According to a 2019 report from the Centre for the Governance of AI at the University of Oxford, 82% of Americans believe that robots and AI should be carefully managed. Concerns cited ranged from how AI is used in surveillance and in spreading fake content online (known as deep fakes when they include doctored video images and audio generated with help from AI) to cyber attacks, infringements on data privacy, hiring bias, autonomous vehicles, and drones that don’t require a human controller.

What happens when injustices are propagated not by individuals or organizations but by a collection of machines? Lately, there’s been increased attention on the downsides of artificial intelligence and the harms it may produce in our society, from unequitable access to opportunities to the escalation of polarization in our communities. Not surprisingly, there’s been a corresponding rise in discussion around how to regulate AI.

AI has already shown itself very publicly to be capable of bad biases — which can lead to unfair decisions based on attributes that are protected by law. There can be bias in the data inputs, which can be poorly selected, outdated, or skewed in ways that embody our own historical societal prejudices. Most deployed AI systems do not yet embed methods to put data sets to a fairness test or otherwise compensate for problems in the raw material.

There also can be bias in the algorithms themselves and in what features they deem important (or not). For example, companies may vary their product prices based on information about shopping behaviors. If this information ends up being directly correlated to gender or race, then AI is making decisions that could result in a PR nightmare, not to mention legal trouble. As these AI systems scale in use, they amplify any unfairness in them. The decisions these systems output, and which people then comply with, can eventually propagate to the point that biases become global truth.

The unrest on bringing AI Ethics

Of course, individual companies are also weighing in on what kinds of ethical frameworks they will operate under. Microsoft president Brad Smith has written about the need for public regulation and corporate responsibility around facial recognition technology. Google established an AI ethics advisory council board. Earlier this year, Amazon started a collaboration with the National Science While we have yet to reach certain conclusions around tech regulations, the last three years have seen a sharp increase in forums and channels to discuss governance. In the U.S., the Obama administration issued a report in 2016 on preparing for the future of artificial intelligence after holding public workshops examining AI, law, and governance; AI technology, safety, and control; and even the social and economic impacts of AI. The Institute of Electrical and Electronics Engineers (IEEE), an engineering, computing, and technology professional organization that establishes standards for maximizing the reliability of products, put together a crowdsourced global treatise on ethics of autonomous and intelligent systems. In the academic world, the MIT Media Lab and Harvard University established a $27 million initiative on ethics and governance of AI, Stanford is amid a 100-year study of AI, and Carnegie Mellon University established a centre to explore AI ethics.

Corporations are forming their own consortiums to join the conversation. The Partnership on AI to Benefit People and Society was founded by a group of AI researchers representing six of the world’s largest technology companies: Apple, Amazon, DeepMind/Google, Facebook, IBM, and Microsoft. It was established to frame best practices for AI, including constructs for fair, transparent, and accountable AI. It now has more than 80 partner companies. Foundation to fund research to accelerate fairness in AI — although some immediately questioned the potential conflict of interest of having research funded by such a giant player in the field.

Are data regulations around the corner?

There is a need to develop a global perspective on AI ethics, Different societies around the world have very different perspectives on privacy and ethics. Within Europe, for example, U.K. citizens are willing to tolerate video camera monitoring on London’s central High Street, perhaps because of IRA bombings of the past, while Germans are much more privacy oriented, influenced by the former intrusions of East German Stasi spies ,  in China, the public is tolerant of AI-driven applications like facial recognition and social credit scores at least in part because social order is a key tenet of Confucian moral philosophy. Microsoft’s AI ethics research project involves ethnographic analysis of different cultures, gathered through close observation of behaviours, and advice from external academics such as Erin Meyer of INSEAD. Eventually, we could foresee that there will be a collection of policies about how to use AI and related technologies. Some have already emerged, from avoiding algorithmic bias to model transparency to specific applications like predictive policing.

The longer take is that although AI standards are not top of the line sought after work, they are critical for making AI not only more useful but also safe for consumer use. Given that AI is still young but quickly being embedded into every application that impacts our lives, we could envisage  an array of AI ethics guidelines by several countries for AI that are expected to lead to mid- and long-term policy recommendations on AI-related challenges and opportunities.

 Chief AI ethical officer on the cards?

As businesses pour resources into designing the next generation of tools and products powered by AI, people are not inclined to assume that these companies will automatically step up to the ethical and legal responsibilities if these systems go awry.

The time when enterprises could simply ask the world to trust artificial intelligence and AI-powered products is long gone. Trust around AI requires fairness, transparency, and accountability. But even AI researchers can’t agree on a single definition of fairness: There’s always a question of who is in the affected groups and what metrics should be used to evaluate, for instance, the impact of bias within the algorithms.

Since organizations have not figured out how to stem the tide of “bad” AI, their next best step is to be a contributor to the conversation. Denying that bad AI exists or fleeing from the discussion isn’t going to make the problem go away. Identifying CXOs who are willing to join in on the dialogue and finding individuals willing to help establish standards are the actions that organizations should be thinking about today. There comes the aspect of Chief AI ethical officer to evangelize, educate, ensure that enterprises are made aware of AI ethics and are bought into it.

When done correctly, AI can offer immeasurable good. It can provide educational interventions to maximize learning in underserved communities, improve health care based on its access to our personal data, and help people do their jobs better and more efficiently. Now is not the time to hinder progress. Instead, it’s the time for enterprises to make a concerted effort to ensure that the design and deployment of AI are fair, transparent, and accountable for all stakeholders — and to be a part of shaping the coming standards and regulations that will make AI work for all

While some predict mass unemployment or all-out war between humans and artificial intelligence, others foresee a less bleak future. A future looks promising, in which humans and intelligent systems are inseparable, bound together in a continual exchange of information and goals, a “symbiotic autonomy.” If you may. It will be hard to distinguish human agency from automated assistance — but neither people nor software will be much use without the other.

Mutual Co-existence – A Symbiotic Autonomy

In the future, I believe that there will be a co-existence between humans and artificial intelligence systems that will be hopefully of service to humanity. These AI systems will involve software systems that handle the digital world, and also systems that move around in physical space, like drones, and robots, and autonomous cars, and also systems that process the physical space, like the Internet of Things.

I don’t think at AI will become an existential threat to humanity. Not that it’s impossible, but we would have to be very stupid to let that happen. Others have claimed that we would have to be very smart to prevent that from happening, but I don’t think it’s true.

If we are smart enough to build machine with super-human intelligence, chances are we will not be stupid enough to give them infinite power to destroy humanity. Also, there is a complete fallacy due to the fact that our only exposure to intelligence is through other humans. There are absolutely no reason that intelligent machines will even want to dominate the world and/or threaten humanity. The will to dominate is a very human one (and only for certain humans).

Even in humans, intelligence is not correlated with a desire for power. In fact, current events tell us that the thirst for power can be excessive (and somewhat successful) in people with limited intelligence.

You will have more intelligent systems in the physical world, too — not just on your cell phone or computer, but physically present around us, processing and sensing information about the physical world and helping us with decisions that include knowing a lot about features of the physical world. As time goes by, we’ll also see these AI systems having an impact on broader problems in society: managing traffic in a big city, for instance; making complex predictions about the climate; supporting humans in the big decisions they have to make.

Intelligence of Accountability

A lot of companies are working hard on making machines to be able to explain themselves — to be accountable for the decisions they make, to be transparent. A lot of the research we do is letting humans or users query the system. When Cobot, my robot, arrives to my office slightly late, a person can ask , “Why are you late?” or “Which route did you take?”

So they are working on the ability for these AI systems to explain themselves, while they learn, while they improve, in order to provide explanations with different levels of detail. People want to interact with these robots in ways that make us humans eventually trust AI systems more. You would like to be able to say, “Why are you saying that?” or “Why are you recommending this?” Providing that explanation is a lot of the research that is being done, and I believe robots being able to do that will lead to better understanding and trust in these AI systems. Eventually, through these interactions, humans are also going to be able to correct the AI systems. So they are trying to incorporate these corrections and have the systems learn from instruction. I think that’s a big part of our ability to coexist with these AI systems.

The Worst Case Contingency

A lot of the bad things humans do to each other are very specific to human nature. Behavior like becoming violent when we feel threatened, being jealous, wanting exclusive access to resources, preferring our next of kin to strangers, etc were built into us by evolution for the survival of the species. Intelligent machines will not have these basic behavior unless we explicitly build these behaviors into them. Why would we?

Also, if someone deliberately builds a dangerous and generally-intelligent AI, other will be able to build a second, narrower AI whose only purpose will be to destroy the first one. If both AIs have access to the same amount of computing resources, the second one will win, just like a tiger a shark or a virus can kill a human of superior intelligence.

In October 2014, Musk ignited a global discussion on the perils of artificial intelligence. Humans might be doomed if we make machines that are smarter than us, Musk warned. He called artificial intelligence our greatest existential threat.

Musk explained that his attempt to sound the alarm on artificial intelligence didn’t have an impact, so he decided to try to develop artificial intelligence in a way that will have a positive affect on humanity

Brain-machine interfaces could overhaul what it means to be human and how we live. Today, technology is implanted in brains in very limited cases, such as to treat Parkinson’s Disease. Musk wants to go farther, creating a robust plug-in for our brains that every human could use. The brain plug-in would connect to the cloud, allowing anyone with a device to immediately share thoughts.

Humans could communicate without having to talk, call, email or text. Colleagues scattered throughout the globe could brainstorm via a mindmeld. Learning would be instantaneous. Entertainment would be any experience we desired. Ideas and experiences could be shared from brain to brain.

We would be living in virtual reality, without having to wear cumbersome goggles. You could re-live a friend’s trip to Antarctica — hearing the sound of penguins, feeling the cold ice — all while your body sits on your couch.

Final Word – Is AI Uncertainty really about AI ?

I think that the research that is being done on autonomous systems — autonomous cars, autonomous robots — it’s a call to humanity to be responsible. In some sense, it has nothing to do with the AI. The technology will be developed. It was invented by us — by humans. It didn’t come from the sky. It’s our own discovery. It’s the human mind that conceived such technology, and it’s up to the human mind also to make good use of it.

I’m optimistic because I really think that humanity is aware that they need to handle this technology carefully. It’s a question of being responsible, just like being responsible with any other technology every conceived, including the potentially devastating ones like nuclear armaments. But the best thing to do is invest in education. Leave the robots alone. The robots will keep getting better, but focus on education, people knowing each other, caring for each other. Caring for the advancement of society. Caring for the advancement of Earth, of nature, improving science. There are so many things we can get involved in as humankind that could make good use of this technology we’re developing

Global Capability Centers(GCC’s) are at a pivotal turning point as the pace at which digitization is changing every aspect is fast paced and agile. The rapid transformation and innovation of GCC’s today is driven by new age or exponential technologies :AI, internet of things (IoT), blockchain, cloud computing , RPA, Cyber security. Exponential technologies are seen to double their performance every couple of years while reducing their costs in half. In recent times; GCC story is in a changing era of value and transformative arbitrage. Most of the GCCs are aiming towards deploying suite of exponential technologies :RPA, Blockchain, IoT, AI to get into digital play. It is widely predicted that exponential technologies will disrupt and transform capability centers in the coming decades.

This blog aims to demystify emerging exponential technologies and examine the developing role that it could play in both the immediate and long-term future of GCC’s. From applying AI to exploring how blockchain could be used to transform businesses, we will envision ways to apply and adopt exponential technologies to GCC related challenges.

Cloud Based Digital Transformation

Big Data technology and cloud computing are widespread across the globe GCC’s are finding the right way to use it, so they can accomplish their business goals. As automation drives businesses, insights derived from big data analytics are like a data mine for businesses to make data-driven decisions. The onset of big data and cloud has led changing job roles and responsibilities in GCC such as BI/BD engineers, Cloud Architects, BI/BD Solutions Architects, Data Visualization Developer.

Automation, RPA for GCC’s

GCC’s today are rapidly adopting robotic process automation. The aim for the workforce is to focus more on value added tasks. Automation value can be leveraged when Cognitive strikes convergence with RPA and enable autonomous decision making, understanding natural language, self-learning and ability to handle scenarios that entail unstructured data and complex decision making.

Automation is seen as the current and huge opportunity in GCC’s. It has a huge potential in its ability to capture the rule-based market. Robotic Process Automation are delivered as virtual Robots, tools, or a set of scripts, an error free enabled automated process. Some of the emerging roles in this area include RPA developer, Deployment engineer

Blockchain

Increased collaboration between businesses, GCC’s and tech vendors unlock the power of blockchain across multiple use cases. Given its immutable and decentralized nature, blockchain will be invaluable in sectors such as manufacturing, supply chain and financial services and we will see innovative use cases coming out of these domains

Within blockchain, smart contracts specifically will gain immense traction. The business value of smart contracts is remarkably clear – they drastically reduce the time and effort for routine but lengthy paperwork processes, while maintaining the sanctity through a blockchain network.

Blockchain development is reshaping the GCC environment with  emerging distributed ledger technology. This requires niche skill sets and roles such as Blockchain developers/engineers, Blockchain legal consultant

Artificial Intelligence Predominance

AI’s ability to enhance decision making, reinvent business models and ecosystems, and remake the customer experience will drive the payoff for digital initiatives through 2025. The AI foundation consists of numerous technologies and techniques that have grown over many years: recommendation systems, decision trees, linear regression and neural networks impacting the next-gen GCC’s.

Following core trends in AI will dominate across GCC;s:

Adoption of “plug and play”, as-a-service solutions in AI for organizations with less than global-scale resources to think about integrating narrow AI.

Enterprise Conversational AI will see mainstream adoption and will look to add voice enabled interfaces to their existing point-and-click dashboards and systems.

AI and machine learning continue to be the most penetrable technology trends within GCC’s. The capability centers are adopting software tools that are enabled with machine learning and AI capabilities to eliminate manual intervention. the emerging job titles and roles evolve as Data scientists, Statisticians.

Internet of Things (IoT)

As capability centers are becoming more digital to deliver a connected and seamless experience, IoT will trend among the latest technologies. The emergence of this technologies give rise to newer job roles such as IoT Managers, IoT Business Designers, full stack developers etc. The functional and technical areas of these roles span across the expertise of applying sensors, embedded devices, software and other electronics to businesses with front-end and back-end technologies.

The rise of exponential technologies and the need to stay upbeat with it, allows scope for the changing landscape of GCC’s through new opportunities and roles. Technologies :cloud computing, cyber security , AI , blockchain, robotics process automation (RPA) will continue to be in the fore front of this changing landscape. The GCC’s will continue to directly boost the need for skills on the exponential technologies front . Time for GCC heads and talent acquisition leaders to revamp their business and talent strategies .

The age of Artificial Intelligence is upon us. Businesses and society are now looking towards AI for transformative outcomes. Businesses specifically are investing huge amounts of money on AI technology that will not only bring in efficiencies across multiple processes, but also unlock new revenue streams that will deliver quantum bottom-line impact. With the AI transformation playing out rapidly in our personal and professional lives, we need to deeply understand what the future of work will look like in the age of AI.

Within the business organization, there is a huge need to ramp up skill development interventions. The traditional roles of employees in an organization are rapidly changing as they are expected to stay in step with the developments in the world of AI. Business executives are now needed to deeply understand the potential of Artificial Intelligence and translate it into a viable roadmap for their business. Technology leaders need to take centre-stage in how their organizations adopt and harness the power of AI. The CIO is now fast becoming the key custodian of the most valuable resource in business today i.e. data. We are seeing a fast proliferation of digital evangelists and transformation officers who are charged with developing a framework within which the future of the organization will operate.

Ushering the Future of Work

On a tactical level, the burning question now is how subjects such as Data Science, Artificial Intelligence and Machine Learning can be infused in the career pathways of existing employees. How can organizations can build a steady pipeline of future talents with expertise in AI? Mastery of exponential technologies (AI, cloud computing, blockchain, IOT, cybersecurity etc.) will be remarkably important for both business and technical professionals. It is critical that transformation leaders and digital evangelists are well-versed in building internal capabilities that converge around the nexus of technology competencies, managing a hybrid workforce and ensuring the adoption and dispersion of AI.

For us to usher in the future of work powered by Artificial Intelligence, we need to ensure that a few key enablers come together. We need to expand the scope of executive education and the courseware that goes with it. Next, we need to seriously consider the potential impact of shorter, tactical courses. Corporations need to augment their training programs with shorter, time-boxed courseware that can deliver instant impact for the organization. Finally, we need to reimagine multiple, personalized career pathways. We need to move away from the traditional one-size-fits-all training and deliver more tailored, fit-for-purpose and relevant education to employees. Here are the three critical interventions for the business and technology leaders to execute in order to usher in the future of work that is enabled by AI.

1.Develop New Age Skills and Competencies in AI Technology

Upgrading the technology competencies and skills of business and technology leaders and their teams seems like the most critical first step. With the landscape of technology is rapidly evolving, we need to urgently upskill the present and future workforce to ensure a quality supply of talent. We need new age coursework in computer science that can hugely develop the ability of students in subjects such as Artificial Intelligence Machine Learning, Deep Learning, Natural Language Processing and other AI related concepts. On a broader scale, we also need Universities and colleges to improve the existing knowledge-base of AI enabling technologies such as Cloud, DevOps, Blockchain etc as well for the workforce.

At present we see a decent level of advancement in the field of computer science training and education. However, other trades within the technical area which also require to be upgraded as well. By doing so, we will be able to ensure a wholesome and future-proof education for the aspirants who wish to build their careers in the world of AI. For instance, students studying for a major in the field of electronics could shape their focus on mastering AI-enabling technologies such as GPUs and Quantum Computing. The students presently pursuing a specialization in mechanical engineering could achieve some level of sophistication in allied subjects of robotics and 3D Printing. Subject matter experts in the fields of industrial engineering, operations and supply chain would also do well to extend their skill sets to machine learning and blockchain as well thus creating a convergence of their interest areas and realities of the market – which will empower them with the required tools to succeed in the workplace of the future.

2. Reimagining the Process of Developing of New Age Technology

This interventions pertains to the embedding the design in the process of development and user adoption of AI technology. A commonly held misconception around design of a product or software is that it is restricted to simply the look or feel of the product or software. This is simply not true. As a Steve Jobs once proclaimed – Design is not just what is looks like and feels like. Design is how it works.

For the growth of AI to live up to the hype, we need to reimagine the process by which we develop new age technology. We need to build design into the fabric of the development and engagement process to ensure that the conceived idea is brought to fruition. Transformation evangelists aiming to spearhead the future of work should treat design as the creative process that aids the development of breakthrough products.

We are already seeing several inroads that design frameworks such as Human Centered Design and Empathy-led Design are making in the technology realm. These frameworks not only guide the development process, but also the user experience of the final software / hardware being developed. These frameworks do so by putting the user at the center of the journey.

3.Managing the ‘People’ of the Future Workforce

 As I mentioned before the understanding of traditional roles in the future of work is rapidly changing. New roles are also emerging where data custodians and algorithm at scale engineers are put to work to develop the technology that powers the business of the future. On the macro level, we are seeing rapid changes in the paradigm of staffing as well. With the gig economy in full force, we are seeing more dynamic team compositions – where individuals with varied skill sets are required to continuously augment teams on a need basis. Advances in the fields of technology and management typically ordain large-scale transformation in the manner in which organizations manage their workforce.

On the micro level we are seeing that increased instances of automation are requiring managers to build and scale blended teams comprising humans and AI. This disruption requires a paradigm shift how the future workforce is managed. Teams in the future will showcase increased diversity and will be more interdisciplinary than ever before. Managing teams, careers and coaching for improved performance in the future will require a new set of metrics. Change evangelists need to devise these metrics – which will be imperative to how the workforce of the future is managed.

New technologies will require new approaches to project management and staffing. To ensure the supply of these critical skills, we also need courses that provide an education of subjects such as people management.

Our very understanding of our workplace is being rapidly disrupted. Increasingly a convergence of the right people, process and technology is required to unearth insights from a seemingly exponentially increasing size of data. To turn this data into actionable intelligence that powers business processes must be the focus of business and technology leaders – as well as educationists that build the talent pipeline for the future. Academia is required to urgently intervene and provide theoretical and practical training in AI subjects to both the existing workforce and the future pipeline of talent. We also need a dispersion of soft skills that will enable and evangelize this change. With growing interest and appreciation of technologies and platforms around Artificial Intelligence and the Digital Workplace, organizations need to ask tough questions of themselves. The time is now to consider the various forces at play. With increased AI augmentation and the transformation of processes and people that enable it, the topic of the Future of Work requires immediate and urgent attention.

The Internet of Things(IoT) has profusely pertinent applications. The effectiveness can be realized through operation and integration of the IoT across applications from domestic use to large scale industrial usage.

In this blog write-up, I would like us to deeply examine dynamics of IoT for SME’s to discover a range of applications and advantages to potentially become a driving force by looking at some of the critical unsolved problems. IoT potential is looked through multiple lenses in this sector, particularly its implications and application across SME landscape.

It is imperative to understand that analytics and IoT are two sides of the same coin. Basically, the information gathered from sensors requires analytics applications. The duo combo will influence to make informed decisions based on the data and behaviors collected.

From this perspective, application and its use have been extended for large industries and organizations. One of the potential benefits that IoT offers is in saving of costs related to process automation and rebound in customer satisfaction.

Similar benefits of IoT can be translated and enabled for SMEs in a relevant scale. SME’s have started embracing IoT and its related technologies. Here we would try to demystify IoT for SMEs such as retailers and companies with more moderate capitals by examining the developing role that it could play in both the immediate and long-term future. Smart devices and sensors are vital for this Machine-to-Machine (M2M) link. By applying machine learning to exploring how IoT could be used to transform businesses, we will envision ways to apply and adopt to SME related challenges

IoT in Retail

Retail across business are a strategic fit for IoT characteristics and intelligent sensors that can measure them. Some areas gaining pace in the industry include Automated Checkouts, Personalized Discounts, Beacons, Smart Shelves, In-store Layout Optimization and Optimizing Supply Chain Management

Sensors acts as the gate way for the above-mentioned areas and are placed at strategic points to capture customers interests, popular and moving brands, kinds of customers etc.

This information will enable customer segmentation and create applications designed for each segment such as promotions or discounts especially during launch offers for new products. In conclusion, IoT for SMEs can enable businesses to design enhanced strategies based on captures information through sensors.

Managing warehouses and production lines

Another potential segment that offers a strong application in IoT for SMEs is warehouse management. Sensors enable to track movement of goods in the warehouse or production lines. They also calculate the count of inventory creating a automated systems to create flags when the merchandise/raw material are running short.  Stock replacement/ replenishment requirements can be triggered automatically with alarms.

IoT in supply chain management

Service delivery is another prodigious application of IoT for SMEs. Again, sensors play a critical role in enabling the status of shipment or delivery at every stage. Apart from the above, it is significantly used for calculating improved trajectories for final mile delivery times.

Optimal routes for the delivery to improve the overall customer experience at minimal cost is key application of IoT in this segment.

Predictive and precautionary maintenance

Another application where IoT for SMEs is gaining rapid pace and is highly attractive is predictive and preventive maintenance. Here it enables a system for alerts for early detection and timely replacement of parts or status updates of machines for remote management.

End to end (E2E) operational application of IoT

Intelligent operations begin with integration of data from manufacturing, distribution and sales & marketing divisions. The factual application and advancement of IoT is to integrate all this data in creating new e2e products and services based on preferences of customers in combination to the data collected and validated.

IoT enabled healthcare

Healthcare services and clinics offers personalized service to accompany patients beyond the visits. IoT for SMEs provides solutions for these clinic-based models that result in a competitive advantage.  This enables to redesign the dynamics with patients a simple example could be to prompt a trigger for the ophthalmologic patient to replace glasses or improvement tracking. Use of sensors and Big Data also can give the complete vision of an operation and relevant tracking.

Customer based business models

IoT also offers an opportunity for a personalized service for an SME dedicated to plumbing and its predictive maintenance of spare parts to predict pipe installation failure by reviewing its surrounding conditions via application from your cell phone

The above mentioned are some of the many applications and advantages of enabling IoT for SMEs. The principle remains the same exploiting cutting edge technology allows to improve business model through informed decisions based on the data that IoT provides.

Security is a very important part of the above implementation. All technology is vulnerable to attacks. It is critical for the SMEs to consider security as part of the implementation. Below section illustrates some of the guidelines.

Digital Security for SMEs

Security is an essential aspect for SMEs or large organizations. IoT are also highly vulnerable to such attacks. It is important to factor every aspect of your IT architecture with the right security programs. This is key for deployment and commissioning of your sensors and Big Data programs to secure data.

SMEs need to consider aspects of hardware and software associated with IoT implementation model. Emphasis need to be laid on the types of networks, communications and back-up etc. and take inventory of equipment’s, software’s for the type of security that will protect attacks from identified vulnerabilities.

In summary, SME’s are becoming more digital to deliver a connected and seamless experience, IoT will trend among the latest technologies. The emergence of this technologies give rise to newer job roles such as IoT Managers, IoT Business Designers, full stack developers etc. in this sector. The functional and technical areas of these roles span across the expertise of applying sensors, embedded devices, software and other electronics to businesses with front-end and back-end technologies.

The rise of exponential technologies such as IoT and the need to stay upbeat with it, allows scope for the changing landscape of SME’s through new opportunities and roles. IoT will continue to be in the fore front of this changing landscape for SMEs while it is imperative for them directly boost this digital frontier.

As AI continues to dominate discussions amongst the CXO’s of Fortune 500 companies; Startups might in fact be at the pole position to derive strategic gains accruing from leveraging AI.  Armed with accessible funding, young and upbeat talent professionals and overall buoyancy in the demand consumption, Startups are increasingly challenging and upending incumbent businesses. This is attributed to a substantial extent due to their unwavering focus on adopting exponential – including artificial intelligence(AI) – to acquire, retain customers, embed AI across the business value chain and cement their market share. Several startups have initiated to leverage to disrupt their existing and adjacent industries. The transformative power of AI has been the cornerstone of their exponential growth.

AI continues to be a secret sauce and competitive advantage for startups. Data detonation, lower cost of storage and processing and continuously enriched self-learning machine curated algorithms, AI will continue to be a huge multiplier for startups – by bolstering customer acquisition and retention to improving efficiencies, augmenting the top line and getting embedded across the business value chain of their businesses.

Entrenching Competitive Advantage Through AI

Industry and functional use cases of AI range far and wide. It is imperative that startups first consider their business model to identify the drivers to their business, estimate potential uplift and time-to-value to prioritize the order in which AI use cases are deployed. Here are few areas that can deliver immediate impact.

Understand Your Current Customers

AI can both accelerate the speed and quality with which you understand your current customer base – alongside informing startups of the most opportune ways to serve them. For instance:

• Recommender systems – which are extremely mainstream today. Ecommerce websites are increasingly tapping into the purchase and browsing history of customers, not only to surface their next purchase, but also nudge customers through promotional pricing.
• By using natural language processing (NLP) powered chatbots, startups can very quickly build and scale their customer service function – while ensuring continuous availability at a nominal long-term cost. When combined with sentiment extraction and mining, these ‘intelligent’ agents can pre-process customers’ emotions and provide preferential pricing / promotional offers to customers who have had a negative experience with the startup.
• With AI, startups can capture and re-create customer journey maps – how customers navigate pages, information contained on web-pages and ultimately make the purchase decisions. This can enable startups to build more personalized customer experience on their digital platforms. 

Acquire Your Next Customer

In additional to galvanizing CLTV and other retention metrics, AI can also be a crucial part of the customer acquisition process by:

• Improving the accuracy of prospect targeting, by continuously analyzing the drivers of current buyers and mapping them against the cues provided by current prospects – all the while maintaining a lower cost of customer acquisition
• Measuring and benchmarking the success attribution of marketing initiatives and spends – enabling marketing teams to focus their efforts on high-impact marketing activities to continuously drive improved performance.
• In a B2B setting, AI can help judge a browsing prospect’s propensity-to-purchase / act on a call-to-action (based on past users’ actions). This can inform sales teams’ efforts and act as a strong pre-qualification stage in the B2B sales process.

Accelerate Time-to-Market for Products

Beyond commercial functions, AI can also have a transformative impact on the manufacturing and distribution process and help startups realize significant advantages by:

• Pushing closer to 100% on-demand production – through continuous improvement in demand forecasting. This will help create leaner production units, improve predictability in production schedules and reduce wastages due to over-production.
• Using autonomous physical systems for packaging, shipping and warehouse management
• Running smarter and leaner distribution chain – through better demand forecasting at a micro-level, optimizing the size of the delivery vehicle and delivery routes of vehicles (based on inventory shipped) to contain transportation costs.
• Ensuring optimal stock availability at storefront – while balancing wastage due to oversupply and stockouts due to insufficient supply. This would again be incumbent on improving demand forecasting.  

Running a tight ship

Finally, given that startups typically operate on very tight budgets and at high speed of execution, AI is a crucial intervention to help them run a tighter ship. While all these tasks are crucial – whether you are a startup or a large enterprise, AI can help achieve outstanding outcomes at a fraction of the cost. This can happen by:

• Speeding up the recruitment process through bots and NLP-powered automated resume scanning. This can reduce the TAT for new hires, by sifting through a large pile of resumes to identify and shortlist the most viable candidates for interview.
• Augmenting the budgeting and financial planning process using AI. Here AI can help going through multiple reports and compiling the findings that eventually inform budgeting decisions
• Automating administrative tasks such as travel planning and front-desk management.

Why AI Is a Game-changer for Startups

Startups cannot afford to ignore the disruptive power that artificial intelligence can bring. AI is particularly suited to be a game-changer for startups because:

• Given the size and scale at which startups operate, it is easier to conceptualize and implement AI-centric solutions – without having the navigate the bureaucracy of multiple stakeholders in the decision process.
• Scalability and continuous improvement are built into the very fabric of AI – investments in AI by startups will see exponential value realization with enriched data sets and refined algorithms.
• The need for speed and cost efficiencies is paramount for startups. For startups to truly disrupt their industry incumbents, speed is of essence. A slow pace of growth usually kills startups before their story even takes wings.
• Having seen examples of corporations who ignored their startup rivals burning their fingers (from Blockbuster and Netflix to Yahoo and PageRank) traditional incumbents are increasingly taking note of technology savvy startups and partnering with them to entrench their market position, through VC’s and startup accelerators. If focusing on channels is crucial to growth in your industry, AI-centric processes will provide a clear competitive differentiation over your rivals.

AI is both a necessity and an important lever for Startups to grow exponentially in their markets. Through AI, Startups will be better positioned to successfully disrupt their incumbents, win market share and customer delight. Startups not actively harnessing the power of AI to achieve speed and manage scale will be doing so at their own peril.

AI and blockchain are two of the prime drivers in the technology space that catalyze the pace of innovation and demonstrating radical shifts across every industry. Each of this technical venture comes with a degree of technical complexity and business implications. Fusion of the two will be able to redesign the entire technical landscape along with a human effect from scratch.

Blockchain has its own limitations, it is a mix of  technology-related and culture influence from the financial services sector, but most of them can be conceited by AI in a way or another.

The illustrated points below will be able to give a gist of the potentials that can be realized at the intersection of AI and Blockchain:

Energy consumption in mining: Mining has already proven that it requires tons of energy and is heavy in the economic perspective. AI has mastered in optimizing energy consumption across multiple sectors, similar results can be expected for the blockchain as well. AI can dramatically reduce the costs of maintaining servers and validate potential savings to lower investments in mining hardware.

Federated Learning: Blockchain is growing at a steady pace of 1MB every 10 minutes. Blockchain pruning  is a possible solution through AI. A new decentralized learning system such as federated learning, for example, or new data sharing techniques to make the system more efficient.

Security: Concerns still exist on the security system of built-in layers and applications  for Blockchain  (e.g., the DAO, Bitfinex, etc.). The mileage created by machine learning in the last two years makes AI a solid candidate for the blockchain to guarantee secure applications deployment, especially given the fixed structure of the system.

Blockchain-AI Data gates: Blockchain has proven its ability for record keeping, authentication, and execution while AI drives decisions by assessing/understanding patterns and datasets, ultimately engendering autonomous interaction. The combo (AI and blockchain) will be become a data gate with these several characteristics that will ensure a seamless interaction in the nearest future.

Auditing of AI through blockchain: AI is seen as a black box ( complex set of calculations and algorithms) to distinguish patterns or trends. This makes it a difficult task for the humans to govern the choices taken by the artificial intelligence in yielding results. Accountability of the AI black box is seen as biggest challenge, considering  concerns across the community for tampering or the altering happening to the calculations for the given input which eventually reflects in the output generated. This challenge can be easily comprehended by the blockchain innovation. Implementing robust auditing of these calculations utilizing the blockchain is seen as the biggest driver for enhancing the credibility of the business organizations and reinstating trust in the reliability of the information.

Leverage on Artificial Trust: Future roadmap of this fusion can successfully lead into creation of virtual agents that will create new ledger by themselves. Machine to machine interaction will be the new norm reinstating trust in a secure way to share data and coordinate decisions, as well as a robust mechanism to reach a quorum.

Machine performance monitoring and changes: Blockchain miners (companies and individuals)  pour an incredible amount of money into specialized hardware components. AI can complement such as machine/equipment monitoring to deploy more efficient systems and do away with the unproductive heavy ones.

Blockchain for better information management: AI has a proven mechanism that runs of an incorporated or centralized database. In such a case, there are always chances for information occurrence of a mishap, i.e. gets lost, altered, or undermined. 

Blockchain and artificial intelligence fusion can eliminate the above concern. Under the umbrella of blockchain the data is decentralized and stored within different nodes or systems. This reinstates trust on  that your information is safe and unaltered. Most importantly the information is time-stamped and is in the sequence making recuperation less demanding and exact. 

Some key challenges on the block: The fusion throws open technical and ethical implications arising from the interaction between these two technologies, such as the need to edit data on a blockchain and most importantly the duo pushing to become data hoarder. Experimentations alone will be able to provide a detailed answer on these lines.

In conclusion blockchain and AI are the two sides of the technology spectrum. One efficiently fosters centralized intelligence while the other promotes decentralized applications in an open-data environment. The fusion of the two will be an intelligent way to amplify positive externalities and advance mankind, most importantly reap the maximum potential for business needs.

Across the world of technology, we are seeing the proliferation of new age developments across software and hardware – titled “Exponential Technologies”. The term refers to a wide range of recent technology breakthroughs – Artificial Intelligence, Internet of Things, Cloud Computing, Augmented and Virtual Reality, Blockchain and the allied. They are collectively referred to as ‘exponential’ considering the humungous potential value that they could possibly add to business. As these technologies continue to mature in their development and adoption, the world is gaining a more concrete insight into the worth of these technologies and their use cases. 2019 will most certainly be the year where these technologies will go mainstream – and deliver exponential value to their proponents. With high investor interest (and money) riding on these new age technologies, I am confident that in 2019, there will be a high uptake in their commercialization. Here are the top10 trends that I foresee in 2019 in exponential technologies :

1. Blockchain Beyond the Hype

In 2018, there was no doubt a lot of excitement and buzz as technology vendors and investors started investigating blockchain and cryptocurrency. In 2019, expect blockchain to move beyond the hype and enter the mainstream. Gartner estimates that blockchain applications will create $3.1 trillion in business value by 2030. Over 2018, several tech-savvy businesses started their own experiments with blockchain in areas such as supply chain, which is ripe for a blockchain-powered disruption. Within blockchain, I foresee:

Increased collaboration between businesses and tech vendors to unlock the power of blockchain across multiple use cases. Given its immutable and decentralized nature, blockchain will be invaluable in sectors such as manufacturing, defense and financial services – and we will see innovative use cases coming out of these domains

Within blockchain, smart contracts specifically will gain immense traction. The business value of smart contracts is remarkably clear – they drastically reduce the time and effort for routine but lengthy paperwork processes, while maintaining the sanctity through a blockchain network

Due to the numerous crypto frauds seen uncovered in the last year, more and more sovereign governments will push legislation to regulate and establish clear rules around blockchain and cryptocurrency. I have no doubts that this will have a net positive impact – as it will demonstrably improve the consumer confidence and enterprise adoption for these technologies by laying down a clear legal framework for their use

2. 3rd Platform Technology to Accelerate Digital Transformation

A combination of social, mobile, data-driven decision-making and cloud infrastructure and processing is commonly referred to today as 3rd platform technology. In 2019, there will be no stopping the juggernaut of internal IT departments moving ever faster towards digital technology.

According to a research by IDC, it is expected that by 2023, 75% of all IT spending will be on such 3rd platform technology, with over 90% of all enterprises building “digital native” IT environments

Further advanced technologies such as distributed cloud, hyperagile app technologies and architectures, AI at the edge and AI-powered voice UIs will be central to how enterprises enable digital transformation using 3rd platform technologies.

This expansion in demand for 3rd platform technologies will be the outcome on increasing pressures on internal IT to become profit centers and unlocking new sources of revenue for the parent enterprise. Using easily scalable and replicable digital frameworks, early adopter IT departments would be able to commercialize this technologies to their competitors while giving their businesses critical competitive advantage

3. Quantum Computing to Come of Age

Quantum computing is a non-traditional form of computing operating on the quantum state of subatomic particles and representing information as elements denoted through quantum bits. The unmitigated rise in the development and permeation of quantum computing is the third key trend that I see for 2019. It is estimated that by 2023, 20% of organizations will carve out budgets for quantum computing projects, as opposed to less than 1% today.

 With heavier software paradigms such as Internet of Things, Artificial Intelligence and blockchain achieving mainstream status, there will be large scale demand for quantum computing to come out of the shadows of academia and into business. Quantum computing will move well beyond a buzzword and will be part of multiple projects at an experimental scale at corporations.

Quantum Computing will succeed where traditional computing has failed, providing parallel execution and exponential scalability. Such systems will take on problems too complex for a traditional approach or where the latency for traditional algorithms would be untenable

Business leaders across multiple industries – automotive, financial, insurance, pharmaceuticals, military and research organizations – will see massive gains through the advancements in Quantum Computing .

 4.Acceleration in the Pervasiveness of the Internet of Things

While Internet of Things has demonstrably hit mainstream status across industries such as consumer goods and retail, and use cases such as supply chain and logistics, we will see further acceleration in its adoption in 2019

 IOT-enabled hardware devices will proliferate nearly all walks of human life. Devices from sensors, wearables, smart assistants and wearables will be a feature in everyday life for most individuals in the developed world and will be a key focus for powering digital transformation

With increasing demand for IOT-powered devices across use cases will definitively bring endpoint security into focus for enterprises. As IOT devices become the first frontier for communication with consumers through highly sensorized environments, we will see a rapid escalation in the adoption of endpoint security practices and software

To support this deep network of the Internet of Things will require an immediate focus on rapidly enabling 5G connectivity in 2019. Not having a robust underlying infrastructure to support IOT will be disastrous for businesses and individuals who will be highly reliant on it for their day-to-day activity.

 5. Convergence of AI, Blockchain, Cloud and IO

Could a future software stack comprise AI, Blockchain and IOT running on the cloud? It is not too hard to imagine how these exponential technologies can come together to create great value. In 2019, I expect that we will see a strong spread of use cases that effectively combine these technologies.

Internet of Things devices will largely be the interface with which consumers and other societal stakeholder will interact. Voice-enabled and always connected devices – such as Google Home and Amazon’s Alexa will augment the customer experience and eventually become the primary point of contact with businesses

Artificial Intelligence frameworks such as Speech Recognition and Natural Language Processing are making huge advances. These will be the translation layer between the sensor on one end and the deciphering technology on the other end

Blockchain-like decentralized databases will act as the immutable core for managing contracts, consumer requests and transactions between various parties in the supply chain

Cloud will be the mainstay for running these applications requiring huge computational resources and very high availability. I expect more cloud vendors to come forward (Amazon and Google for instance already have) with specialized cloud frameworks to handle the torrent of requests that these type of applications would require.

6.New UI/UX Interfaces to Emerge on the Scene

To unlock and harness the true value of exponential technology it is incumbent that we do not rely only on existing paradigms of end-user interfaces such as web and mobile. We need to reinvent new paradigms and explore game changing new interfaces that will help usher better customer and user experiences.

 Conversational platforms – ones which are primarily activated through voice and voice-recognition AI will conduct numerous exchanges on behalf of customers. Already we are seeing rapid adoption of conversational interfaces such as Google Home, Amazon Alexa and Apple’s Siri. These will only grow and prominence and entire CX use cases will be centered around these platforms

Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) will be increasingly leveraged across a vast selection of topical use cases. Incorporating these alongside traditional interfaces will be crucial to delivering the future of an immersive user experience. According to Gartner, we will shift from thinking about individual devices and fragmented user interface (UI) technologies to a multichannel and multimodal experience.

These immersive experience-led interfaces such as VR and AR will become increasingly popular, with 70% of enterprises experimenting with such technology for consumer and enterprise use and 25% of organizations deploying it into production.

7.Edge Computing to become an Enterprise Mandate

Simply put, edge computing is a computing topology in which information processing, and content collection and delivery, are placed closer to these endpoints. For reducing the latency running AI algorithms and eventual response times, edge computing will become an enterprise mandate for use cases involving a convergence of IOT and AI.

In 2019, adoption of edge computing will be driven by the need to keep the processing power close to endpoints as opposed to a centralized cloud server. Having said that, edge computing will not necessitate the creation of a new architecture. Cloud and edge computing will complement each other. Cloud services will be charged with centralized service execution, not only on centralized servers, but also across distributed servers on-premises and on-the-edge devices themselves.

Five years down the line expect to see specialized AI chips, supporting greater processing power, storage and other advanced capabilities. They will be incorporated into a wider array of edge devices. Not too far into the future, we will see 40% of organizations’ cloud deployments include an element of edge computing and 25% of endpoint devices and systems will execute AI algorithms.

We will see more intelligent and empowered edge computing devices as well. According to Gartner, storage, computing and advanced AI and analytics capabilities will expand the capabilities of edge devices through 2028.

8. DevOps Augmented by AI

Despite almost universal acceptance of the DevOps framework across global enterprises, adoption has been patchy and slow. This is due to numerous reasons, ranging from a distributed toolset and a paucity of expert practitioners. However with the emergence of AI, we will see an increased process automation between software development and deployment, accelerating the enablement of DevOps

AI-powered QA suites will increase the automation quotient in the DevOps process. Given the advancements seen in automation, AI will rapidly intervene in the QA process across unit testing, regression testing, functional testing and user acceptance testing.

DevSecOps will combine the power of DevOps and AI in the field of information security. A centralized logging architecture recording suspicious activity and threats combined with ML-based anomaly detection techniques will empower developers to accurately pinpoint potential threats to their system and secure it for the future.

AI will also break the cultural barriers that typically exist between developer and operations teams. . AI-powered systems will enable DevOps teams to have a single, unified view into system issues across a complex toolchain while improving the collective knowledge of anomalies detected and the pathways for redressal.

 9.Autonomous Things on the Rise:

At present, we are seeing experiments at an advanced level in the field of autonomous things. Autonomous things comprise whole gamut of unmanned objects – from drones, cars and robots. In 2019, I expect there to be a steady rise in the adoption and appreciation of this area of technology

 Autonomous things of today are largely centered around the current paradigm of basic automation and rigid if-else programming rules. The next revolution in the field of autonomous things will be by exploiting the power of AI to exhibit more advanced, proactive and multi-threaded behaviors

Demand for autonomous things will continue to grow, specifically for autonomous vehicles. According to a Gartner survey, by 2021, 10% of new vehicles will have autonomous driving capability, compared to less than 1% in 2017.

Robotics and drones powered by AI will be able to address more complex use cases bringing in further efficiencies to incumbent businesses in the field of logistics delivery, warehouse management and manufacturing

10. AI to Disrupt Cybersecurity

Finally, the last key trend in the exponential technologies space for 2019 pertains to cybersecurity. While this is a remarkably advanced field, we will see continued growth and evolution of cybersecurity in combination with artificial intelligence

 Using anomaly detection and machine learning, AI will hugely disrupt the field of cyber security. Security practitioners will be empowered to identify intrusions and malafide behavior faster using automated, always-on algorithms to constantly survey the secured network for wrongful activity and address concerns before they break-ins occur

AI can be quickly training over a massive data set of cyber security, network, and even physical information. Cyber security vendors will soon roll out AI-enabled solutions that will learn at an abstract level to detect and block abnormal behavior, even when this behavior does not fit within a known pattern. I expect that in 2019 companies will incorporate ML into every category of cybersecurity products.

By extension, we will see a fight between good AI and bad AI in the domain of cybersecurity. There are genuine fears that the next generation of attacks will not be carried out by human hackers but pieces of code designed to rapidly infiltrate a secure environment. Countering that with so-called ‘good AI’ will be crucial in undermining the impact these fast-paced attacks can have

The AI Race is fast heating up. While private enterprises tend to view this through a lens of achieving competitive advantage through breakthrough business and process innovation, there is a much larger play between nations competing to achieve supremacy in the domain of Artificial Intelligence. Across the globe – from Japan in the east to United States in the west – every major industrialized nation is ramping up their efforts (and rhetoric) to build indigenous AI capability. These economies have shown great interest, from the federal to the local levels, to achieving the much-vaunted status as the world leader in Artificial Intelligence. While the approaches by each country may differ – the end goal is some variation of achieving a preeminent position as the single distinguished player in the field of AI.

At this point, it is natural to ask – why? Why are entire economies and governments frantically organizing themselves to win in this race? The answer lies mainly in the size of what is at stake. According to a recent report by global consulting company PwC, AI’s contribution to the global economy is expected to be $15.7 trillion by 2030. The nation that serves the largest portion of this need will not only have the highest revenue, but also the highest number of in-demand professionals, the lowest dependency on other nations in this massive field of work, alongside being the singular force to reckon with in the future of the world.

This might explain why, today, the US and China are at the forefront of this technology. According to the same report, China and North America will see the largest part of the global value-pie ($7trillion and $3.7 trillion respectively). When the stakes are this high, you probably do not want to depend on the benevolence of others. You ought to ensure that every capability you require is available within your own shores. In China, the government stands strongly behind AI adoption, announcing their intention to become “a principal world center of artificial intelligence innovation” by 2030. On the other hand, the US has the highest number of AI startups and one of the deepest wells of venture capital to fund the startups’ endeavors. Not to mention, they are also home to larger tech corporations – Google, Amazon, Facebook, Microsoft, IBM etc. – which are also pioneering AI research in their own way.

While the US and China have taken a quantum leap ahead over their other competitors, the field of AI is not exactly a duopoly. While these two are clearly the leaders across any measurement criteria that you would employ, there are several others in the fray – Japan, South Korea, Germany, France, the UK, Canada, Israel, Russia and India – who are all in various stages of launching their visionary plans and developing on-ground leadership through either private enterprise, public support – or both.

With the size of the prize outlined, the next logical question would be – how is India doing in this space? What steps is India taking to ensure that we do not fall by the wayside as the world runs to win this monumentally important race?

There’s some good news and some not so good news on that front. For one, India is not yet considered among the absolute top rung of AI superpowers today. While we do have significant numbers of STEM graduates passing through academia each year, most of them are currently involved in the so-called lower end of the IT value chain – infrastructure services and maintenance etc. On the bright side, India is uniquely positioned to deliver strong AI leadership, assuming we take steps in the right direction on the policy side, as well as in industry-academic collaboration.

Why do I feel India is uniquely positioned? Consider the following:

• India continues to have a strong continuing focus on STEM education. As AI enters the mainstream curricula of our universities, we will realize the benefits of having a robust intellectual capital in this arena.
• Typically, it is data that powers an AI application. India, with the second largest population in the world (and increasingly connected to smart devices) has the potential to not only generate massive data sets, but also one of the most diverse set of data due to the inherent diversity across class, language and other cultural aspects – which can power the most enriched applications of AI
• There is a strong impetus on the policy front in India for AI – with Digital India, Skill India programs started by this government, in addition to constituting NITI Aayog – a national-level think-tank to execute on a vision rich with emerging technology

So how can we combine India’s inherent advantages, with some strong moves already made in the AI space, to possibly achieve AI supremacy in the near future? Here are three clear areas that require a high degree of attention and action to fulfil that vision.

1. Lead with Policy

With a strong, forward-looking government, India is already making the right noises on the development of AI. NITI Aayog – the think-tank I had mentioned earlier – has constituted a committee to study and deliver a National AI Strategy for India. In their June 2018 discussion paper, they identified 5 areas where India is uniquely poised to deliver AI leadership due to our intrinsic advantages – healthcare, agriculture, education, smart cities and smart mobility and transportation. While the Aadhar program has had its critics, it is likely to be instrumental in building a massive training set of citizen data, enabling India to build some thought-leading application in AI. The government has also pledged to put their money where their mouth is – with $480mn projected to be spent on the Digital India program in 2018. While this spending pales in comparison to the spending of other countries (China has committed $150bn up to 2030), it will be instrumental for founding a strong test-bed for incubating our AI vision. The government is also planning a national data and analytics platform in collaboration with private players to utilize the huge amount of data with the help of AI.

2. Facilitate through Academia

Close to 2.6mn students graduated out of STEM fields from India in 2016. While I mentioned that these graduates have anywhere between no to a rudimentary understanding of AI today – it does represent the huge footfall seen in these fields, who would be well-served through a healthy training in AI-centric technologies.

The more pressing problem can be seen in core AI research. While India is ranked 5th in the world today terms of number of papers published (14,864 between 2010-16), we are still a fair way behind the US (63,344) and China (39,820) on this metric. Worse still, India ranks a distant 19th on the metric of H-Index (measured between 1996 and 2016), which leads to a concern on whether our current research is citation-worthy or rooted in business applicability. So, while the appetite for research exists, the contribution to the overall body of knowledge still needs some upgrading.

To address this, the aforementioned NITI Aayog discussion paper, recommends the set-up of a 2-tier integrated approach for boosting research in both core AI and applied AI. The first – COREs (Centers of Research Excellence in Artificial Intelligence) will be focused on developing a better understanding of existing core research and pushing technology frontiers through creation of new knowledge. The second – ICTAI (International Centre for Transformational Artificial Intelligence) will have a mandate of developing and deploying application-based research through Private sector collaboration. This framework would also consist an umbrella organization addressing issues relating to access to finance, social sustainability and the global competitiveness of the technologies developed. This body would be similar to the Campus for Research Excellence and Technological Enterprise (CREATE), Singapore program or Innovate UK.

3. Implement through Private Industry

While the first two points deal with strengthening the backbone of AI research and education, this final aspect deals with building high-class industry-grade IP with wide applicability. Due to a huge democratization in information, both large tech corporations and startups are aware of the challenges that can be solved through AI and are building solutions to address these challenges. Behemoths IT and consulting players are already investing in academic partnerships to set up a base for IP development and workforce training. Startups too, while not similarly endowed, are looking to build visionary products that will transform the industry through collaboration with academia. Through such an industry-academia collaboration, Indian technology companies would be able to foster synergy by developing bleeding edge research in India which can be gainfully employed to solve global challenges. Extending the Make in India initiative would be crucial to ensure that the intellectual property of the work done by Indians stays in the home country, boosting our credibility in this space.

In conclusion, while India is already among some of the top nations in the world today in the field of Artificial Intelligence, there still is a long way to go to hit the absolute pinnacle in this space. However, given that AI is still is in a nascent stage, there is significant scope for India to still emerge as the leading light in this space. With this sustained and rapid pace of progress, I am certain that India will soon emerge as the preeminent leader in the field of AI.

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 .