed up review and summary writing of long drawn cases and their
history using natural language processing and voice recognition

• Use image analytics for surveillance and identification of wrong-doers in areas recognised for high criminal activity

• Surface fraudulent deals – especially among land deals – using anomaly detection frameworks to speed up delivery of justice

• Improve public services and transparency by routing RTI requests through intelligent bots, thus making it more efficient to get critical information

With a population of over 1.3 billion people, distributed across a huge landmass, public services urgently need technology-centric solutions that are both intelligent and scalable. AI will effectively address a number of these problems. To this end, it is critical that public services act sooner than later and ramp up their data sets, identify technology partners for ideation and apply AI techniques to power the India’s next leap forward.

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.

Introducing, the first of its kind, must have primer for CxOs, executives and professionals on executing AI and Analytics strategies in their enterprises for end-to-end transformative impact. Includes:Introducing, the first of its kind, must have primer for CxOs, executives and professionals on executing AI and Analytics strategies in their enterprises for end-to-end transformative impact. Includes:

• Exhaustive repertoire of AI and Analytics strategy roadmaps, frameworks and methodologies for CXO’s, coupled with broad exhibit plan of making the enterprises AI ready
• A comprehensive overview of the algorithm economy and its deep transformative potential of morphing enterprises into math houses
• Incisive study of C-suite stakeholders – CMO, CPO, CFO, CIO’s radical role and functional changes on strategic and operational sides underpinned by AI and Analytics infusion
• Outline of the immense AI and Analytics adoption and consumption scenarios in high impact industries of Banking, Insurance, Healthcare, Life Sciences, Retail and CPG
• Thought provoking facets of AI and Analytics pervasive interventions in exponential technologies: Chatbots , RPA , IoT , Cybersecurity , Blockchain , Cryptocurrency

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.