This mixed view of AI is not surprising because the technology does more than automate tasks. The person whose role no longer includes a certain repetitive task automated by AI may not necessarily lose their job. “Rather, they may now have new responsibilities that more broadly focus on human capabilities that AI cannot deliver,” Rachel Russell, executive director of corporate strategy at Allegis, said.

Increasingly, as more research is carried out into the impact of artificial intelligence in the workplace, it is becoming clearer that AI’s impact on employment will have some jobs disappearing. However, in this (near) future the role of humans will be to direct AI rather than for humans to be replaced by it. “Of course some jobs will certainly be lost as AI takes on skills formerly attributed to humans, but new jobs will also emerge…In addition, there will likely be new needs beyond technical development, such as AI ethicists to manage the risks and liabilities associated with AI,” Russell added.

As AI takes on more of the work we do, continuous learning and a willingness to develop new skills will likely be a requirement for nearly every worker to maintain their job. AI, it seems will be instrumental in changing and optimizing the digital workplace.

1. AI’s Impact On Data Analysis
Maksym Podsolonko is CEO and founder of Slovenia-based eazyplan, which brings automation to event creation and management. He says that given the amount of data used in any business transaction now, AI is the only way to analyze everything that is available to decision makers. “AI is the only way to effectively analyze digital experiences on scale. Only Machine Learning (ML) is capable of sifting through all usage data and finding the patterns that human eye simply misses. At this stage majority of organizations are not using this opportunity, leaving analysis of data coming from different tools mainly manual or slightly automated with SQL,” he said.

He argues that current offerings are still too complicated and expensive for a wide use. As a result, within the coming years we will see a significant surge of easily-accessible tools automating data analytics with ML.

2. Improving Intelligent Marketing
As companies undertake significant digital transformation initiatives, it is also becoming clear that artificial intelligence (AI) is no longer just an interesting idea, according to Katrin Ribant, Chief Solutions Officer and co-founder of Datorama, a marketing intelligence company based in New York City. “AI-enabled solutions are already changing how work gets done in marketing, customer experience and customer service departments. These departments are already aware of how AI can meet their various needs and streamline certain processes,” she said.

According to Ribant, the future decision makers will look to the vendors that AI-driven specializations. “Moving forward, professionals in these fields will look to vendors who can provide specific AI-driven features on top of their core offerings”.

She also points to the fact that enterprises are using AI to build digital experiences as AI-powered tools become more adept at workplace tasks. Some chatbots used by customer experience and customer service departments are able to pass the Turing test, making them indistinguishable from a human.

Marketers are freeing up to 80 percent of their day spent number-crunching with AI-enabled marketing technology platforms. A recent study showed that 53 percent of marketers plan to adopt AI within two years, the highest percentage of all technologies they are evaluating. These changes signal the widespread adoption of AI in the enterprise that is on the horizon and will fully optimize digital experiences.

3. Securing The Digital Workplace
AI is also being used to secure the digital workplace. If there is a great deal of talkabout AI’s wide-ranging impact on digital experiences in every vertical and discipline, one of the most important contributions is in the way it is enabling a whole new generation of digital security experiences. “AI-enabled software and dedicated chipsets together with tiny and accurate 3D cameras, are now allowing for fast, secure and transparent recognition, authentication and access management capabilities,” George Brostoff, CEO of SensibleVision, said.

He explained that one of the reasons these new authentication solutions are so transformative is that AI allows for the rapid capture, analysis and manipulation of the tsunami of information captured in big data sets. Digital experiences tied to authentication and credential validation enabled by AI are much faster, more convenient and at the same time more secure.

AI-enabled digital experiences deliver this new authentication paradigm across broad set of enterprise applications and settings. “The newest generation of AI-enabled smartphones allows users to simply point a handheld device at their face to validate their identity. We are also seeing Enterprise-level applications in more public setting,” he said.

There are many more AI-enabled authentication applications coming into play, including the use in urban settings of AI-enabled 3D video cameras to monitor access to physical locations such as data centers, hospitals or transportation hubs. All of these innovative digital experiences tied to authentication are being made possible by the ongoing evolution and application of AI. And we are only at the very early stages of this transformation.

4. Enabling Digital Learning
Raphael Sweary is co-founder and president of San Francisco-based WalkMe, which brings predictive analytics to digital workplaces. Sweary says that using AI coupled with deep analytics, enterprises can predict user behavior to provide step-by-step guidance and engagement on how to use a system, allowing the individual to be an instant pro on how to use technologies they haven’t encountered before.

With global IT spending expected to reach upwards of $3.7 trillion this year, ensuring an optimal digital user experience, improved productivity and measurable return on investment is paramount. We are already seeing big business capitalize on AI — and we can expect that in the near future, AI will learn about the user so that we don’t need to learn how to use any software,” he said.

5. Recruiting Help From AI
For Jonathan Duarte, founder of one of the internet’s first job boards called for GoJobs.com, the recruiting process for most companies, from small businesses to global enterprises, is ripe for process automation. There are several manual steps in the process that are currently being automated using subsets of artificial intelligence; including natural language processing (NLP) and machine learning. The debate in the recruiting and HR circles is whether this is true AI or not, but it is coming and coming quickly.

Take, for example, setting up interviews — necessary but time consuming. Historically, these tasks have been handled by recruiting coordinators or recruiters, who juggle multiple phone calls and email exchanges in order to book rooms and schedules for multiple participants, and then get confirmations. “Process automation, using messaging, algorithms, and digital calendars are going to rapidly change the interview scheduling process, and therefore eliminate a significant amount of manual processes handled by recruiting coordinators. This technology has yet to be deployed to the mass market, but it is coming. So, predicting the loss of this labor requirement is clear,” he said.

6. Using AI to Prioritize Data
Agata Celmerowski, VP of Marketing at Klaviyo the email marketing automation platform for ecommerce said that the most important capability is the interaction between data and AI. She said that brands and other enterprises need to think about the data they’re collecting, how they’re storing it, and how they can use it to create a better experience for their customers. “Without that focus, they’ll be limited in what they can do with AI. It’s important to view data as a strategic business advantage, and use it to make personalized content,” she said.

She added that the marketing industry is at a tipping point. “There is simply too much noise. Using brute force to try and cut through the clutter will stop working altogether in 2018. The only path to success for an ecommerce businesses of any size will be in making every interaction with current and prospective customers incredibly relevant to the consumer,” she said.

2018 is the year where using accurate, rich, and meaningful data to enhance every customer touchpoint will stop being optional, she said. It will become the new status quo for every ecommerce brand. Merchants who can’t quickly and effectively use things like website behavior, purchase patterns, or engagement metrics to drive their marketing strategy will be left behind.

From there, the natural progression will be applying increasingly sophisticated techniques to analyze that data and determine the best ways to accelerate growth. There’s a lot of hype in martech space today around things like artificial intelligence, but so far it’s been a series of empty promises.

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Corporations have been relying on Learning Management Systems (LMS) to faciliate learning and address the challenges of distance, time and cost factors. LMS implemented in most organisations have been facilitating in hosting content, providing content access to the learners, conducting assessments and keeping track of their progress. The expectation of training managers in the ’90s was for technology to deliver static e-learning built around the digital libraries. LMS transitioned to becoming a platform for talent management in the next decade. Today the requirement is for continuous learnin. It is expected to be data driven and mobile friendly. With the arrival of Industry 4.0 and digital functioning, traditional LMS is no longer equipped to cope with the transformational business landscape and the emerging needs of the employees as an aid to their performance. In the new era, analytics will lead and drive every function in the organisation. Learning is also expected to be influenced by data, to become machine driven, more intelligent and develop the ability to anticipate learning needs.

Businesses are therefore evaluating the question of whether to retain the LMS and upgrade them or replace them with digital learning platforms. The survey findings of most organisations have been highlighting that the static content with the boundaries of learning pre fixed for all individuals which is typically the case with most LMS. Therefore need of the hour is ‘just in time content’ rather than just ‘in case content’ and alignment of learning outcomes with the business outcomes. Digital learning platforms differ from the traditional LMS fundamentally on account of their design around key principles namely, the importance of personalisation, the recognition of the individual’s identity in the context of highly networked environment and the necessity to support this persona and the significance of the overall learner experience which is the key to retaining the attention and the interest of the learners. Just as in the consumer buying process where personalisation and customisation are essential for successful business models, it is important to recognise that the learner experience is critical to sustaining interest in the learning process. For this, the learner profiles and their learning requirements have to be understood—the needs could be preparatory for a role, some would be just in time and some others could be remedial.

Apart from organisation-designed content, digital learning platforms such as Skills Alpha provide access to numerous external channels of learning and also nurture networked learning and user generated content. Courseera, YouTube, Udemy, to name a few, are frequented by all and by integrating these channels into the platform, it becomes feasible to appeal to varying learning patterns and styles of the employees and thus add a significant depth and dynamic edge to the content. Further, byte sized and video based content are preferred increasingly as learners have short span of time and this trend has to be addressed by the digital platform. Coaching and feedback from on the job training are other useful dimensions of learning thus providing a seamless experience for the employees. As a result, training managers need to focus on content curation instead of original content creation. Often we come across an incremental approach to ‘digital learning’. In other words, some managers would consider digital learning as a proposition that takes place with the access to intranet, social media, LMS and assessments and employees are able to move from one medium to another for their learning needs on their own, supplemented by offline initiatives such as performance management or competency matrix. This approach fails to deliver the optimal learning experience as seamless navigation and data capture at every stage, personalisation and collaboration with custom views for different stakeholders are not feasible with this approach.

One significant factor which is impacting most performers in organisations is the urgent necessity to embed analytics driven approaches to their functioning as the roles are becoming more complex. Digital learning platforms provide analytics support to business managers and L&D managers. Deep analytics could help teams develop personalised plans and monitor their progress. Investments in technology can be justified only if the correlation can be established between learning outcomes and business outcomes such as sales growth, employee engagement, customer satisfaction index, attrition rates and employee engagement score. Just as customer engagement parameter has become morescientific and data driven, it is also expected that the measurement of learning effectiveness becomes more accurate and reliable. Digital learning platforms with their built-in analytics and the capability to bring together multitudes of relevant content in a dynamic fashion could be the game changer for businesses working on the transformation agenda.

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In July 2016, Georgia Tech professor Ashok Goel hired Jill Watson, a teaching assistant for the spring semester. Jill was great at online student interactions, answering questions and was nominated as an outstanding assistant by students. The difference between Jill and other assistants? Jill is a chatbot. Her DNA is based on an open source platform (with pre-fed customized data) developed specifically to handle an enormous amount of students posts on the forum. And thus began an interesting new phase in one of the most respected sectors in the world – the education sector.

In India too, personalized digital learning platforms, new learning models delivering customized knowledge and flipped classrooms are finally dismantling the “one size fits all” approach towards student learning and education. Learning is becoming immersive (inside and outside the classroom); spurring creativity, critical thinking and meaningful real-world experiences.

Technological disruption is revitalising the education industry, enabling both students and teachers to strengthen their skills and knowledge to adapt to the new requirements of a 21st-century work force. These are exciting times to be an educator and a student.

But, the growth is disparate and still rudimentary. Understandably so for a country like ours with widespread geographical, cultural, gender and financial differences. Coupled with limited infrastructure, the dream of a truly literate India has a long time coming. There is a wide gap between expectations and reality both in terms of physical infrastructure and technological adoption.

While the government and academia work on improving the former, the latter has the power to really turn things around. What it needs in that endeavour is a solid data led backbone. Educational institutions today hold large amounts of data collected from online applications, classroom exercises/testing and student surveys. Social media too provides a rich source of information to capture student learning styles, preferences, concerns, reactions and perceptions.

This unstructured but valuable resource can be effectively analysed for insights to boost student achievement, increase faculty and staff productivity and improve operational effectiveness via better financial management and streamlined operations.

On a student level, the right utilisation of data sets will provide educators with real-time feedback on students’ performances, strengths and weaknesses. Data analytics can track student learning curves based on learning style and capability. Teaching techniques can accordingly be altered based on student pace and skill level, further augmented with technology innovations. If we can Dial in a Doctor, why not a teacher?

On a national level, a central repository can help make better decisions for the entire sector. The ball has already started rolling with the Digilockers and the National Repository of Open Educational Resources (high-quality digital content in local languages). The next step would be to make data accessible to institutions and academia alike – so that they can build customised techniques basis local and individual differences.

However, this is not a small undertaking. Challenges include low awareness, an absence of a data driven, insight – oriented culture, availability of uniform data sources and the apparent cost associated with data mining. Greater efforts are needed to adopt data management and infrastructure backbone for informed decisions, increased efficiency and greater accountability.

One of the foremost ways to address these issues is to increase collaborative efforts between educational institutions and data management enterprises. India must invest in a strong technology backbone, focused on hardware and software development across schools and higher education institutions. Educators need be trained to integrate technology in their pedagogy for effective learning.

But is the future of education solely dependent on technology? While data is set to move the educational landscape, educators are also working incessantly to build a sustainable and meaningful education system for all. The future will be about putting students first – with an innovative new curriculum focused on real-world needs, class design revamps for greater collaboration, rehashing educator roles and harnessing big data and artificial intelligence. The global conversation on innovative education is growing much louder and the urge to get rid of antiquated education systems has never been stronger and we should definitely not sit this one out.

There are tremendous growth opportunities for big data and analytics in the education sector. The Digital India drive can catalyse such initiatives by creating an enabling environment across the country.

Therefore, as we adapt to the needs of a 21st-century workforce, the sector needs a robust infrastructure background with measured approaches to manage life critical, business critical, real time, and mobile data. Backed up with investment, coherent strategies, and top-notch human talent, it’s now time to change the education sector with data.

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