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In today’s fast paced & volatile VUCA world, leaders face unprecedented challenges. They need to navigate through volatility while staying focused on strategy, business performance and culture.  Artificial Intelligence is fast becoming a game changing catalyst and a strategic differentiator and almost a panacea to solve large, complex and unresolved problems.  To be an AI powered organization, leaders not only need to have a broad understanding of AI strategy, they need to know how and where to use it.  AIQRATE advisory services and consulting offerings are designed to enable leaders and decision makers from Enterprises, GCCs, Cloud Providers, Technology players, Startups, SMBs, VC/PE firms, Public Institutions and Academic Institutions to become AI ready and reduce the risk associated with curating, deploying AI strategy and ensuing interventions and increase the predictability of a durable leader’s success.

In the age of the bionic enterprises, AI continues to dominate the technology & business landscape.  Under the aegis of transformation, disruption and innovation, AI has several applications and impact areas which usher a new change in how we make decisions in the enterprise and personal spheres.  Traditionally, human decisions are to a large extent based on intuition, gut and historical data.  In the age of AI, several of our decisions will be taken by algorithms.  Leveraging AI, the ability to mimic the human brain and the ensuing ability to sense, comprehend and act will significantly go up and will result in emergence of augmented intelligence in decision making. Enterprises, GCCs, SMBs, Startups and Government Institutions are attempting to harness the power of AI to change the way they do business.  All these industry segments are looking at AI becoming the secret sauce behind making them gain a competitive advantage.  If you have not started yet, you are already behind the competition, however large or pedigreed you might be.

So, where are you placed on your AI journey?  At AIQRATE, we can guide you on your journey of understanding what AI can do for you, embedding it within your business strategy, functional areas and augmenting the decision-making process.

At AIQRATE, we are here to help you with the art of the possible with AI.  Through our bespoke AI strategy frameworks, methodologies, toolkits, playbooks and assessments, we will bring seamless Transformation, Innovation and Disruption to your businesses.  Leveraging our proven repository of consulting templates and artifacts, we will curate your AI strategic approach roadmap. Our advisory offerings and consulting engagements are designed in alignment with your strategic growth, vision and competitive scenarios.

We are at an inflection point where AI will revolutionize the way we do business. The paradigms of customer, products, offerings, services and competition will change dramatically; and being AI-ready will become a true differentiator.  AIQRATE will be your strategic partner to help you to prepare for what’s next in order to stay relevant.

Enclosing here our journey walk through of 2020.

Artificial intelligence is fast making its way into mainstream education. I do not infer as part of the standard technical curriculum. But several schools, colleges, universities and other academic institutions are adopting AI in the process of delivering impactful education to students and their numbers are rapidly increasing.

Across the world, we are seeing AI augmentation in different facets of the education system – from automating routine tasks that teachers have to perform to crafting personalised education curriculum that is line with a student’s aptitude and areas of interest.

The education sector in India suffers from deep-rooted challenges that need wholesale solutions. The bulk of our students is compelled to go through archaic pedagogical methods that are employed to deliver static and outdated curricula.

For a while now, Bill Gates and other tech stalwarts have been excited by the idea of infusing AI into the education system. Bill Gates calls this bouquet of technology-driven, impactful delivery of coursework as ‘Artificially Intelligent Tutoring Systems’ and hopes that it leads to better internalisation of course content. This column shares some of the areas where AI can leave its mark on the education system and revolutionise the way the next generation of students learn.

Freeing up Teacher’s Time

Teachers are burdened with several menial, low-value tasks that are ripe for an AI augmentation. These tasks neither deliver better learning outcomes nor improve student experience. The time our teachers spend performing hygiene activities – from taking the attendance of the class, evaluating and grading tests and assignments and performing peer reviews – is enormously wasteful.

The time spent by teachers can be easily unlocked through AI, helping them focus on what they do best – teaching and coaching for success. Bringing in AI into the core way-of-working of schools today will help eliminate these burdensome tasks in the following ways:

• By curating tests for students automatically based on the aptitude of students in the classroom. Rather than relying on teachers to conjure up questions in the classroom, AI can help tutors assess the learning level of students and contextually bring up questions. Teachers will be able to administer tests much more easily by using a gradational question bank powered by AI

• Grading the administered tests and assignments. This is another time-consuming and often low-value task that can easily be taken up by AI administered-tests. AI can help automate the repetitive task of grading tests, thus helping teachers focus more on how they can create a better platform for learning by coaching and solving questions from students. AI-graded tests can also help bring up commonly occurring patterns of errors (ie, are students mainly making the same mistakes?), in effect providing input to teachers on which lesson plans require more impetus in the next class

• Ease out repetitive administrative tasks. Teachers also spend hours over the year submitting periodic reviews to their supervisors and coordinators, taking attendance and peer reviewing the efficacy of other teachers. This workload can also be supported by AI – by maintaining automated attendance logs, summarising the test scores of students and reporting the performance of teachers

Curricula, Content Planning

The present-day curricula delivery process is largely inefficient. The current paradigm requires a teacher to deliver pre-designed, standardised content to a classroom full of students with diverse aptitudes and interest levels. The negative impact of current pedagogical methods can still be manifested through the employability score of the current generation.

By leveraging the variegated applications powered by AI techniques, academia will not only be able to deliver more personalised curricula and lesson plans but also improve students’ understanding and retention of the coursework, leading to an improvement in educational outcomes. Here are a few examples of how we can enable those:

• AI can be instrumental in creating a culture of continuous improvement among teachers. By tracking their performance across different key metrics, the educational system will be able to uncover the areas where teachers need support and coaching more effectively. AI can also help curate the coursework for teacher improvement, thus making sure that teachers are continuously updated and can continuously refine their craft

• By infusing AI into the skills and aptitude assessment process for students, schools will be able to better judge the current level of understanding among students for a particular subject area as well as where their innate inclinations lie. Often, students are unclear or unsure about how they can make the most of their talents and how they can channel them into a trade. AI can help schools map out the data of previous students, their career achievements and tie that back to educational research. This will allow schools to accurately predict the subjects for which a student has a natural inclination and then coach her in that direction

• AI can also use data around student attention, interest combined with their aptitudes and abilities to recommend customised coursework. This will help students build a structured career path. This AI-centric approach will foster personalised training pathways and provide students with the skills needed to succeed in their future professions, rather than burdening them and staggering their confidence as the current system does

Optimising Classroom Experience

To fully unleash the creativity and expertise of teachers, the education system needs to also imbue AI-led applications in the classroom on a day-to-day basis. This will enable teachers to work at full throttle. Time spent on minding students and reorienting classroom methods to ensure better student engagement can be saved by using AI in the following ways:

• AI can help improve the tracking of students’ attention levels and help teachers intervene before a student loses interest in the classroom content. While teachers are conversant in minding students that actively disrupt the classroom, engaging students who are quietly inattentive is a comparatively difficult task. By employing attention tracking devices, teachers can much easily monitor the attentiveness of the class and mind them before they tune out

• By aggregating the attention scores of the classroom, AI can help teachers devise a more potent mix of teaching, testing and activities – to continuously ensure better class performance and engagement

AI can bring a plethora of benefits to the education system at large, providing improved educational outcomes to all stakeholders – students, teachers and parents. Through personalised curricula, improved efficiency in the time management for teachers and effective in-class monitoring and assistance, AI can shift the paradigm of how the education system works and how coursework is consumed and leveraged by the next generation of students.

The advent of Artificial Intelligence in the corporate world is disrupting existing business processes and changing the way organizations are run. AI is fast becoming a cornerstone of how businesses manage their bottom line, while opening new revenue streams that could provide a boost to their toplines as well. Given the scale of its impact, there is no doubt that AI will also have a severe impact on the science that governs how organizations are run today.

I am obviously referring to incumbent management theories and models that govern modern organizational management. In classic terms, management theories are frameworks of wisdom which guide the decisions made by organizational leaders that have survived phenomenally well over the period of the modern enterprise. Sure, there have been reasons to fine-tune each one to the realities of each era and industry, but the core construct has been omnipresent through the years.

With AI’s entry into the mainstream of business, management theories may need to be re-evaluated and tweaked appropriately. While the core construct remains powerfully relevant, an injection of the new-age reality of AI will help managers and business leaders apply them in a more contemporary manner on a few theories and models that are being redefined by AI.

 Porter’s Five Forces

The theory of the Five Competitive Forces put forth by Michael Porter in 1979 is one of the marquee and evergreen theories in management thought schools. Michael Porter suggests that organizations looking for an understanding of their competitor environment need to consider the impact from five perspectives and work on reducing the risks associated: 1) Threat of new entrants, 2) Threat of Substitutes, 3) Bargaining Power of Customers, 4) Bargaining Power of Suppliers and 5) Intra-industry Rivalry. The construct of this theory is that when businesses need to evaluate the competitiveness (or for that matter, the probability of success) in a business or an industry, they need to keep in consideration these five levers that determine an industry’s attractiveness.

With AI now entering the fray, it is time to reimagine our understanding of Porter’s theory. Specifically, when it comes to the threat of new entrants. Over the years, AI has levelled the playing field as a secret sauce, moving even the most established incumbents from their positions in traditional industries. One must look at how AI is fuelling Amazon’s massive growth – which has hugely disrupted the traditional retail industry. Amazon uses AI in a variety of ways – from identifying the next likely purchase to piloting drone-based deliveries. It was no surprise when Amazon’s announcement last year that it will be entering the healthcare industry led to a tumble in the share price of traditional healthcare companies. AI puts enterprises in a pole position and organizations that harness its’ power correctly stand to gain huge ground over those that do not.

Elton Mayo’s Human Relations Theory

Elton Mayo’s landmark research in the field of organizational productivity comes from his studies in the 1920s at Hawthorne plants in Chicago. In seeking to answer questions around how to improve human productivity, he and his assistants tried tinkering with multiple variables that might have an impact on the quality of the labour force’s work – such as light, duration of breaks and duration of working hours. After all these variables proved inconclusive on how to uplift worker productivity, Mayo finally hit upon his hypothesis i.e. giving attention to employees is what truly resulted in improved performances. Giving your workers a voice in the decision-making process, an experience of greater freedom and autonomy and considering the inherent social needs of people – is the most critical lever in the productivity puzzle.

Enter Artificial Intelligence. With AI taking away much of the scud work involved in managing the varied bureaucracies inherent in organizations, leaders will find a lot more time in managing the performance of its most valued asset – human talent. By simplifying routine and repetitive processes for leadership and the people, we can afford to pay much more attention to the well-being of our human talent, celebrate successes and course-correct flagging performances – with the much-needed human(e) touch.

Total Quality Management (TQM)

Many models and theories surround the overall framework for TQM (Total Quality Management) – a science that owes much of its early evolution to manufacturing techniques originating in Japan. At its very essence, TQM is the science that governs the quality in the manufacturing process. It relates to the adherence of manufactured products with agreed specifications, evolved keeping in mind the needs of the end user. TQM bridges multiple concepts – from customer centricity, lowering the waste in manufacturing processes with a view to increasing the overall quality of the manufacturing output.

The theories surrounding this domain may also be due for a revamp. TQM has long been a data-driven process – relying heavily on a post-mortem understanding of evidence-based decision-making and process improvement. With AI in the picture, organizations can improve predictions around off-specified products earlier, leading to a quantum leap in manufacturing quality. AI is also helping improve the forecasting process, thus reducing the waste created through unused, unsold inventory. Similarly, AI will reduce the overhead associated with identifying anomalous manufacturing conditions and provision for predictive machine maintenance as well to keep up the quality standards in manufacturing activity.

The Future of Organizational Management

The defining case for AI to changing existing models and theories of management boils down to the need for creating a blended workforce comprising both humans and machines. Management science today is largely rooted in building more efficient and agile organizations for humans. In the future, humans and AI will work side-by-side to achieve shared organizational goals. This means that AI will help remove a lot of administrative work that often throttles the productivity of leaders – and allow them to direct their energies towards more complex, judgement driven work that requires them to think creatively. Intelligent machines will soon be considered by the workforce to be ‘colleagues’ and the evolution of management thought needs to account for policies and systems that make the most out of this hybrid workforce.

In conclusion, infusing AI will make business more human centric.  Ironic as it may sound, putting AI in charge of the day-to-day, routinized activities will lead to more time for compassionate interactions between humans and unleash human creativity in a huge way. New management theories and models that emerge in the future will hence need to account for the impact of AI – and help organizations and their leaders understand how to navigate this new normal in business.

The traditional archetype of learning and employment – where students went to university and learned a skill that served them for the entirety of the careers – is rapidly evolving and changing. Today, we are in the age of continuous learning – where there is an inherent expectation on members of the employed workforce to constantly upgrade their knowledge and soft and hard skills. Earlier, executive education used to be a reserve of a privileged few high performers at large organizations, who showed great promise and rapidly rose through the ranks. Now, continuous learning across all the segments of the workforce is increasingly the new normal – almost to the point where it is the mandate for organizations that wish to grow and succeed in the business sphere. There is an expectation now that even the rank-and-file of the organization devote time to learning, unlearning & relearning and apply newfound ideas and techniques into their area of business.

What has been the driver behind this change? Why has upskilling and reskilling become the norm for the contemporary corporate career? A few important reasons underpin this change. The nature of the business today is extremely dynamic. Business environment and competitive landscape are changing faster than ever, with technology becoming the mainstay of the modern business. Tech-enabled startups are moving in and challenging traditional incumbents across industries. These changes are made a further complex with emergent ideas and changing paradigms of organizational management and leadership. In today’s fast-moving world of business, it is a critical priority for executives to keep their organizations nimble, proactive and armed with every arrow in their quiver, to ensure the continued success of their firms

Executive education is an important medium to achieve this goal and helps bridge the skill gap that is almost certain to rise when industries and organizations face structural headwinds. However, for executive education to live up to the promise and deliver value to employees and their organizations, we need to re-look at the programs itself. We need to ensure that the coursework and curriculum are topical, contextualized and relevant, whilst being personalized to the needs of the organization and its professionals. Here are a few perspectives on how executive education can be adapted for the industry4.0 era: 

Expand the Scope of Executive Education and the Courseware

As we dismantle the traditional paradigms of work and education, we also need to rewire our traditional understanding of what an executive education comprises. For years, corporations relied on top-tier management schools and universities to facilitate the essential leadership training for their workforce. In today’s world, rewiring an understanding of leadership is just not going to cut it. Executive education programs need to add more in terms of practical, on-the-job skills, that will help employees perform better and remain relevant to the needs of the business. 

There is now a strong case to expand the scope of executive education beyond traditional B-schools and include even MOOC-based education – which is provided by a plethora of websites today. Coursera, Udemy, LinkedIn Learning – to name a few – provide very tactical, hands-on understanding of essential, practical skills that the workforce can put to use right away, while also facilitating the career change aspirations that employees may have. Organizations need to seek out these MOOC-based providers to augment the executive education curriculum in a way that increases its scope and reach among employees.

These programs could very well help employees refresh their skill sets. For instance, such programs could help coders become well-rounded full-stack developers. Similarly, those with data engineering skills could be moved into areas such as high-performance analytics or artificial intelligence. Team leads could be educated formally in the tools and techniques associated with product management. For mapping current employee skills with the contemporary requirements of the business, MOOCs can be a critical intervention to incrementally upskill employees in their domain of work.

Incorporating the importance of shorter, tactical courses

Whilst there is no doubt about the value provided by a long-form one-year executive education program, companies also need to consider the benefits of short-term tactical coursework. Corporations need to augment their training programs with shorter, time-boxed courseware that can deliver instant impact for the organization. 

There are two reasons why this is important. Firstly, given the speed at which technology and business mature, it may not always make sense to put someone in a one-year program and wait for the delivery of associated results. In such circumstances, short form courses help deliver faster time-to-value – with employees able to deliver results in weeks, rather than months. Secondly, shorter-term courses also help reduce some of the inherent barriers people have towards learning. Shortening the learning cycle, putting it to use immediately and seeing real-life results, can help employees see instant benefits of the lifelong learning paradigm and break down mental barriers to learning.

Co-create multiple, personalized career pathways

The key word here is ‘personalized’. We need to move away from the old thinking of one-size-fits-all training to deliver more tailored, fit-for-purpose and relevant executive education to employees. To start, organizations need to develop skill maps and assessments – to identify where the workforce is today in terms of the required skill sets and where they are expected to be. Once this is performed, L&D teams can help create personalized learning journey-maps for their employees – based on the career interests and aspirations of employees. For instance, for some employees, it may make sense to provide a refresher and upskilling in their current areas of work and for others, it may make sense to reskill them in new areas of the business. Either way, developing a personalized training regimen for the executive education of the employees will deliver better results and help them excel in their field and improve the efficacy of learning programs too.

In the academic, industry forums and conferences, India is being positioned as a potential superpower in the field of AI on the world stage. It is our collective vision to see India as the premier destination of AI in the foreseeable future. There is seemingly a lot of work to be done if we are to overtake our formidable competitors and eventually the current leaders in the space of AI – the US and China. But no intervention is perhaps as urgent as the need to promote natively developed AI research and intellectual property within our academic institutions, universities and corporate enterprises. 

China spends 2.07% of its GDP on core research and development. In India, that number is a meagre 0.6%. Given this, it should not be surprising that China produced 4.5 times the number of citable research documents in the field of AI between 2010 and 2016. In terms of citations, the H-index of papers published in India (100) lags well behind China (195) and the US (413) – for the same period. At this juncture, it is evident that India’s contribution to the overall body of knowledge of Artificial Intelligence has been both quantitatively and qualitatively disappointing.

The contribution from Indian corporate enterprises has also been found to be wanting. For the period between 2001 and 2016, corporates contributed to merely 14.42% of the AI research done in India, with almost 70% of that being done by foreign multinationals doing business in India – the likes of Microsoft, Google,  IBM and others. 

There is a definite need for both academia and Indian industry to step up and contribute to India’s vibrancy in the domain of artificial intelligence. Here are 5 critical interventions needed urgently to take our research capabilities to the next level: 

Foster a collaborative approach to IP development

There is often a pattern of multiple research projects happening in silos – through collaboration between like-minded researchers from the same field of science. Artificial Intelligence on the other is an interdisciplinary subject. It essentially requires researchers from different walks of life – data engineers, machine learning scientists and those conversant with real-life challenges that AI can solve – to come together to solve common problems and add to the overall body of knowledge.

Universities need to quickly recognize that and build research capabilities in AI in an interdisciplinary, collaborative manner. The good thing is, universities by their very nature tend to host brilliant minds from multiple fields, but it is imperative that these minds be brought together for real, cutting-edge research to be published in the field of AI

Boost Funding for Research at Universities

Whilst India does not lag in the number of STEM graduates, our research capability is still inadequate. This is partly because of a lack of funding for research. For millennials, the allure of a corporate job or opting for entrepreneurship is far too high due to the compensation and benefits on offer.

It is critical that local and national governments intervene to help academic research in the field of AI be a viable career option for those who are interested in research. By expanding the budget allocated towards research and development, more researchers would be able to tap into public grants to expand the research in AI. 

Promote Corporate Funding and Empanelment

The onus of funding AI research cannot only fall on public institutions. Private Indian corporations would also be a huge beneficiary of locally developed AI competency. It is critical that we continue the work in building the bridge between academia and corporations to fund and promote AI research.

Several large companies, as well as startups, recognize the need for indigenously developed research in Artificial Intelligence. Corporate empanelment programs take multiple forms – from the low touch speaker arrangements to corporations helping setup topical centers of excellence at universities in a technology area that is key to their business success. More arrangements of the latter form are necessary, in addition to individual research grants and scholarships that corporations provide.

Release government datasets for AI algorithm development

Activating the development and learning for AI algorithms requires access to a lot of data. Here again, the government should step in to provide the relevant data sets to researchers working on complex, India-specific problems. 

In multiple circumstances, the government holds a treasure trove of data which can be hugely beneficial for the learning cycle of algorithms and promoting their development. The government should take a positive stand on sharing data sets, all the while keeping in mind data security measures and privacy rights of citizens.

Two-Pronged Approach – Core AI as well as Applications-Led IP Development

Research in AI needs to be supported at two levels. First, the development of patented core AI algorithms that will have broad, cross-industry applicability. The second is the development of intellectual property that is topical to industry or sector-specific problems.

India suffers from numerous topical problems that need an AI-led solution – from providing health care services to our burgeoning population, support for those engaged in the agricultural sector and provision of basic public infrastructure – roads, hospitals, schools and sanitation facilities. In addition to developing core mathematical capability, significant value can be unlocked through by developing expertise around these large, complex problems in AI, with solutions that can be applied to help countries facing similar problems.

Building core R&D capabilities and more IPs in Artificial Intelligence is key to cementing India’s position and competitiveness in this space. Building strong capabilities in AI is now a mandate for most of the world’s most powerful countries and it is imperative that India does not fall by the wayside. The good news is that NITI Aayog, MEITY, NASSCOM and others are already taking concrete steps to engage the stakeholder community – researchers, educational and corporate institutions – for AI research. The foundational aspects – an inclination towards STEM streams and an orientation towards key subject areas among the current students is there as well. It is time we harness their wisdom and knowledge to catapult India to the forefront in the field of AI. 

We are truly entering the Golden Age of Artificial Intelligence. With data and computational power making giant strides year on year, AI promises to unlock untold benefits for business and transform human life as we know it – a transformation that will play out in our professional and personal lives. Data Science and AI careers are in high demand as students and working professionals flock to courses in these subjects to ride the wave and build their careers. Enough has been written and consumed around the potential of AI and how corporations and universities need to enable their students to make the most of this emerging new technology.

Actively Seek Private and Public Funding for Research

Many countries globally do provide public funding programs for educational institutions. However, at the present level, this may be insufficient, and the exchequer may not be in a position to fill the massive capital gap required to improve research capabilities and labs.

To this end, it is critical that universities actively seek out ways to secure funding from public and private sector institutions. Several creative collaboration opportunities are surfacing to the instrument such partnerships. Corporations are always interested to seek inputs from the leading scientific minds to add to their portfolio of cutting-edge solutions and intellectual property. Some of the commonly seen engagement models include – securing research grants for topical research allied with a challenging business problem, setting up technology incubation labs to work on bleeding-edge technologies with exponential potential and sponsoring hiring hackathons to identify the best of talent. 

To stem the brain drain from academia to corporate, universities need to offer corporations a model where academicians can add value to corporations while staying inside the university and keep the pipeline brimming with young talent. Privately funded research from a corporate perspective could be a useful way to engage professors while keeping them available to be able to develop fresh professionals. Data Science and AI professors at institutions may not simply be interested in studying, but also generating research with wide applicability. Universities with a strong financial muscle and backing of public and private agencies would be able to support such aspirations of professors and help them continue to stay relevant in the subjects that are highly relevant to the workforce today.

Re-Educate Academicians in Data Science and Artificial Intelligence

While universities make strategic moves required to increase their muscle to improve research capabilities, they also need to consider training more of their faculty members to address classroom requirements of students wanting to study AI. Universities need to augment their training curriculum for faculty to infuse subjects that can help them take up AI as a subject for students.

For instance, technical institutions are typically rich in academics that impact computer science curriculum; additional subjects such as machine learning, deep learning, statistical methods and data engineering will help them become better-rounded professors, able to teach AI concepts to students. Similarly interested candidates from the pure science faculty – such as math and statistics – can be trained in computer science methods. Such cross-pollination of skills would help create a better talent pool available to serve a larger base of students.

Engage Industry for Academic Internship Programs

Finally, universities need to promote hands-on skills in artificial intelligence among academia by developing corporate internship programs. Through this intervention, university faculty will be able to broaden their understanding of real-life applications of AI – the application of topical AI solutions to solve relevant business problems.

At present, a small number of universities do provide their professors with opportunities to collaborate on industry-specific use cases. For faculty that gain exposure to such programs, it can be a truly transformational learning experience – and one that they can replicate in their classrooms for enabling better guidance for their students. Universities that boast of such industry connects become automatically more appealing to prospective students – as they enter the campus knowing that they will learn material that is truly relevant to the age that we live in, rather than having just a cursory, booking understanding of AI-related concepts.

Reorienting existing academia and bringing in a supply of talented young researchers in the field of Artificial Intelligence should be the top priority for universities today globally. With the high demand for this technology today and abundance of impactful use cases, it is critical that we keep the tap running and bringing in more researchers and academicians is a critical part of the solution that can help keep the AI revolution going. 

Since this is an Engineers’ day Special, we have used some quotes from different professionals 

“On this Engineers’ Day, we pledge to make engineers intelligent designers with ideas instead of making them screwdrivers,” says. Ravi Raj, Brand Head, Director, Sales & Support at NetRack 

With the advancement of technology, both the industry and the government is focusing and welcoming the fourth state of Industry revolution: Industry 4.0 which enables the wide range of digital concepts especially in ESDM Industry in multiple ways by making engineers and the technology leaders more flexible to adapt and meet the new demands of the market easily. On this special occasion of Engineers day, we at NetRack would like to congratulate all engineers across the globe for bringing the wave of innovation and solution leading to faster sustainable and profitable future of India. 

Every year, more than 20 lakh engineering graduates passed out from their colleges but without having their practical or skillful experience to contribute to the industry as a whole. And, in this dynamic industry, the scenario is witnessing more in a magnified way and which needs specialized and skills to cater its requirements. The only solution is emphasizing on their skills and offering them specialized training from the operational level to even the engineers’ level.   We have also come across, very few colleges/ engineering schools have not stressed this issue so far. 

On this special occasion, we as one the key Industry leader should take the pledge to not only focus to make them skillful but intelligent designers with new ideas. However, this, in turn, helps in fulfilling make & create (in)n India initiative with innovation.      

However, we are thankful to all the engineers for their highly valuable expertise and dedication and wish them all the very best for future endeavors!! 

“Emphasizing more on hands-on training to expose engineers’ to the real world to make them job ready”, says Adam Paclt, CEO, IceWarp on this Engineers’ Day

It is the fact that science and technology are the spine of any country to scale-up its growth development.  Similarly, for any country’ economy, investment in skilling and reskilling the engineers’ is the necessity to enhance their knowledge both technical and vocational skills along with transferable and digital skills to make them job ready  

we have to train our young and aspiring engineers who are committed to driving development by adopting the best practices of Industry 4.0 to transform the industry. For this, the major area where we at IceWarp believes that the Industry and academia have to jointly take a step forward in building and filling the Industry-academia gap by incorporating skills-based courses in their curriculum of engineering degree. 

On this Engineers’ Day, we pledge to help the young engineers to unleash their true potential and discover their true self by giving more emphasis on the principle of hands-on practical training exposing them to real-world situations and reasoning.    

Companies should also change their working culture by offering an apprenticeship programme which in turn will provide hands-on exposure to high-value engineering skills in an industrial environment.   Moreover, Industry ’s the mission must promote the cooperation, not competition by adopting the holistic approach to connect with a variety of personas and to become an agent of change.  

“We salutes the spirit of all Indian Engineers, whose innovations have contributed to the world’s Digital Transformation journey across industries,” says Mr Krishna Raj Sharma, Director & CEO at iValue InfoSolutions:

We at iValue have solution offerings which cater to the Digital transformation needs of the customers. It is important to skill the engineers and re-skill them time and again on the latest technologies so that they are abreast and capable of giving better and optimum solutions in order to address a customer’s DX journey. We firmly believe in enabling our women employees on technology and we began this exercise by hiring campus recruits and ensured they travel through the complete training cycle of solution sales journey and are ready for facing customers and partners addressing Industry Revolution 4.0. across multiple continents. There is a paradigm shift in the way the business is done in the IT fraternity. Hence, it is of prime importance that the channel community ensures there is a constant innovation in GTM and technology adaption as it will play a major role in creating a differentiator in the market. iValue salutes the spirit of all Indian Engineers, whose innovations have contributed to the world’s Digital Transformation journey across industries.”

Artificial intelligence is slowly, and steadily, making its way into mainstream education. And not simply as part of educational curricula. We are seeing increasing instances of schools, colleges and other academic institutions leveraging AI as a crucial part of the process in which they deliver education to their students. In the West, numerous examples abound of these educational institutions leaning heavily on AI – from delivering personalized educational curricula to automating routine tasks that classroom teachers have to routinely perform. 

Tech luminaries such as Bill Gates are enthused by the idea of Artificially Intelligent Tutoring Systems – which can ensure impactful delivery of course content and improved internalization of that content among students. The education sector in India, currently reeling from endemic problems – from static curricula to dated pedagogical methods – has much to gain through an AI-driven facelift. Let us look at some of the areas where AI can make its way into education and revolutionize the way the next generation of students learns.

Augment Planning of Curricula and Lesson Plans

The present-day paradigm of a teacher delivering pre-designed, standardized content to a classroom of students with diverse aptitudes and interest levels – is remarkably inefficient. We’ve seen the negative impact that the current pedagogical methods have had on the employability levels of the current generation. To this end, by leveraging the variegated applications of artificial intelligence techniques, academia would be able to deliver more personalized curricula and lesson plans, improve students’ understanding and retention of the coursework and in turn improve educational outcomes. Here are a few examples of how we could enable those:

By infusing AI into the skills assessment and aptitude assessment process for students, schools and universities will be able to better judge both – the current level of understanding among students for a specific subject area and where their innate inclinations lie. Often, students are unclear or unsure about where they see their career graph moving and what they would like to do in the future. Through AI, schools and universities can map out the data of previous students and their career achievements and tie that back to educational research. This way, schools, and universities may be able to accurately predict which subjects a student has a natural inclination towards and then coach them for a career in that direction.

Going in the same vein, AI can also use data around student attention, interest, aptitude, and ability to recommend customized coursework. This will help build the capability of students towards a specific career path and bring better value to the time of students. This AI-centric approach would help foster more personalized training pathways and enable students with the skills they need to succeed in their future professions, rather than burdening our students and staggering their confidence as done by the current system.

Furthermore, AI can also be instrumental in enabling continuous improvement for teachers. By tracking their performance across a variety of metrics, schools will be able to better uncover the areas where teachers need support and coaching. AI can also help curate the coursework for teacher improvement, thus making sure that teachers are continuously updated and continuously refine their craft

Automating Routine, Low-Value Tasks

Teachers today are overburdened by all manner of menial, low-value tasks that neither improve student experience nor deliver better learning outcomes. Enormous time is spent by our teachers worrying about and performing hygiene activities – from taking the attendance of the class, evaluating and grading tests and assignment and performing peer reviews. We can unlock this time spent by teachers and help them focus on what they do best – teaching and coaching for success. By incorporating AI into the core way-of-working of schools today, we can eliminate these burdensome tasks in the following ways:

By automatically curating tests for students based on the aptitude of students in the classroom. Rather than relying on teachers to conjure up questions in the classroom, AI could help understand the learning level of students and fire up the questions. By using a gradational question bank, teachers would be able to administer tests much more easily.

The other related time-consuming area for teachers tends to be grading the administered tests and assignments. These tasks can much easily be eliminated by using the AI administered tests. AI can help automate the repetitive task of grading tests, thus helping teachers focus more on coaching, solving questions from students and helping create a better platform for learning. AI-graded tests can also help surface patterns of errors (i.e. are students mainly making the same mistakes?), thus providing input to teachers on which areas of training require more impetus in the next class.

Among other several administrative tasks – teachers also spend hours over the year taking attendance, peer reviewing the efficacy of the other teachers and submitting periodic reviews to their supervisors and coordinators. This workload can also be supported by artificial intelligence – by maintaining automated attendance logs, summarizing the test scores of students and reporting the performance of teachers.

Optimizing the Classroom Experience

AI in education can go well beyond simply personalizing course content and unburdening teachers. To fully inform and unleash the creativity and expertise of teachers, we also need to imbue AI-led applications in the classroom on a day-to-day basis so that teachers can work at full-throttle. Time spent on minding students and reorienting classroom methods to ensure better student engagement can be saved by using AI in the following ways:

AI can help improve the tracking of students’ attention levels and help teachers intervene before students lose interest in the classroom content. While teachers are conversant in minding students that actively disrupt the classroom, engaging students who are quietly inattentive is a comparatively difficult task. Using attention trackers, teachers can much easily monitor the attentiveness of the class and mind them before they tune out.

Finally, by aggregating the attention scores of a particular classroom, AI can help teachers devise a more potent mix of teaching, testing, and activities – to continuously ensure better class performance and engagement

Using AI to augment classroom and educational institutions is of interest to everyone – students, teachers, and parents – and can help bolster educational outcomes. By personalizing the curriculum, optimizing the time of teachers and effective in-class monitoring and assistance, AI can be a game-changer in the way coursework is consumed and leveraged by the next generation of students. 

A combination of economic, business and technology factors have led to a steady rise in synergistic partnerships between industry and academia in India. Whilst a strong academia-industry partnership model has existed for several years in USA, UK, Canada, Singapore and few other countries.  India Inc. is catching up quickly to the transformative benefits that academia-industry engagements can bring to both parties. Rather than co-opt existing technology, corporates are under increasing pressure to incubate and deliver bleeding edge technology solutions to ensure continued competitive advantage and they are turning to some of the brightest minds in universities today for ideas on how to do that.

While there may be several drivers for corporates inking strategic, operational partnership modes with an academic institution; I see three common themes: First, academic institutions are under increasing pressure to deliver bleeding edge research that has commercial viability and real-world applications. They simply cannot ignore events in the business spheres anymore. For this, they rely on enterprises to provide contextualized understanding within which they can frame their problem statements and hypotheses. Second, we are also witnessing a muted public-sector funding for research, combined with the proliferation of private universities. As a result, academics need the patronage of corporates to fund their long-term research endeavors and goals. Lastly, R&D departments at organizations need the bright minds from academia to deliver results in a shorter time-frame and lower cost. These three critical drivers are spurring a healthy interest in developing academia-industry engagements. 

From AI, analytics perspective; research and innovation are the key differentiators. Let us dig deeper into the academia-industry engagements and dwell on building robust and synergistic engagement model and framework between academia-industry: 

Collaboration for Data Sets

This engagement is primarily intended for gaining access to data for running analysis and hypothesis building. Usually, an organization may need access to multiple varieties of data sets that are available with universities, to refine and improve their statistical models. These engagements can be often seen between enterprises and university hospitals – a hot-bed of structured and unstructured patient data. Healthcare-focused technology companies typically need access to tons of data to build and improve their AI systems – to capture every possible variation of the data and ensure that their model accounts for the best results.

An example of such a collaboration can be seen between Google DeepMind and University College London (UCL) for the use of AI in radiotherapy. The key to this partnership is UCL’s hospital and the availability of researchers in a real-world medical environment. 

Applied Research

The second area of collaboration is for industry and academia to create real-world applicability for research. Academics tend to be extremely visionary in their ability to add to a body of knowledge through thorough and intelligent research but may often lack visibility into challenges faced by businesses. By leveraging business context provided by corporates, they can add a flavor of high applicability to their research. Additionally, solving relevant, business-critical problems, researchers can also improve their visibility among their community, while potentially improving their H-Index scores through highly citable research.

An example of this collaboration is CA Technologies and IIIT Hyderabad engagement, they recently signed an agreement to set up a co-innovation lab. They intend to work together on topical problems in areas of Natural Language Processing, AI and Machine Learning, as per the company statement. For the researchers, this agreement would help improve their visibility through publications in scientific journals and CA Technologies can identify reference architectures and prototypes that will enable faster development timelines.

Co-Curriculum development and learning programs

This alliance between industry and academia is for cross-pollinating and co-creating AI, analytics academic curricula. Given the dynamic nature of business today, enterprises are collaborating with universities for providing continuous AI, analytics training to their employees across disciplines. This ensures that their employees have a contemporary understanding of the best practices in their field of work, while also promoting employee satisfaction. On the other hand, universities carry this understanding of the needs of the corporate sector and incorporate the same into their AI, analytics academic curriculum. For universities, this is a critical way in which they can create a comprehensive coursework that is exceeding relevant in the job market today.

Whilst, these are few prevalent areas of collaboration; other ones may look at mutually inking long-term strategic initiatives that involve academic institutions adding a cross-dimensional flavor to multiple analytics projects and requirements at organizations. The journey between academic – institutions collaboration has evolved and will witness several novel engagement models in the future. The continuous evolution of learning, unlearning and relearning phase will usher a new paradigm in academia-industry collaboration  

We are on the definitive cusp of the 4th Industrial Revolution. Earlier industrial revolutions ushered mechanization of previously manual tasks, leading to a huge shift in production output and increased operational efficiencies while creating a new range of skills for the workforce to master. According to Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, the transformation driven through this technological revolution will be unlike anything that humankind has experienced before and will require an integrated and comprehensive response involving all stakeholders of the global polity – from the public and private sector businesses, academia, and civil society.

Industry 4.0 – defined by breakthroughs in emerging technologies such as Robotics, Artificial Intelligence, Internet of Things, 3D Printing, Autonomous Vehicles and Quantum Computing – will yet again create a massive shift. It is increasingly common news that a manufacturing major is introducing robots on the production line. With smarter factories, smarter production and smarter supply chains, running autonomous production and delivery of manufactured goods, the question is bound to arise – what are the engineers supposed to do?

Engineering has long been a highly sought-after stream of education in India. Consider this – approximately 1.5mn students graduate out of 3,000+ AICTE-affiliated institutions in India, every year. [However, endemic problems surround their quality and technical output. Research after research confirms the disconnect between the education imparted to students, and the skills required on the job. According to the National Employability Report 2016, conducted by Aspiring Minds, 80% of engineers are considered unemployable. Even in India’s highly vaunted software industry, 95% of engineers are thought to be unfit to take up software jobs.

If the average Indian engineer is unfit to perform the tasks expected from him today, what hope is there for him to be able to perform the jobs of tomorrow? The 4th Industrial Revolution is only going to complicate matters by contracting the number of available jobs, while looking for specialized skills that Indian engineers most likely won’t have. One example, according to Talent Supply Index 2017 published by hiring startup Belong, there are only 8 data scientists for every 10 data scientist jobs in India.

It is undoubtedly a matter that needs urgent attention from educational institutes. The need for alignment between the skill-suppliers (colleges) and skill-consumers (businesses) is greater than ever before, and it is critical that educators stay in step with this new wave of industrialization, or risk falling by the wayside.

Embedding AI in engineering streams

Artificial Intelligence is the cornerstone of this new wave of industrialization. Embracing emerging technology areas will ensure that the engineering workforce is relevant for the jobs of the future and their knowledge needs to be embedded in traditional engineering syllabi. It is commonly assumed that AI only happens at the intersection of computer science and mathematics. While that is somewhat true at present, other streams too are looking at developing topical AI programs. Let’s look at these other engineering fields and how AI can be embedded into their existing coursework.
Civil or Construction Engineering is often considered to be far removed from AI disruption. However, AI is already making in-roads into this field. With geo-spatial intelligence and historical earthquake data, civil engineers can make better decisions on assessing the landscape available for projects, understand the materials required to withstand environmental conditions, or at times drop a risky project that might be too dangerous to develop. AI-driven predictive maintenance helps engineers optimally predict maintenance schedules for civil infrastructure developed – mitigating the risks posed by damaged infrastructure to civilians. AI can also help parse image data to detect damage to property, assess the extent of repairs required, and the costs of that repair work. Beyond these, AI can also help design smarter buildings – optimally utilizing electricity and water resources, while also bringing efficiencies to construction costs by automating inventory procurement decisions.

Another stream of engineering assumed to be immune from AI intervention is Chemical Engineering. Chemical Engineers with an understanding of AI can reduce the time for new chemical development, by modeling the impact of chemical combinations. AI can help predict and test the quality and resilience of new formulations. Chemical engineers with a knowledge of how to operationalize robotics technology for combining potentially dangerous chemicals – will again be an important intervention in this area of engineering.

Even across diverse engineering domains – metallurgy, oceanology and aerospace engineering, knowledge of artificial intelligence will be critical. Metallurgists with a knowledge of AI can run models to understand the properties of various metals and build stronger and more purpose-driven alloys. Oceanographers can leverage AI technology to parse geospatial information to better understand sea-beds and model the chemical and physical properties of oceans. In Aerospace Engineering, AI can bring untold efficiencies through robotics for assembling components. AI can predict failures and maintenance schedules required for aerospace equipment. In each of these domains, knowledge of AI, Robotics, Predictive Analytics, Computer Vision and Deep Learning will help ingest large volumes of unstructured disparate data, autonomously generating insights in a much lower time span – while improving the speed of the production process.

Finally, in certain streams within engineering – Mechanical, E&TC and Electrical – AI lends itself more naturally. Mechanical Engineers need to upskill themselves to develop and run autonomous robots that can do complex assembly and integration tasks. Education in Electronics Engineering needs to tend more in favor of developing Industrial IoT, Quantum Computers and advanced chipsets that can handle the large-scale processing required to run cross-platform AI applications. Electrical and Telecommunications engineers, given an education in Artificial Intelligence, can automate, monitor and improve the uptime and performance of their respective systems.

Leading the Way

A substantial chunk of upskilling needs of existing engineering graduates are handled by online courses. We are seeing an increased proliferation of AI, Machine Learning, cybersecurity, IoT, and Robotics courses delivered by online educational platforms: Coursera, Udemy, Udacity, UpGrad, while their programs are well serving the current crop of engineers, some of the other prominent academic institutions and academies  – ISB , Manipal global education , Jigsaw Academy , IFIM , Institute of Product leadership (IPL) ,UPES, Praxis Business School , Shiv Nadar engineering school, IITD are few listed ones that have taken vantage position in imparting AI , Analytics programs . A structured learning and innovative pedagogy approach are needed from traditional educational institutes for skilling new engineering graduates, to be able to master these new means of learning. They need to alter their curriculum to ensure that the next generation of engineers is equipped to handle the next generation of opportunities. However, it is very important that other institutions also follow suit and promote the cause of AI education.AI  will usher a   new beginning in the education arena and the ones that have the ability to learn, unlearn and relearn will succeed in the professional spheres.