India’s Investments in the digital financial infrastructure—known as “India Stack”—have sped up the large-scale digitization of people’s financial lives. As more and more people begin to conduct transactions online, questions have emerged about how to provide millions of customers adequate data protection and privacy while allowing their data to flow throughout the financial system. Data-sharing among financial services providers (FSPs) can enable providers to more efficiently offer a wider range of financial products better tailored to the needs of customers, including low-income customers.

However, it is important to ensure customers understand and consent to how their data are being used. India’s solution to this challenge is account aggregators (AAs). The Reserve Bank of India (RBI) created AAs in 2018 to simplify the consent process for customers. In most open banking regimes, financial information providers (FIPs) and financial information users (FIUs) directly exchange data. This direct model of data exchange—such as between a bank and a credit bureau—offers customers limited control and visibility into what data are being shared and to what end. AAs have been designed to sit between FIPs and FIUs to facilitate data exchange more transparently. Despite their name, AAs are barred from seeing, storing, analyzing, or using customer data. As trusted, impartial intermediaries, they simply manage consent and serve as the pipes through which data flow among FSPs. When a customer gives consent to a provider via the AA, the AA fetches the relevant information from the customer’s financial accounts and sends it via secure channels to the requesting institution. implementation of its policies for consensual data-sharing, including the establishment and operation of AAs. It provides a set of guiding design principles, outlines the technical format of data requests, and specifies the parameters governing the terms of use of requested data. It also specifies how to log consent and data flows.

There are several operational and coordination challenges across these three types of entities: FIPs, FIUs, and AAs. There are also questions around the data-sharing business model of AAs. Since AAs are additional players, they generate costs that must be offset by efficiency gains in the system to mitigate overall cost increases to customers. It remains an open question whether AAs will advance financial inclusion, how they will navigate issues around digital literacy and smartphone access, how the limits of a consent-based model of data protection and privacy play out, what capacity issues will be encountered among regulators and providers, and whether a competitive market of AAs will emerge given that regulations and interoperability arrangements largely define the business.

Account Aggregators (AA’s):

ACCOUNT AGGREGATORS (AAs) is one of the new categories of non banking financial companies (NBFCs) to figure into India Stack—India’s interconnected set of public and nonprofit infrastructure that supports financial services. India Stack has scaled considerably since its creation in 2009, marked by rapid digitization and parallel growth in mobile networks, reliable data connectivity, falling data costs, and continuously increasing smartphone use. Consequently, the creation, storage, use, and analyses of personal data have become increasingly relevant. Following an “open banking “approach, the Reserve Bank of India (RBI) licensed seven AAs in 2018 to address emerging questions around how data can be most effectively leveraged to benefit individuals while ensuring appropriate data protection and privacy, with consent being a key element in this. RBI created AAs to address the challenges posed by the proliferation of data by enabling data-sharing among financial institutions with customer consent. The intent is to provide a method through which customers can consent (or not) to a financial services provider accessing their personal data held by other entities. Providers are interested in these data, in part, because information shared by customers, such as bank statements, will allow providers to better understand customer risk profiles. The hypothesis is that consent-based data-sharing will help poorer customers qualify for a wider range of financial products—and receive financial products better tailored to their needs.

Data Sharing Model : The new perspective:

Paper based data collection is inconvenient , time consuming and costly for customers and providers. Where models for digital-sharing exist, they typically involve transferring data through intermediaries that are not always secure or through specialized agencies that offer little protection for customers. India’s consent-based data-sharing model provides a digital framework that enables individuals to give and withdraw consent on how and how much of their personal data are shared via secure and standardized channels. India’s guiding principles for sharing data with user consent—not only in the financial sector— are outlined in the National Data Sharing and Accessibility Policy (2012) and the Policy for Open Application Programming Interfaces for the Government of India. The Information Technology Act (2000) requires any entity that shares sensitive personal data to obtain consent from the user before the information is shared. The forthcoming Personal Data Protection Bill makes it illegal for institutions to share personal data without consent.

India’s Ministry of Electronics and Information Technology (MeitY) has issued an Electronic Consent Framework to define a comprehensive mechanism to implement policies for consensual data-sharing. It provides a set of guiding design principles, outlines the technical format of the data request, and specifies the parameters governing the terms of use of the data requested. It also specifies how to log both consent and data flows. This “consent artifact” was adopted by RBI, SEBI, IRDAI, and PFRDA. Components of the consent artifact structure include :

1. Identifier : Specifies entities involved in the transaction: who is requesting the data, who is granting permission, who is providing the data, and who is recording consent.
2. Data : Describes the type of data being accessed and the permissions for use of the data. Three types of permissions are available: view (read only), store, and query (request for specific data). The artifact structure also specifies the data that are being shared, date range for which they are being requested, duration of storage by the consumer, and frequency of access.
3. Purpose : Describes end use, for example, to compute a loan offer.
4. Log : Contains logs of who asked for consent, whether it was granted or not, and data flows.
5. Digital signature : Identifies the digital signature and digital ID user certificate used by the provider to verify the digital signature. This allows providers to share information in encrypted form

The Approach : 

THE AA consent based data sharing model mediates the flow of data between producers and users of data, ensuring that sharing data is subject to granular customer consent. AAs manage only the consent and data flow for the benefit of the consumer, mitigating the risk of an FIU pressuring consumers to consent to access to their data in exchange for a product or service. However, AAs, as entities that sit in the middle of this ecosystem, come with additional costs that will affect the viability of the business model and the cost of servicing consumers. FIUs most likely will urge consumers to go directly to an AA to receive fast, efficient, and low-cost services. However, AAs ultimately must market their services directly to the consumer. While AA services are not an easy sell, the rising levels of awareness among Indian consumers that their data are being sold without their consent or knowledge may give rise to the initial wave of adopters. While the AA model is promising, it remains to be seen how and when it will have a direct impact on the financial lives of consumers.

Differences between Personal Data Protection & GDPR ?

There are some major differences between the two.

First, the bill gives India’s central government the power to exempt any government agency from the bill’s requirements. This exemption can be given on grounds related to national security, national sovereignty, and public order.

While the GDPR offers EU member states similar escape clauses, they are tightly regulated by other EU directives. Without these safeguards, India’s bill potentially gives India’s central government the power to access individual data over and above existing Indian laws such as the Information Technology Act of 2000, which dealt with cyber crime and e-commerce.

Second, unlike the GDPR, India’s bill allows the government to order firms to share any of the non personal data they collect with the government. The bill says this is to improve the delivery of government services. But it does not explain how this data will be used, whether it will be shared with other private businesses, or whether any compensation will be paid for the use of this data.

Third, the GDPR does not require businesses to keep EU data within the EU. They can transfer it overseas, so long as they meet conditions such as standard contractual clauses on data protection, codes of conduct, or certification systems that are approved before the transfer. 

The Indian bill allows the transfer of some personal data, but sensitive personal data can only be transferred outside India if it meets requirements that are similar to those of the GDPR. What’s more, this data can only be sent outside India to be processed; it cannot be stored outside India. This will create technical issues in delineating between categories of data that have to meet this requirement, and add to businesses’ compliance costs.

In the past months due to lockdowns and WFH, enterprises have got an epiphany of massive shifts of business and strategic models for staying relevant and solvent. Digital transformation touted as the biggest strategic differentiation and competitive advantages for enterprises faced a quintessential inertia of mass adoption in the legacy based enterprises and remained more on business planning slides than in full implementation. However, Digital Transformation is not about aggregation of exponential technologies and adhoc use cases or stitching alliances with deep tech startups. The underpinning of Digital transformation is AI and how AI strategy has become the foundational aspect of accomplishing digital transformation for enterprises and generating tangible business metrics. But before we get to the significance of AI strategy in digital transformation, we need to understand the core of digital transformation itself. Because digital transformation will look different for every enterprise, it can be hard to pinpoint a definition that applies to all. However, in general terms: we define digital transformation as the integration of core areas of business resulting in fundamental changes to how businesses operate and how they deliver value to customers.

Though, in specific terms digital transformation can take a very interesting shape according to the business moment in question. From a customer’s point of view, “Digital transformation closes the gap between what digital customers already expect and what analog businesses actually deliver.”

Does Digital Transformation really mean bunching exponential technologies? I believe that digital transformation is first and foremost a business transformation. Digital mindset is not only about new age technology, but about curiosity, creativity, problem-solving, empathy, flexibility, decision-making and judgment, among others. Enterprises needs to foster this digital mindset, both within its own boundaries and across the company units. The World Economic Forum lists the top 10 skills needed for the fourth industrial revolution. None of them is totally technical. They are, rather, a combination of important soft skills relevant for the digital revolution. You don’t need to be a technical expert to understand how technology will impact your work. You need to know the foundational aspects, remain open-minded and work with technology mavens. Digital Transformation is more about cultural change that requires enterprises to continually challenge the status quo, experiment often, and get comfortable with failure. The most likely reason for business to undergo digital transformation is the survival & relevance issue. Businesses mostly don’t transform by choice because it is expensive and risky. Businesses go through transformation when they have failed to evolve. Hence its implementation calls for tough decisions like walking away from long-standing business processes that companies were built upon in favor of relatively new practices that are still being defined.

Business Implementation aspects of Digital Transformation

Disruption in digital business implies a more positive and evolving atmosphere, instead of the usual negative undertones that are attached to the word. According to the MIT Center for Digital Business, “Companies that have embraced digital transformation are 26 percent more profitable than their average industry competitors and enjoy a 12 percent higher market valuation.” A lot of startups and enterprises are adopting an evolutionary approach in transforming their business models itself, as part of the digital transformation. According to Mckinsey, One-third of the top 20 firms in industry segments will be disrupted by new competitors within five years.

The various Business Models being adopted in Digital Transformation era are:

• The Subscription Model (Netflix, Dollar Shave Club, Apple Music) Disrupts through “lock-in” by taking a product or service that is traditionally purchased on an ad hoc basis, and locking-in repeat custom by charging a subscription fee for continued access to the product/service
• The Freemium Model (Spotify, LinkedIn, Dropbox) Disrupts through digital sampling, where users pay for a basic service or product with their data or ‘eyeballs’, rather than money, and then charging to upgrade to the full offer. Works where marginal cost for extra units and distribution are lower than advertising revenue or the sale of personal data
• The Free Model (Google, Facebook) Disrupts with an ‘if-you’re-not-paying-for-the-product-you-are-the-product’ model that involves selling personal data or ‘advertising eyeballs’ harvested by offering consumers a ‘free’ product or service that captures their data/attention
• The Marketplace Model (eBay, iTunes, App Store, Uber, Airbnb) Disrupts with the provision of a digital marketplace that brings together buyers and sellers directly, in return for a transaction or placement fee or commission
• The Access-over-Ownership Model (Zipcar, Peer buy) Disrupts by providing temporary access to goods and services traditionally only available through purchase. Includes ‘Sharing Economy’ disruptors, which takes a commission from people monetizing their assets (home, car, capital) by lending them to ‘borrowers’
• The Hypermarket Model (Amazon, Apple) Disrupts by ‘brand bombing’ using sheer market power and scale to crush competition, often by selling below cost price
• The Experience Model (Tesla, Apple) Disrupts by providing a superior experience, for which people are prepared to pay
• The Pyramid Model (Amazon, Microsoft, Dropbox) Disrupts by recruiting an army of resellers and affiliates who are often paid on a commission-only mode
• The On-Demand Model (Uber, Operator, TaskRabbit) Disrupts by monetizing time and selling instant-access at a premium. Includes taking a commission from people with money but no time who pay for goods and services delivered or fulfilled by people with time but no money
• The Ecosystem Model (Apple, Google) Disrupts by selling an interlocking and interdependent suite of products and services that increase in value as more are purchased. Creates consumer dependency

Since Digital Transformation and its manifestation into various business models are being fast adopted by startups, there are providing tough competition to incumbent corporate houses and large enterprises. Though enterprises are also looking forward to digitally transform their enterprise business, the scale and complexity makes it difficult and resource consuming activity. It has imperatively invoked the enterprises to bring certain strategy to counter the cannibalizing effect in the following ways:

• The Block Strategy. Using all means available to inhibit the disruptor. These means can include claiming patent or copyright infringement, erecting regulatory hurdles, and using other legal barriers.
• The Milk Strategy. Extracting the most value possible from vulnerable businesses while preparing for the inevitable disruption
• The Invest in Disruption Model. Actively investing in the disruptive threat, including disruptive technologies, human capabilities, digitized processes, or perhaps acquiring companies with these attributes
• The Disrupt the Current Business Strategy. Launching a new product or service that competes directly with the disruptor, and leveraging inherent strengths such as size, market knowledge, brand, access to capital, and relationships to build the new business
• The Retreat into a Strategic Niche Strategy. Focusing on a profitable niche segment of the core market where disruption is less likely to occur (e.g. travel agents focusing on corporate travel, and complex itineraries, book sellers and publishers focusing on academia niche)
• The Redefine the Core Strategy. Building an entirely new business model, often in an adjacent industry where it is possible to leverage existing knowledge and capabilities (e.g. IBM to consulting, Fujifilm to cosmetics)
• The Exit Strategy. Exiting the business entirely and returning capital to investors, ideally through a sale of the business while value still exists (e.g. MySpace selling itself to Newscorp)

The curious evolution of AI and its relevance in digital transformation

So here’s an interesting question, AI has been around for more than 60 years, then why is it that it is only gaining traction with the advent of digital? The first practical application of such “machine intelligence” was introduced by Alan Turing, British mathematician and WWII code-breaker, in 1950. He even created the Turing test, which is still used today, as a benchmark to determine a machine’s ability to “think” like a human.The biggest differences between AI then and now are Hardware limitations, access to data, and rise of machine learning.

Hardware limitations led to the non-sustenance of AI adoption till late 1990s. There were many instances where the scope and opportunity of AI led transformation was identified and appreciated by implementation saw more difficult circumstances. The field of AI research was founded at a workshop held on the campus of Dartmouth College during the summer of 1956. But Eventually it became obvious that they had grossly underestimated the difficulty of the project due to computer hardware limitations. The U.S. and British Governments stopped funding undirected research into artificial intelligence, leading to years known as an “AI winter”.

In another example, again in 1980, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 80s the investors became disillusioned by the absence of the needed computer power (hardware) and withdrew funding again. Investment and interest in AI boomed in the first decades of the 21st century, when machine learning was successfully applied to many problems in academia and industry due to the presence of powerful computer hardware. Teaming this with the rise in digital, leading to an explosion of data and adoption of data generation in every aspect of business, made it highly convenient for AI to not only be adopted but to evolve to more accurate execution.

The Core of Digital Transformation: AI Strategy

According to McKinsey, by 2023, 85 percent of all digital transformation initiatives will be embedded with AI strategy at its core. Due to radical computational power, near-endless amounts of data, and unprecedented advances in ML algorithms, AI strategy will emerge as the most disruptive business scenario, and its manifestation into various trends that we see and will continue to see, shall drive the digital transformation as we understand it. The following will the future forward scenarios of AI strategy becoming core to digital transformation:

AI’s growing entrenchment: This time, the scale and scope of the surge in attention to AI is much larger than before. For starters, the infrastructure speed, availability, and sheer scale has enabled bolder algorithms to tackle more ambitious problems. Not only is the hardware faster, sometimes augmented by specialized arrays of processors (e.g., GPUs), it is also available in the shape of cloud services , data farms and centers

Geography, societal Impact: AI adoption is reaching institutions outside of the industry. Lawyers will start to grapple with how laws should deal with autonomous vehicles; economists will study AI-driven technological unemployment; sociologists will study the impact of AI-human relationships. This is the world of the future and the new next.

Artificial intelligence will be democratized: As per the results of a recent Forrester study , it was revealed that 58 percent of professionals researching artificial intelligence ,only 12 percent are actually using an AI system. Since AI requires specialized skills or infrastructure to implement, Companies like Facebook have realized this and are already doing all they can to simplify the implementation of AI and make it more accessible. Cloud platforms like Google APIs, Microsoft Azure, AWS are allowing developers to create intelligent apps without having to set up or maintain any other infrastructure.

Niche AI will Grow: By all accounts, 2020 & beyond won’t be for large, general-purpose AI systems. Instead, there will be an explosion of specific, highly niche artificial intelligence adoption cases. These include autonomous vehicles (cars and drones), robotics, bots (consumer-orientated such as Amazon Echo , and industry specific AI (think finance, health, security etc.).

Continued Discourse on AI ethics, security & privacy: Most AI systems are immensely complex sponges that absorb data and process it at tremendous rates. The risks related to AI ethics, security and privacy are real and need to be addressed through consideration and consensus. Sure, it’s unlikely that these problems will be solved in 2020, but as long as the conversation around these topics continues, we can expect at least some headway.

Algorithm Economy: With massive data generation using flywheels, there will be an economy created for algorithms, like a marketplace for algorithms. The engineers, data scientists, organizations, etc. will be sharing algorithms for using the data to extract required information set.

Where is AI Heading in the Digital Road?While much of this is still rudimentary at the moment, we can expect sophisticated AI to significantly impact our everyday lives. Here are four ways AI might affect us in the future:

Humanizing AI: AI will grow beyond a “tool” to fill the role of “co-worker.” Most AI software is too hidden technologically to significantly change the daily experience for the average worker. They exist only in a back end with little interface with humans. But several AI companies combine advanced AI with automation and intelligent interfaces that drastically alter the day to day workflow for workers

Design Thinking & behavioral science in AI: We will witness Divergence from More Powerful Intelligence To More Creative Intelligence. There have already been attempts to make AI engage in creative efforts, such as artwork and music composition. we’ll see more and more artificial intelligence designing artificial intelligence, resulting in many mistakes, plenty of dead ends, and some astonishing successes.

Rise of Cyborgs: As augmented AI is already the mainstream thinking; the future might hold witness to perfect culmination of man-machine augmentation. AI augmented to humans, intelligently handling operations which human cannot do, using neural commands.

AI Oracle : AI might become so connected with every aspect of our lives, processing though every quanta of data from every perspective that it would perfectly know how to raise the overall standard of living for the human race. People would religiously follow its instructions (like we already follow GPS navigations) leading to leading to an equation of dependence closer to devotion.

The Final Word

Digital business transformation is the ultimate challenge in change management. It impacts not only industry structures and strategic positioning, but it affects all levels of an organization (every task, activity, process) and even its extended supply chain. Hence to brace Digital led disruption, one has to embrace AI-led strategy. Organizations that deploy AI strategically will ultimately enjoy advantages ranging from cost reductions and higher productivity to top-line benefits such as increasing revenue and profits, richer customer experiences, and working-capital optimization.

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The buoyancy around artificial intelligence adoption is palpable. It seems that not a day goes by without one of the giants in the industry coming out with a breakthrough application of AI, or a new nuance is added to the overall body of knowledge. Horizontal and industry-specific use cases of AI abound and there is always something exciting around the corner every single day.

However, with the keen interest from global leaders of multinational corporations, the conversation is shifting towards having a strategic agenda for AI in the enterprise. Business heads are less interested in topical experiments and minuscule productivity gains made in the short term. They are more keen to understand the impact of AI in their areas of work from a long-term standpoint. Perhaps the most important question that they want to see answered is – what will my new AI-enabled enterprise look like?

The question is as strategic as it is pertinent. For business leaders, the most important issues are – improving shareholder returns and ensuring a productive workforce – as part of running a sustainable, future-ready business. Artificial intelligence may be the breakout enabler of our time, but business leaders are more occupied with trying to understand just how AI can usher in a new era of their business, how it is expected to upend existing business value chains, unlock new revenue streams, and deliver improved efficiencies in cost outlays. Let us try to answer these questions.

AI is Disrupting Existing Value Chains

Ever since Michael Porter first expounded on the concept in his best-selling book, Competitive Advantage: Creating and Sustaining Superior Performance, the concept of the value chain has gained great currency in the minds of business leaders globally. The idea behind the value chain was to map out the interlinkages between the primary activities that work together to conceptualize and bring a product / service to market (R&D, manufacturing, supply chain, marketing, etc.), as well as the role played by support activities performed by other internal functions (finance, HR, IT etc.). Strategy leaders globally leverage the concept of value chains to improve business planning, identify new possibilities for improving business efficiency and exploit potential areas for new growth.

Now with AI entering the fray, we might see new vistas in the existing value chains of multinational corporations. For instance:

Manufacturing is becoming heavily augmented by artificial intelligence and robotics. We are seeing these technologies getting a stronger foothold across processes requiring increasing sophistication. Business leaders need to now seriously consider workforce planning for a labor force that consists both human and artificial workers at their manufacturing units. Due attention should also be paid in ensuring that both coexist in a symbiotic and complementary manner. Logistics and Delivery are two other areas where we are seeing a steady growth in the use of artificial intelligence. Demand planning and fulfilment through AI has already reached a high level of sophistication at most retailers. Now Amazon – which handles some of the largest and most complex logistics networks in the world – is in advanced stages of bringing in unmanned aerial vehicles (drones) for deliveries through their Amazon Prime Air program. Business leaders expect outcomes to range from increased customer satisfaction (through faster deliveries) and reduction in costs for the delivery process.

Marketing and Sales are constantly on the forefront for some of the most exciting inventions in AI. One of the most recent and evolved applications of AI is Reactful. A tool developed for eCommerce properties, Reactful helps drive better customer conversions by analyzing the clickstream and digital footprints of people who are on web properties and persuades them into making a purchase. Business leaders need to explore new ideas such as this that can help drive meaningful engagement and top line growth through these new AI-powered tools.

AI is Enabling New Revenue Streams

The second way business leaders are thinking strategically around AI is for its potential to unlock new sources of revenue. Earlier, functions such as internal IT were seen as a cost center. In today’s world, due to the cost and competitive pressure, areas of the business which were traditionally considered to be cost centers are require to reinvent themselves into revenue and profit centers. The expectation from AI is no different. There is a need to justify the investments made in this technology – and find a way for it to unlock new streams of revenue in traditional organizations. Here are two key ways in which business leaders can monetize AI:

Indirect Monetization is one of the forms of leveraging AI to unlock new revenue streams. It involves embedding AI into traditional business processes with a focus on driving increased revenue. We hear of multiple companies from Amazon to Google that use AI-powered recommendation engines to drive incremental revenue through intelligent recommendations and smarter bundling. The action item for business leaders is to engage stakeholders across the enterprise to identify areas where AI can be deeply ingrained within tech properties to drive incremental revenue.

Direct Monetization involves directly adding AI as a feature to existing offerings. Examples abound in this area – from Salesforce bringing in Einstein into their platform as an AI-centric service to cloud infrastructure providers such as Amazon and Microsoft adding AI capabilities into their cloud offerings. Business leaders should brainstorm about how AI augments their core value proposition and how it can be added into their existing product stack.

AI is Bringing Improved Efficiencies

The third critical intervention for a new AI-enabled enterprise is bringing to the fore a more cost-effective business. Numerous topical and early-stage experiments with AI have brought interesting success for reducing the total cost of doing business. Now is the time to create a strategic roadmap for these efficiency-led interventions and quantitatively measure their impact to business. Some food for thought for business leaders include:

Supply Chain Optimization is an area that is ripe for AI-led disruption. With increasing varieties of products and categories and new virtual retailers arriving on the scene, there is a need for companies to reduce their outlay on the network that procures and delivers goods to consumers. One example of AI augmenting the supply chain function comes from Evertracker – a Hamburg-based startup. By leveraging IOT sensors and AI, they help their customers identify weaknesses such as delays and possible shortages early, basing their analysis on internal and external data. Business leaders should scout for solutions such as these that rely on data to identify possible tweaks in the supply chain network that can unlock savings for their enterprises.

Human Resources is another area where AI-centric solutions can be extremely valuable to drive down the turnaround time for talent acquisition. One such solution is developed by Recualizer – which reduces the need for HR staff to scan through each job application individually. With this tool, talent acquisition teams need to first determine the framework conditions for a job on offer, while leaving the creation of assessment tasks to the artificial intelligence system. The system then communicates the evaluation results and recommends the most suitable candidates for further interview rounds. Business leaders should identify such game-changing solutions that can make their recruitment much more streamlined – especially if they receive a high number of applications.

The Customer Experience arena also throws up very exciting AI use cases. We have now gone well beyond just bots answering frequently asked questions. Today, AI-enabled systems can also provide personalized guidance to customers that can help organizations level-up on their customer experience, while maintaining a lower cost of delivering that experience. Booking.com is a case in point. Their chatbot helps customers identify interesting activities and events that they can avail of at their travel destinations. Business leaders should explore such applications that provide the double advantage of improving customer experience, while maintaining strong bottom-line performance.

The possibilities for the new AI-enabled enterprises are as exciting as they are varied. The strategic areas shared herein are by no means exhaustive, but hopefully seed in interesting ideas for powering improved business performance. Strategy leaders and business heads need to consider how their AI-led businesses can help disrupt their existing value chains for the better, and unlock new ideas for improving bottom-line and top-line performance. This will usher in a new era of the enterprise, enabled by AI.

With the global lock down caused by the COVID-19 and the unforeseen loss of business momentum , the luxury of time now seems to have disappeared completely. Businesses that once mapped strategy planning in one- three-year phases must now reset and scale their strategic initiatives in a matter of days or weeks. In one of the survey initiated by Harvard university , about 70 percent of  top fortune 1000 companies senior executives said the pandemic is likely to accelerate the pace of their business transformation. The acceleration is evident already across sectors and geographies. Consider how multiple banks have swiftly migrated physical channels online. How healthcare providers have moved rapidly into tele-health, insurers into self-service claims assessment, and retailers into contactless shopping and delivery.

The COVID-19 crisis seemingly provides a sudden glimpse into a future world, one in which artificial intelligence has become central to every interaction, forcing both enterprises and individuals further up the adoption curve almost overnight. A world in which digital channels become the primary customer-engagement model, and automated processes become a primary driver of productivity—and the basis of flexible, transparent, and stable supply chains. A world in which agile ways of working are a prerequisite to meeting seemingly daily changes to customer behavior. This being powered by a robust AI driven algorithmic engines . If a silver lining can be found, it might be in the falling barriers to improvisation and experimentation that have emerged among customers, markets, regulators, and organizations. In this unique moment, enterprises can learn and progress more quickly than ever before. The ways they reset and revive post crisis will deeply influence their performance in tomorrow’s transformative world, providing the opportunity to retain greater agility as well as closer ties with customers, employees, and suppliers. Those that are successfully able to make gains  will likely be more successful during recovery and beyond.

Now is the time to reassess business strategy and curate AI strategy core to the business models & processes—to provide near-term readiness to employees, customers, and the broad set of stakeholders to which businesses are increasingly responsible and those that position you for a post crisis world. In this world, some things will snap back to previous form, while others will be forever changed. Playing it safe now, understandable as it might feel to do so, is often the worst option.

A Black Swan event demands new strategic approaches : AI Strategy comes to the rescue

Every enterprise knows the virtues of  how AI pilots new business models in “normal” times, but very have implemented AI strategy @scale and velocity suddenly required by the COVID-19 crisis. That’s because in normal times, the customer and market penalties for widespread “test and learn” can seem too high, and the enterprises obstacles too steep. Shareholders of public companies demand immediate returns. Finance departments keep tight hold of the funds needed to move new initiatives forward quickly. Customers are often slow to adjust to new ways of doing things, with traditional adoption curves reflecting this inherent inertia. And organizational culture, with its own siloes, hinders agility and collaboration. As a result, enterprises often experiment at a pace that fails to match the rate of change around them, slowing their ability to learn fast enough to keep up. Additionally, they rarely embrace the acceleration needed to move quickly from piloting initiatives to scaling the successful ones, even though analyst studies have shown that swift moves to curate AI strategy early and at scale, combined with a sizeable allocation of resources against AI implementation , correlate highly with value creation  As the COVID-19 crisis forces your customers, employees, and supply chains into digital channels and new ways of working, now is the time to ask : Does my enterprise have an AI strategy to reimagine customer experiences , innovate new products & services and transform my business for competitive advantage ? Strange as it may seem, right now, in a moment of crisis, is precisely the time to boldly advance your move to curate an AI strategy .

AI Strategy Curation : Strategic Focus Areas :

Crafting an AI strategy goes beyond building light weight , beta mode algorithms , pursuing adhoc business problems for driving AI engagements or cobbling up together a bunch of AI geeks ; it requires a strategic approach driven by boards , CXOs’ , business leaders and decision makers to focus on the following key areas :

1.Craft Novel Business capabilities embedded with AI

By now you have built your contingency response model and insights hub;  you need to coordinate your crisis response. This insights hub provides a natural gathering point for crucial strategic information, helping you stay close to the quickly evolving needs of core customer segments, and the ways in which competitors and markets are moving to meet them. Mapping these changes helps address immediate risks, to be sure, but it also affords looking forward in time at bigger issues and opportunities—those that could drive significant disruption as the crisis continues. Just as AI has disrupted business models and value chains in the past, the COVID-19 crisis will set similar “ecosystem”-level changes in motion—not just changes in economics but new ways of serving customers and working with suppliers across in a new ecosystem.  In the immediate term, for example, most enterprises are looking for virtual capabilities for their previously physical offerings, or at least new ways of making them accessible with minimal physical contact. The new offerings that result can often involve new partnerships or the need to access new platforms and digital marketplaces in which your company has yet to participate. As you engage with new partners and platforms, look for opportunities to move beyond your organization’s comfort zones, while getting visibility into the places you can confidently invest valuable time, people, and funds to their best effect. AI based strategy that involves building recommended intelligence systems,  reasoning and intuition to address complex problems and explore ideal future states, will be crucial.

2. Embed AI into your core business model

Going beyond comfort zones requires taking an end-to-end view of your business and operating models. Even though your resources are necessarily limited, the experience of leading enterprises suggests that focusing on embedding AI in to the areas that touch more of the core of your business will give you the best chance of success, in both the near and the longer term, than will making minor improvements to noncore areas. Enterprises that make minor changes to the edges of their business model nearly always falter in their business goals. Tinkering leads to returns on investment below the cost of capital and to changes that are too small to match the external pace of disruption. Enterprises that rapidly adopts embedding AI driven algorithms and using those to redefine their business at scale have been outperforming their peers. This will be increasingly true as companies deal with large amounts of data in a rapidly evolving landscape and look to make rapid, accurate course corrections compared with their peers. On a short term basis , this may mean , opening up business models for introspection , however, embedding AI into the core business areas : marketing , sales , supply chain , finance will radically change your enterprise’s ability to derive insights & intelligence.

3. Reset your business strategies with AI

No enterprise can accelerate the delivery of all its strategic imperatives without looking to M&A to speed them along. This is particularly true with AI strategy, where M&A can help companies gain talent and build capabilities, even as it offers access to new products, services, and solutions, and to new market and customer segments. More broadly, we know from research from previous black swan events  that enterprises that invest when valuations are low outperform those that do not. In more normal times, one of the main challenges enterprises face in their AI led transformations and adoption is the need to acquire AI  talent and capabilities through acquisitions of startups that are typically valued at multiples that capital markets might view as dilutive to the acquirer. The current downturn could remove this critical roadblock, especially with enterprises temporarily free from the tyranny of quarterly earnings expectations.

In the next part of the series , I will elaborate on the steps and interventions that are required to craft & curate an AI strategy . Stay Tuned…..

The strategic perspectives provided herein will provide you crucial overview of the AI’s increasing prevalence amongst Indian industry, government and peripheral ecosystem and the significant impact AI will generate for India in the coming years and the possible strategic considerations that India needs to initiate to attain AI supremacy. The ensuing details also highlights the relative comparison amongst India, China and USA on the steady progress being done in AI adoption. VC firms, PE funds and investors attempting to understand where to target investment, what offerings and capabilities would lead to better performance and gains, and how to capitalize on AI opportunities, it’s crucial for them to understand the International economic potential of AI for now and projections in the coming years. Cutting across all these strategic considerations is how to build responsible AI operating models and keep it transparent enough to maintain the confidence of customers and wider stakeholders.

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

Without doubt, AI is going to be a big game changer in the international setting. A previous set of reports from multiple analysts concluded that AI could contribute up to $15.7 trillion to the global economy in 2030, more than the current output of China and India combined. Of this, $6.6 trillion is likely to come from increased productivity and $9.1 trillion is likely to come from consumption-side effects. Global GDP will be up to 14% higher in 2030 as a result of the accelerating development and take-up of AI from the standpoint of direct economic impact of AI, China and USA will have greatest gains in GDP. Even though USA will reach its peak of AI led growth faster due to huge opportunities in parallel technologies implementations and advanced customer readiness for AI.

China, on the other hand will have a slower but stable rise in GDP gains, post COVID 19 because a large portion of Chinese GDP comes from manufacturing, a sector which is highly susceptible to AI disruption in its operation, and also a higher rate of capital re-investment within Chinese economy compared to EU and USA. As productivity in China eventually catches up with USA , USA will focus more on importing AI-enabled products from China due to economically cheap alternative China provides. Hence by 2030, China will see much larger impact in its GDP.

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

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

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

The consumer revolution set off by AI opens the way for massive disruption as both established businesses and new entrants drive innovation and develop new business models. A key part of the impact of AI will come from its ability to make the most of parallel developments such as 5G connectivity.

India’s Macroeconomic Landscape of AI

India is already way ahead of many other countries in implementing artificial intelligence (AI). More than 40% of the enterprises are going beyond pilot and test projects and adopting the technology at a larger scale coupled with 1400+ global capability centers that have become frontiers in pushing AI led innovation and transformation for their parent organizations. The Indian government’s Digital India initiative, too, has created a favorable regulatory environment for increased use of AI.

Recipe for AI Success in India – Digital Deluge & Data Detonation

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

Intel is betting on Artificial Intelligence (AI) to drive demand for its electronic chips, for which it is aiming to train 15,000 scientists, developers, engineers and students on AI in India over the next one year. The company will host 60 courses under its ‘AI Developer Education Program’. These will train people on ways they can adopt AI for better research, testing or even building of products. Intel is looking at India due to the country’s large base of technical talent. The country is the third largest global site for AI companies. As India’s largest e-commerce marketplace Flip kart is looking to put in use its mammoth pile of data to predict sales of products months in advance. The company is working on an artificial intelligence (AI) solution that will give it an edge over rivals by helping it make smarter decisions in ordering, distribution and pricing products on its platform. Ultimately, the AI system will allow Flip kart to boost efficiency and reduce the cost of products for customers. While rival Amazon, which has around a 10-year head start over Flip kart, is known to have some of the most advanced sales prediction engines, the Indian company has the advantage of having a bigger data set of the country’s online consumer market.

AI Inroads in the Private Sector

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

The AI Startups Sphere of India- Startups are clearly playing a major role in innovating faster than enterprises, which has led to several partnerships. SAP India has invested in Niki.ai, a bot that improves the ordering experience. Then there’s Ractrack.AI, where a bot improves customer engagement and provides insights; it functions as a virtual communications assistant to convert the customer into a client. Racetrack is helping companies turn leads into meaningful engagements by using AI. Another startup, LUCEP, converts all potential queries into leads with their AI engine. The objective is to generate insights from data and simplify customer interaction with a business and also convert them into leads. Indian startups saw $ 10 billion in risk capital being deployed across 1,540 angel and VC/PE deals between January and December 2019. VC/PE firms predict that AI would be key themes to invest in for next few years.

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

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

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

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

Indian Governance Initiatives – Huge Scope for Investment of AI – As India emerges as a premier destination for AI, scope for investment opens in the governance aspect, in several ways. Governance schemes have a unique trait of the baggage of large volume and large scale implementation need, which can be tackled with Deep learning. For example, in Swachh Bharat Initiative, specifically construction of toilets in rural India, public servants are tasked with uploading images of these toilet constructions to a central server for assessment. Image recognition can be used to target unfinished toilets. It can also be used to identify whether the same official appears in multiple images or if photos were uploaded from a different location other than the intended place. Other initiatives such as the Make in India, Digital India & Skill India can be augmented with AI to deal with scale. The range of application for AI techniques could range from crop insurance schemes, tax fraud detection, and detecting subsidy leakage and defense and security strategy.

An AI system can improve and enrich the agriculture of India by enhancing the bodies like The Department of Agriculture Cooperation and Farmers Welfare, Ministry of Agriculture runs the Kisan Call Centers across the country etc. It can help assist the call center by linking various available information like soil reports from government agencies and link them to the environmental conditions. It will then provide advice on the optimal crop that can be sown in that land pocket. As the need for large scale implementation and monitoring of governance initiative becomes more pronounced, the need for AI becomes absolute and it will open doors to considerable AI investment in the future of India.

Finally, Looking Ahead – A Collaborative Innovation led ecosystem

AI innovations which fall under assisted, augmented and autonomous intelligence will help users understand and decide which level of intelligence is helpful and required in their context, thereby making AI Acceptance easier for the people. At the same time, this AI continuum can be used to understand economic ramifications, usage complexity and decision-making implications. While academia and the private sector conduct research on various AI problems with diverse implications in mind, the public sector with its various digital initiatives (Digital India, Make in India, etc.) can identify areas where parts of the AI continuum can be utilized to increase reach, effectiveness and efficiency, thereby giving direction to AI Innovative Research. A collaborative innovation environment between academia and the private and public sectors will help provide holistic and proactive advisory delivery to the population, for example through public call centers, linking information from various government sources. At the same time, the rich data generated from these interactions can be used to draw deep conclusions. Collaboration between the three pillars could further help get a comprehensive view of problems and find intelligent and innovative ways to increase the efficiency and effectiveness of services delivered to society. India is at a cusp of taking a upward trajectory on establishing AI supremacy ; a strategic roadmap across public, private , SMB’s , Academic and startup sectors will accelerate the path to AI adoption and unleashing new sources of economic output for the country . The journey to attain AI supremacy has begun ……

Piloting thru the COVID era means grasping sudden strategic shifts. Social distancing and process turmoil have taken hold in every aspect of our lives, forcing us to change our routines and reevaluate our goals and expectations in real-time. And, unsurprisingly, these waves of upheaval have triggered a reset on most business operations and on commercial assumptions. We are all resetting the playbook in the new normal.

We are faced with an unprecedented challenge during this disruption. For years leaders have been told that if they are unable to lead ourselves, they cannot possibly lead others. But what does that look like right now? It is a struggle to process this globally shared experience that is simultaneously isolating and deeply personal. In a time when we need to be connected and certain, we may feel disconnected and unsure. On top of that, theory and practice are in constant collision. Navigating the unknowable and uncontrollable future of our businesses is no longer some academic or design-thinking challenge, it is reality. The future is no longer distant, it is right here right now. Hemingway captured this contradiction brilliantly in The Sun Also Rises: When a character is asked, “How did you go bankrupt?” the response is, Gradually and then suddenly.” 

Seeking command and control during this time of next-level upheaval simply will not work. Instead, we must cultivate ingenious mindset. Ingenious leaders are operators and innovators at the same time, and they encourage their followers to be the same. This style of leadership does not come naturally to everyone, but learning to master it is crucial to growth, and it must be prioritized and reactivated in the COVID era. Many corporate leaders are expert operators who drive efficiency and optimization, but not high on innovation and growth. Becoming an ingenious leader requires nurturing new ideas that address consumer pain points using the mindset and tools of a creator. Especially now, in this frightening and fluid new world, business leaders must learn to operate and create. So what is the first step toward being an ingenious leader? How do we elevate ourselves and our teams in this moment? 

Reset

Reset is not surrendering. Reset means embracing The New Normal as our shared reality, not a temporary dilemma to be endured, eventually returning to past goals and expectations. Reset is a crucial element of thoughtful agility. It empowers a team to set down the past, and move forward, unrestrained, into the future. It draws a circle around the knowable that helps us to identify the unknowable, giving us clarity on what we can control and what is beyond our control. Without that clarity, we are unable to set a path into the future. With that clarity, business leaders can make decisions based on new commercial truths, react and pivot quickly, reactivate and discover new growth, and lead their teams with conviction. They can become truly ingenious. Pressing the Reset button also requires us to acknowledge that past precedent is no longer a future predictor of business behaviors and outcomes. Leaders must have flexible mindsets around beliefs about market and consumer signals and shifts. It’s time to let go of past commitments and re-align our enterprises to a new-base business reality. 

Realign

Once we have activated the Reset button, business leaders must prepare to realign and embrace the New Normal . This is where we practice ingenious leadership by shifting focus away from the knowable and controllable and toward the unknowable and uncontrollable. This is where we embrace discomfort and dig into the challenge of transforming our enterprises in two key areas: growth and capability.

Resurrect

To resurrect growth, we will need to innovate into radically changed markets to address new, often sudden strategic shifts in customer needs and problems. We are living in a fragile moment of intense change that requires us to lean into these rare opportunities to discover The New Future. Enterprises must abandon the familiar model of Total Addressable Markets (TAM), where past budgets and behaviors existed pre-disruption. We must shift now to a Total Addressable Problem (TAP) view of the world, where new needs, behaviors, and budgets guide our choices. Leaders should be experimenting and testing ferociously to understand the new TAP view of the world and dig into the problems that people are facing right now. Instead of thinking about the past, leaders must focus on present problems their enterprises are uniquely positioned to solve. 

Run better , Run different

To transform enterprises’ ability to resurrect growth, we will need to adopt and embed new strategies that transform teams and organizations with adaptability and speed to run better & run different. Leaders must cultivate resilience of growth in both capability and culture. Tackling all of this during the COVID era takes empathy and courage. Fortunately, we can see courage, innovation, and exploration all around us. Organizations and individuals are creating ingenious solutions to problems we never knew we would face. Futurologist, Yuval Noah Harari pointed out that we’re currently witnessing “… social experiments on a massive scale that will change the world. We can’t predict what will happen because the main thing is that we have so many choices. It’s not like there is just one predetermined outcome to this epidemic. “The choice we face means dual objective of augmenting the growth post recovery and finding new spots of opportunities. This gives us the opportunity to create growth mindsets, systems, and cultures that transcend traditional strategic planning and efficiency. We can embrace resilience and the capabilities to lead through uncertainty, and rise to meet the New Normal in the leadership code

In midst of this COVID situation across the globe, exponential technologies and an AI led processes around the value chain of supply chain will unleash new scenarios for global enterprises. Robots already stand side-by-side with their human counterparts on several manufacturing floors, adding efficiency, capacity (robots don’t need to sleep!) and dependability. Add in drones and self-driving vehicles ; all these technological advancements are compelling enterprises to reimagine their supply chain.

Supply chains, although automated to a degree, still face challenges brought about by the amount of slow, manual tasks required, and the daily management of a complex web of interdependent parts. The next generation of process efficiency gains and visibility could be on your doorstep with artificial intelligence in supply chain management, if only you’d let the robots automatically open it for you.

 Intelligent Automation: Intelligent automation of the end-to-end supply chain, enabling the management of all tasks and sections in tandem allows you to spend less time on low value, high frequency activities like managing day-to-day processes, and provides more time to work on high value, exception-based requirements, which ultimately drives value for the entire business.

Analysts estimates businesses could automate up to 45% of current work, saving $2 trillion in annual wages. “In addition to the cost and efficiency advantages, Intelligent automation can take a business to the next level of productivity optimization,” . Those ‘lights out’ factories and warehouses are becoming closer to a reality.

Four key elements need to be in place for you to take full advantage of intelligent automation in your supply chain:

• robots for picking orders and moving them through the facility;
• sensors to ensure product quality and stock;
• cognitive learning systems;
• and, artificial intelligence to turn processes into algorithms to guide the entire operation.

In addition, you’ll need strong collaboration internally and among suppliers and customers to tie all management systems back to order management and enterprise resource planning platforms.

Artificial Intelligence In Supply Chain : Strategic coverage areas

AI is changing the traditional way in which companies are operating. Siemens in its “lights out” manufacturing plant, has automated some of its production lines to a point where they are run unsupervised for several weeks.

Siemens is also taking a step towards a larger goal of creating Industrie 4.0 or a fully self-organizing factory which will automate the entire supply chain. Here, the demand and order information would automatically get converted into work orders and be incorporated into the production process. This would streamline manufacturing of highly customized products.

Artificial Intelligence In Supplier Management And Customer Service: Organizations are also increasingly leveraging AI for supplier management and customer management. IPsoft’s AI platform, Amelia automates work knowledge and is able to speak to the customers in more than 20 languages. A global oil and gas company has trained Amelia to help provide prompt and more efficient ways of answering invoicing queries from its suppliers. A large US-based media services organization taught Amelia how to support first line agents in order to raise the bar for customer service.

Artificial Intelligence In Logistics & Warehousing : Logistics function will undergo a fundamental change as artificial intelligence gets deployed to handle domestic and international movement of goods. DHL has stated that its use of autonomous fork lifts is “reaching a level of maturity” in warehouse operations. The next step would be driver less autonomous vehicles undertaking goods delivery operations.

Artificial Intelligence In Procurement :AI is helping drive cost reduction and compliance agenda through procurement by generating real time visibility of the spend data. The spend data is automatically classified by AI software and is checked for compliance and any exceptions in real time. Singapore government is carrying out trials of using artificial intelligence to identify and prevent cases of procurement fraud. The AI algorithm analyzes HR and finance data, procurement requests, tender approvals, workflows, non-financial data like government employee’s family details and vendor employee to identify potentially corrupt or negligent practices. AI will also take up basic procurement activities in the near future thereby helping improve the procurement productivity.

Artificial Intelligence in new product development :AI has totally overhauled the new product development process.by reducing the time to market for new products. Instead of developing physical prototypes and testing the same, innovators are now creating 3D digital models of the product. AI facilitates interaction of the product developers in the digital space by recognizing the gestures and position of hand. For example, the act of switching on a button of a digital prototype can be accomplished by a gesture.

AI In Demand Planning And Forecasting: Getting the demand planning right is a pain point for many companies. A leading health food company leveraged analytics with machine learning capabilities to analyze their demand variations and trends during promotions. The outcome of this exercise was a reliable, detailed model highlighting expected results of the trade promotion for the sales and marketing department. Gains included a rapid 20 percent reduction in forecast error and a 30 percent reduction in lost sales.

AI in Smart Logistics :The impact of data-driven and autonomous supply chains provides an opportunity for previously unimaginable levels of optimization in manufacturing, logistics, warehousing and last mile delivery that could become a reality in less than half a decade despite high set-up costs deterring early adoption in logistics. Changing consumer behavior and the desire for personalization are behind two other top trends Batch Size One and On-demand Delivery: Set to have a big impact on logistics, on-demand delivery will enable consumers to have their purchases delivered where and when they need them by using flexible courier services.

A study by MHI and Deloitte found more than half (51%) of supply chain and logistics professionals believe robotics and automation will provide a competitive advantage. That’s up from 39% last year. While only 35% of the respondents said they’ve already adopted robotics, 74% plan to do so within the next 10 years. And that’s likely in part to keep up with key players like Amazon, who have been leading the robotics charge for the past few years.

Execution led scenario : These examples showcase that in today’s uncertain times, AI embedded supply chains offer a competitive advantage. AI armed with intelligence can analyze massive amounts of data generated by the supply chains and help organizations move to a more proactive form of supply chain management. Thus, in this AI first theme, where the mantra is “evolve or be disrupted”, companies are leveraging AI to reinvent themselves and scale their businesses quickly. AI is becoming a key enabler of the changes that businesses need to make and is helping them manage complexity of business posed by this pandemic.

For Sure, 2020 will test not only the leadership acumen of CXOs, but also the ability of enterprises to operate in the face of extreme ambiguity. Starting as a localized issue, the corona virus (COVID-19) has now reached almost all nations, impacting enterprises across the globe –with dire consequences.

 Already, thousands of people have died, hundreds of thousands have become ill and health services have been stressed way beyond their capacity. For most, the pandemic – and response to it – will be the most significant, and most concerning, event they have experienced. It has cost enterprises billions of dollars in lost revenue (potentially up to $2.1 trillion by the end of 2020). It is clear it will result in a significant drop in economic growth around the world. At this stage of the COVID-19 outbreak, enterprises need to deal with two equally important factors – keeping employees and their families safe, and ensuring business continuity as much as possible. Leaders are scrambling to secure supplies, keep fearful employees motivated to work, and planning for the future while dealing with the here and now. But eventually, like other Black Swan events, the virus will end. And when it does, enterprises need to be ready.

In today’s turbulent world, some  have become better at planning for and mitigating against risk in the face of a crisis. They have resilience built into their structure. But for many others, this could be a time of confusion, fear, and rash decision-making. Unfortunately, in our increasingly ambiguous, volatile and inter-connected world, unanticipated events like this are likely to happen more frequently – and leaders will need to be more agile, transparent, and forward thinking. A new set of attributes will be key to navigating 2020, which is likely to be having tow strategic viewpoints . The first viewpoint will be spent dealing with safety, containment, continuity, and contingency planning – a time for prudent, agile leadership and the second viewpoint will be centered around taking advantage of the pent-up demand in the global economy through transformation and innovation.

 For enterprises to respond now and plan for recovery, they must learn to operate in a state of constant disruption. In a time of unknowns, one thing is certain: what has worked in the past is unlikely to keep working in the future. New habits are forming quickly – people are working from home and consuming products and entertainment in very different ways out of necessity. Building a culture that not only tolerates this shift but thrives in it will separate the winners from the losers.

 This may mean thinking differently about performance and target setting, to keep teams motivated and ensure everyone works collectively for a shared purpose – even when working remotely. It will certainly demand a proactive and empathetic communication response from leaders, who will need to consciously demonstrate the values and behaviors they wish to encourage across the organization. But it should not necessarily mean putting recruitment and retention plans on hold. In challenging times, the quality of your talent can be the ultimate advantage. Retaining your top people has never been more important, and future talent acquisition strategy will be done through the lens of recovering and resetting after the crisis. Ultimately, leaders will need to adapt quickly to changing circumstances – shifting from a measured, inclusive approach today to setting the pace post recovery and making up for lost time.

 In times of crisis, people depend on leaders to provide clarity and hope. Fear can be contagious, breeding irrational behavior and anxiety – and in business, this can lead to lower productivity and employee engagement. While no one can be certain how the impact of this virus will continue to unfold globally, one thing is known: we will experience another business crisis again in the future. Leaders who can use this disruptive period as a time for self-reflection and an opportunity to re-frame their mindset are likely to be better prepared when the next crisis comes along.

 This is the time for agile leaders who can anticipate change – such as the necessity of working remotely – and turn it into a positive new way of working. They can also drive a sense of collective purpose and optimism, accelerate innovation and test new ideas, partner with others, and build trust. So how can you keep responding to such volatile market demands, find new ways to create and act on opportunities, and keep your teams aligned to a common purpose? Now is the time to be in thinking different and be in action mode , a global consumer product brand has ramped up its digital outreach while foot traffic to physical stores remains low. By doubling their digital efforts, they are taking this opportunity to get closer to the customer and build a strong sense of community around their product, which in turn anticipates a significant shift in the way their products will go to market in the future.

 Adaptive leaders can anticipate opportunities like this, while also using strong communication to build trust and engagement within their teams. This will set their enterprises up to thrive through recovery. Best of the leaders are known to use down cycles as opportunities to grow. The following are the five strategic interventions , leaders need to follow in this phase:

 Uncertainty demands over-communication: People need reassurance that there is a plan and a path forward,  If town halls and coffee chats are impossible while teams work remotely, build communication channels via WhatsApp groups or run video seminars. CEOs can share daily 90-second video updates to keep everyone aligned and build a sense of community around new tactics and plans. This gives everyone a common language to take to clients and partners. It’s even more important to stay connected with your team at this time, and create routine ways for people to work together so they feel like they’re fully supported as part of a team.

 Be realistic and build no exaggeration: Leaders are now living with uncertainty and ambiguity, and it’s acceptable to say you don’t know all the answers. Listen to employee concerns, and acknowledge there are sometimes no easy solutions. If you don’t have the answer, bring your team together to discuss and experiment with solutions – focus on testing new things quickly. Being transparent and open in this way may feel uncomfortable, but it can go a long way to building credibility and trust – with staff, customers, shareholders, and the wider community.  

Plan swiftly and make bold decisions: Some leaders will need to make difficult decisions in the interest of long-term business continuity – such as reducing labor costs through staff lay-offs or forced leave. Being really clear and upfront about your plan, or it could be toxic to morale. If you know there will be headcount reduction, or you need to close down a loss-making project or pull back from a market, be compassionate and clear – don’t mislead or give mixed messages, And if you have to do this, do it once and then move forward.

Engage more with your high performing teams: Leaders may need to prioritize where their energy goes – and your best talent and clients should top the list. For example, when Chinese firm provided face masks, which were already scarce, to clients very early in the outbreak, it sent a strong signal that it wanted to keep them safe. Similarly, it’s a common mistake to neglect development of high performers during economic tension – especially when you are relying on them more than ever. When the market recovers, they are likely to jump to new opportunities first. Give them the recognition they need to feel valued right now, in addition to opportunities for personal and professional development. This is one of the highest drivers of employee engagement.

Build a strong emotional intellect: Although it seems the weight of the world is on your shoulders, you still need to take time for yourself  and spend time with family. Only then can you be available for your team – because working intensely under pressure for months on end is not sustainable. This includes taking time to build emotional intelligence. The four domains of Emotional Intelligence (EI) — self-awareness, self-management, social awareness, and relationship management —can help a leader face any crisis with lower levels of stress, less emotional reactivity and fewer unintended consequences, One impact of the virus is likely to be permanent change to the way organizations work. This is your opportunity to learn how to work in a more agile way, including virtual working and rapid prototyping.

 This is a critical moment to develop the leadership capabilities you will need for a very different future. Are you ready for the challenge?

In the time of uncertainty and disruption….Soon, organizations will increasingly be competing on the AI prowess and their supremacy. AI promises to play a critical role ; artificial intelligence can detect patterns in complex data sets at extreme speed and scale, enabling dynamic learning. This will allow organizations to constantly adapt to changing realities and surface new opportunities, which will be increasingly important in an uncertain and fast-changing environment.

But for companies to compete on AI, it is not enough to merely adopt AI, which alone can accelerate learning only in individual activities. As with previous transformative technologies, unlocking the full potential of AI and future of workforce will require fundamental organizational innovation , transformation and disruption. Leaders will need to re-invent the enterprise as an AI driven organization :

• Velocity & Scale : The growing opportunity and need to perform at high velocity bringing scale driven by AI is well known—algorithmic trading, dynamic pricing, real-time customized product recommendations are already a reality in many businesses. But it is perhaps under-appreciated that slow moving forces are also becoming important. For example, trade institutions, political structures and social attitudes are slowly changing in ways that could have a profound impact on business. Gone are the days when business leaders could focus only on business and treat these broader variables as constants or stable trends. But such shifts unfold over many years or even decades. In order to thrive sustainably, businesses must learn at high velocity .
• Rebalancing Humans and Machines equation : Machines have been crucial components of businesses for centuries—but in the AI age, they will likely expand rapidly into what has traditionally been considered white-collar work. Instead of merely executing human-directed and designed processes, machines will be able to learn and adapt, and will therefore have a greatly expanded role in future organizations. Humans will still be indispensable, but their duties will be quite different when complemented or substituted by intelligent machines.
• Integrating External ecosystems with corporate strategies : Businesses are increasingly acting in multi company ecosystems that incorporate a wide variety of players. Indeed, seven of the world’s largest companies, and many of the most profitable ones, are now platform businesses. Ecosystems greatly expand learning potential: they provide access to exponentially more data, they enable rapid experimentation, and they connect with larger networks of suppliers of customers. Harnessing this potential requires redrawing the boundaries of the enterprise and effectively influencing economic activity beyond the orchestrating company.
• Evolving the Organization : The need for dynamic learning does not apply just to customer-facing functions—it also extends to the inner workings of the enterprise. To take advantage of new information and to compete in dynamic, uncertain environments, the organizational context itself needs to be evolve in the face of changing external conditions.

 Today’s organizations, which were designed for more stable business environments, are not well-suited to perform these functions. Reinventing the organization for the next decade will require embracing four imperatives:

1. Integrate AI into the core operating model for survival
2. Migrate human cognition to mature work spheres
3. Re balance the relationship between machines and humans.
4. New age leadership & management approaches

1.Integrate AI into the core operating model for survival : As powerful as today’s level of AI is , it will yield only incremental gains if it simply enhance individual steps of existing processes. The effective rate of an organization’s learning is gated by its ability to act on new insights. And classical organizations act slowly, owing to their reliance on human decision making and hierarchy. Organizations will need not only to automate but also to “embed AI in to the operating model” of significant parts of their businesses.

In order to truly accelerate the speed of learning to algorithmic timescales, organizations will need not only to automate but also to “embed AI ” into significant parts of their businesses. In traditional automation, machines execute a pre-designed process repeatedly and consistently. In AI led transformation, machines use continuous feedback to act, learn, and adapt on their own—without the bottleneck of human intervention.

AI driven systems are designed by combining multiple algorithms into integrated learning loops. Data from digital platforms automatically flows into AI algorithms, which mine the information in real time to facilitate new insights and decisions. These are wired directly into action systems, which continuously optimize outcomes under changing conditions. These actions produce yet more data that can be fed back through the cycle, closing the loop and allowing the organization to learn at the speed of algorithms.

In contrast, traditional organizational approaches—for example, unchanging rules or hierarchical decision processes—can impede companies’ ability to harness the rapid learning potential unlocked by AI ; Actions that companies can take to harness AI include :

• Gather real-time data on all aspects of the business by leveraging algorithms
• Deploy AI at scale, integrated with data and decision-making systems.
• Take human hierarchy “out of the loop” of routine, data-based decision making.

2. Migrate Human Cognition to Mature Work Spheres :The widespread adoption of AI naturally raises the question of what role human workers will play in the organization of the future. Today, there is already widespread concern about the speed at which AI will disrupt the future of work. To shape this future—and to maximize organizational learning capabilities—businesses need to focus human cognition on its unique strengths. Humans should increasingly focus their efforts on these higher-level activities. For example, while correlative analysis is generally sufficient for learning about repeated actions on fast timescales, it is less useful for learning about slow-moving forces, such as political, social, and economic trends. These shifts are unique and depend on the historical context and trajectory, which means there is no repeated data set in which to find patterns. Human abilities, such as understanding causal relationships and generalizing from limited data, are necessary to decode these forces and adapt the organization accordingly.

Counterfactual thinking is also critical, as businesses need increasingly to compete on Imagination. Existing business models are being exhausted faster, and long-term growth is declining, which means companies must continually generate new ideas to grow sustainable. But businesses today, which are often implicitly designed for efficiency and the maximization of short run financial outcomes, are not conducive to imagination. Organizations will need to better facilitate individual and collective imagination.

In addition to imagination and making sense of non-repeated events, there will be many other activities where humans are advantaged, including organizational design, algorithmic governance, ethics, and purpose, to name a few. In these domains of human activity, organizations will need to become more effective at dynamic collaboration to get the most out of their teams. This requires emphasizing self-organization and experimentation by creating an organizational context in which responsive decision making and learning can thrive, rather than by relying on direct instructions.

3. Rebalance the Relationship Between Humans and Machines : The first two imperatives call for a hybrid organization, one that combines the comparative advantages of machines and humans: machines’ ability to rapidly identify complex patterns in big data and humans’ ability to decode complex causal relationships and imagine new possibilities. Together, these will enable the organization to learn on an expanded range of timescales—faster and slower.

But in hybrid organizations, humans and machines will increasingly have to collaborate in new and more effective ways. This includes tasks that require thinking on multiple levels or timescales simultaneously, as well as tasks that demand social interaction, another dimension in which humans are currently far more effective. Organizations will thus need to reimagine the relationship between humans and machines to bring the best out of both and maximize synergies.

 Today’s AI models tend to be “black boxes” that are not designed to be interoperable and may therefore impede trust. For these new types of human-machine relationships to succeed, organizations need to develop effective human-machine interfaces that allow for seamless collaboration. Organizations will need to overcome these hurdles by developing and implementing interfaces that provide transparency into how AI makes recommendations, allowing humans to understand and validate machines’ actions. Similarly, humans and algorithms are rarely matched for bandwidth and complexity. Choosing the right level of abstraction and compression for communication between humans and computers is critical: too much compression will suppress subtlety and prevent the tinkering through which human innovation proceeds, while too little will overwhelm human overseers.

 4. New Age Leadership & Management Approaches :Collectively, the above imperatives point to a very different way of designing and operating organizations with AI —which in turn will significantly change the role of leadership. In particular, leaders will need to focus on several new challenges.Developing governance principles for AI and autonomous machines. : As machines play a greater part in learning and action, the role of leadership in setting guardrails and priorities will take on greater importance. In the last decade, tech companies could sidestep these topics, as the promise and potential of new technologies gave them a license to move fast. But as social scrutiny of technology increases, questions about governance, trust, and ethics are coming to the forefront. And as AI is adopted more widely, all businesses will have to deal with these difficult questions.

 The organizations that will survive and become pioneer will look much different from today’s: they will use different AI driven capabilities; they will operate at different speeds and scales of influence; they will contain different structures and responsibilities; and they will embody different leadership models to enable all of the above. AI will become a force multiplier and will define the DNA of tomorrow’s organization.At the end of the day, its a matter of survival….

Global Capability Centers(GCC’s) are at an inflection point as the pace at which AI is changing every aspect is exponential and at high velocity. The rapid transformation and innovation of GCC’s today is driven largely by ability for them to position AI strategic imperative for their parent organizations. AI is seen to the Trojan horse to catapult GCC’s to the next level on innovation & transformation. 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 AI led strategies to position themselves up as the model template of AI center of Excellence . It is widely predicted that AI will disrupt and transform capability centers in the coming decades. How are Global Capability Centers in India looking at positioning themselves as model template for developing AI center of competence? How have the strategies of GCCs transformed with reference to parent organization? whilst delivering tangible business outcomes , innovation & transformation for parent organizations?

Strategic imperatives for GCC’s to consider to move incrementally in the value chain & become premier AI center of excellence

AI transformation

Artificial Intelligence has become the main focus areas for GCCs in India. The increasing digital penetration in GCCs across  business verticals has made it imperative to focus on AI. Hence, GCCs are upping their innovation agenda by building bespoke AI CoEs. Accelerated AI adoption has transcended industry verticals, with organizations exploring different use cases and application areas. GCCs in India are strategically leveraging one of the following approaches to drive the AI penetration ahead –

• Federated Approach: Different teams within GCCs drive AI initiatives
• Centralized Approach: Focus is to build a central team with top talent and niche skills that would cater to the parent organization requirements
• Partner ecosystem : Paves a new channel for GCCs by partnering with research institutes , start-ups , accelerators
• Hybrid Approach: A mix of any two or more above mentioned approaches, and can be leveraged according to GCC’s needs and constraints.

Ecosystem creation : Startups /research institutes/Accelerators

One of the crucial ways that GCCs can boost their innovation agenda is by collaborating with start-ups, research institutes , accelerators. Hence, GCCs are employing a variety of strategies to build the ecosystem. These collaborations are a combination of build, buy, and partner models:

• Platform Evangelization: GCCs offer access to their AI platforms to start-ups
• License or Vendor Agreement: GCCs and start-ups enter into a license agreement to create solutions
• Co-innovate: Start-ups and GCCs collaborate to co-create new solutions & capabilities
• Acqui-hire: GCCs acquire start-ups for the talent & capability
• Research centers : GCCs collaborate with academic institutes for joint IP creation , open research , customized programs
• Joint Accelerator program : GCCs & Accelerators build joint program for customized startups cohort

To drive these ecosystem creation models, GCCs can leverage different approaches. Further, successful collaboration programs have a high degree of customization, with clearly defined objectives and talent allocation to drive tangible and impact driven business outcomes.

AI Center of Competence/ Capability

GCCs are increasingly shifting to competency , capability creation models to reduce time-to-market. In this model, the AI Center of Competence teams are aligned to capability lines of businesses where AI center of competence are responsible for creating AI capabilities , roadmaps and new value offerings, in collaboration with parent organization’s business teams. This alignment and specific roles have clear visibility of the business user requirement. Further, capability creation combined with parent organization’s alignment helps in tangible value outcomes. In several cases, AI teams are building new range of innovation around AI based capabilities and solutions to showcase ensuing GCC as model template for innovation & transformation . GCCs need to conceptualize a bespoke strategy for building and sustaining AI Center of Competence and keep it up on the value chain with mature and measured transformation & innovation led matrices.

Talent Mapping Strategy

With the evolution of analytics ,data sciences to AI , the lines between different skills are blurring. GCCs are witnessing a convergence of skills required across verticals. The strategic shift of GCCs towards AI center of capability model has led to the creation of AI , data engineering & design roles. To build skills in AI & data engineering, GCCs are adopting a hybrid approach. The skill development roadmap for AI is a combination of build and buy strategies. The decision to acquire talent from the ecosystem or internally build capabilities is a function of three parameters –Maturity of GCC ’s existing AI capabilities in the desired or adjacent areas ,Tactical nature of skill requirement & Availability and accessibility of talent in the ecosystem. There’s always a heavy Inclination towards building skills in-house within GCCs and a majority of GCCs have stressed upon that the bulk of the future deployment in AI areas will be through in-house skill-building and reskilling initiatives. However, talent mapping strategy for building AI capability is a measured approach else can result in being a Achilles heel for GCC and HR leaders.  

GCCs in India are uniquely positioned to drive the next wave of growth with building high impact AI center of competence , there are slew of innovative & transformative models that they are working upon to up the ante and trigger new customer experience , products & services and unleash business transformation for the parent organizations. This will not only set the existing GCCs on the path to cutting-edge innovation but also pave the way for other global organizations contemplating global center setup in India.AI is becoming front runner to drive innovation & transformation for GCCs.