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 ……